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The crack fault is one of the most common faults in the rotor system, and researchers have paid close attention to its fault diagnosis. However, most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals. In this paper, a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function (RBF) network and Pattern recognition neural network (PRNN) is presented. Firstly, a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method, where the crack’s periodic opening and closing pattern and different degrees of crack depth are considered. Then, the dynamic response is obtained by the harmonic balance method. By adjusting the crack parameters, the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots. The analysis results show that the first critical speed, first subcritical speed, first critical speed amplitude, and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis. Based on this, the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input. Test results show that the proposed method has high fault diagnosis accuracy. This research proposes a crack detection method adequate for the hollow shaft rotor system, where the crack depth and position are both unknown.

Identifying the key risk factors of disease from a large amount of clinical data is a prerequisite for further scientific decision-making. In medical practice, the clinical symptom information of patients usually includes various types of data. Meanwhile, the occurrence and development of diseases are joint result of the mutual influence factors. Therefore, there is usually a correlation between attributes. In this paper, we discuss a kind of hybrid attribute feature selection problem considering the correlation between attributes. Firstly, we take the identification of disease pathogenic factors in medical decision as the background, and construct a hybrid attribute decision system. Secondly, by introducing kernel alignment, the uncertain relationship between attributes is defined. Based on this, a three-way clustering model in attribute space is established. Furthermore, a feature selection method for hybrid attribute data based on three-way clustering in attribute space is proposed. Finally, we applied the proposed model to identify the pathogenic factors of stroke and used 279 clinical random samples for simulation analysis. The results verified the applicability and validity of the model. The main contributions of this paper include two aspects. In terms of theory, by introducing kernel alignment, a three-way clustering algorithm in attribute space is established. Meanwhile, a hybrid attribute feature selection method based on three-way clustering is proposed. In terms of application, the proposed method is applied to identify risk factors of stroke.

In the milling process of metallic parts, appropriate tool conditions are essential to reducing processing faults and ensuring manufacturing quality. However, the existing condition monitoring methods are usually limited by recognizing intermediate abnormal states during milling processing, which is inefficient and impractical for real practical applications. Therefore, this paper proposes a tool condition monitoring (TCM) method in the milling process based on multisource pattern recognition and state transfer paths. First, the improved K-means clustering method is used to generate multiple patterns of tool wear. Second, a multisource pattern recognition model framework is developed, and multiple observation windows and the pattern transfer path are considered in the multisource pattern recognition model. Finally, PHM2010 datasets are used to verify the feasibility of the proposed method, and the results demonstrate the applicability of the proposed method in practice for tool condition monitoring.

The grading of gemstones is currently a manual procedure performed by gemologists. A popular approach uses reference stones, where those are visually inspected by specialists that decide which one of the available reference stone is the most similar to the inspected stone. This procedure is very subjective as different specialists may end up with different grading choices. This work proposes a complete framework that entails the image acquisition and goes up to the final stone categorization. The proposal is able to automate the entire process apart from including the stone in the created chamber for the image acquisition. It discards the subjective decisions made by specialists. This is the first work to propose a machine learning approach coupled with image processing techniques for emerald grading. The proposed framework achieves 98% of accuracy (correctly categorized stones), outperforming a deep learning approach. Furthermore, we also create and publish the used dataset that contains 192 images of emerald stones along with their extracted and pre-processed features.

How to represent and classify a testing sample for the representation-based classification (RBC) plays an important role in the filed of pattern recognition. As a typical kind of the representation-based classification with promising performance, collaborative representation-based classification (CRC) adopts all the training samples to collaboratively represent and then classify each testing sample with the reconstructive residuals among all the classes. However, most of the CRC methods fail to make full use of the localities and discrimination information of data in collaborative representation. To address this issue to further improve the classification performance, we design a novel supervised CRC method entitled locality-constrained weighted collaborative-competitive representation-based classification (LWCCRC). In the proposed method, the localities of data are taken into account by using the positive and negative nearest samples of each testing sample with their corresponding weighted constraints. Such devised locality-constrained weighted term can model the similarity and natural discrimination information contained in the neighborhood region for each testing sample to obtain the favorable representation. Moreover, a competitive constraint is introduced to enhance pattern discrimination among the categorical collaborative representations. To explore the effectiveness of our proposed LWCCRC, the extensive experiments are carried out on three different types of data sets. The experimental results demonstrate that the proposed LWCCRC significantly outperforms the recent state-of-the-art CRC methods.

Fine-grained classification is a challenging problem with small inter-class variance and large intra-class variance. It becomes more difficult when only a few labeled training samples are available. Inspired by the procedure of human recognition that two similar objects are usually distinguished by comparing their key parts, we develop a novel few-shot fine-grained classification method, which learns to model the inter-class boundaries in human-like style, i.e., extracting key-part structure information of objects and performing part-by-part comparison. To this end, we first extract the key parts of objects by using the designed key-part detector, which are then encoded by our structure encoder for the final comparison. To tackle with the scarce labeled samples, we train the proposed network under the metric-based few-shot learning methodology. Experiments on benchmark datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art counterparts. Besides, extensive investigations are conducted to verify the contributions of the key components of our method.

Deep convolutional neural networks (DCNNs) have dominated as the best performers on almost all computer vision tasks over the past several years. However, it remains a major challenge to deploy these powerful DCNNs in resource-limited environments, such as embedded devices and smartphones. To this end, 1-bit CNNs have emerged as a feasible solution as they are much more resource-efficient. Unfortunately, they often suffer from a significant performance drop compared to their full-precision counterparts. In this paper, we propose a novel Bayesian Optimized compact 1-bit CNNs (BONNs) model, which has the advantage of Bayesian learning, to improve the performance of 1-bit CNNs significantly. BONNs incorporate the prior distributions of full-precision kernels, features, and filters into a Bayesian framework to construct 1-bit CNNs in a comprehensive end-to-end manner. The proposed Bayesian learning algorithms are well-founded and used to optimize the network simultaneously in different kernels, features, and filters, which largely improves the compactness and capacity of 1-bit CNNs. We further introduce a new Bayesian learning-based pruning method for 1-bit CNNs, which significantly increases the model efficiency with very competitive performance. This enables our method to be used in a variety of practical scenarios. Extensive experiments on the ImageNet, CIFAR, and LFW datasets show that BONNs achieve the best in classification performance compared to a variety of state-of-the-art 1-bit CNN models. In particular, BONN achieves a strong generalization performance on the object detection task.

The one-versus-one (OVO) binarization decomposition scheme is considered as one of the most effective techniques to deal with multi-class classification problems. Its inherent mechanism is to use the “divide-and-conquer” strategy to decompose the multi-class classification problem into as many pairs of easier-to-solve binary sub-problems as possible. One common issue in the OVO scheme is that of non-competent classifiers. In this study, we proposed a novel OVO scheme strategy, named DRCW-FRkNN-OVO, to reduce the negative effect of non-competent classifiers. Specifically, we focused on the definition of region of competence, which plays a crucial role in managing the non-competent classifiers. To overcome the issue of skew and sparse distribution during the management of non-competent classifiers, we developed a relative competence weighting combination method via the fixed radius nearest neighbour search to find the local region within each class for the query sample. Our proposed DRCW-FRkNN-OVO is tested on 30 real-world multi-class datasets compared with several well-known related works. Experimental results supported by thorough statistical analysis confirmed the effectiveness and robustness of our proposed method.

Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition. SED naturally requires achieving two distinct supervision targets: locating fine detailed edges and identifying high-level semantics. Our motivation comes from the hypothesis that such distinct targets prevent state-of-the-art SED methods from effectively using deep supervision to improve results. To this end, we propose a novel fully convolutional neural network using diverse deep supervision (DDS) within a multi-task framework where bottom layers aim at generating category-agnostic edges, while top layers are responsible for the detection of category-aware semantic edges. To overcome the hypothesized supervision challenge, a novel information converter unit is introduced, whose effectiveness has been extensively evaluated on SBD and Cityscapes datasets., International Journal of Computer Vision

The existing pedestrian tracking applications are challenging to balance real-time performance and accuracy. We propose a detection–tracking–correction strategy based on the improved single-shot multi-box detector (SSD), Deep-SORT, and the improved multi-stage object detection architecture (Cascade-R-CNN), which takes both real-time performance and accuracy into consideration. For the detection mechanism, the SSD network is fast and efficient, but the disadvantage of the SSD network is relatively low accuracy. Therefore, the tricks such as cross-entropy loss function, deconvolution, and non-maximum suppression are introduced to improve the SSD network. Then, the improved SSD network is used as the central pedestrian detector to ensure real-time performance. For the tracking mechanism, the Deep-SORT is used to improve the mismatch between tracking and detection. For the correction mechanism, the improved Cascade R-CNN (introducing deformable convolution and group normalization) is used as the reference network to correct the detection errors. The experiment on the data set OTB-100 shows that the proposed strategy has good stability and adaptability in various complex scenes, and the conditions of missed detection and false detection are significantly reduced.

The optimal scale selection is the key problem in the study of multi-scale decision information systems. Dynamic change is one of the main features of big data, and the amount of information will often increase sharply with the passing of time. As is well known, the issue of updating the optimal scale of a multi-scale decision information system has been becoming a challenging problem in recent years, and it indeed has important theoretical and practical research values. Note that adding multiple objects can be viewed as adding one object multiple times. So, it just needs to clarify the change laws of optimal scale under the case of adding one object. Although the existing work has considered this problem in terms of the sufficient condition of updating the optimal scale, the results seem to be inaccurate. In other words, both sufficient and necessary conditions are still missing. In this paper, using sequential three-way decisions, the sufficient and necessary conditions of updating the optimal scale of a multi-scale decision information system are developed for the addition of an object, which makes the theoretical study on updating the optimal scale more complete.

Person images captured by public surveillance cameras often have low resolutions (LRs), along with uncontrolled pose variations, background clutter and occlusion. These issues cause the resolution mismatch problem when matched with high-resolution (HR) gallery images (typically available during collection), harming the person re-identification (re-id) performance. While a number of methods have been introduced based on the joint learning of super-resolution and person re-id, they ignore specific discriminant identity information encoded in LR person images, leading to ineffective model performance. In this work, we propose a novel joint bilateral-resolution identity modeling method that concurrently performs HR-specific identity feature learning with super-resolution, LR-specific identity feature learning, and person re-id optimization. We also introduce an adaptive ensemble algorithm for handling different low resolutions. Extensive evaluations validate the advantages of our method over related state-of-the-art re-id and super-resolution methods on cross-resolution re-id benchmarks. An important discovery is that leveraging LR-specific identity information enables a simple cascade of super-resolution and person re-id learning to achieve state-of-the-art performance, without elaborate model design nor bells and whistles, which has not been investigated before.

Advances in image object detection lead to applying deep convolution neural networks in the document image analysis domain. Unlike general colorful and pattern-rich objects, tables in document images have properties that limit the capacity of deep learning structures. Significant variation in size and aspect ratio and the local similarity among document components are the main challenges that require both global features for detection and local features for the separation of nearby objects. To deal with these challenges, we present TableSegNet, a compact architecture of a fully convolutional network to detect and separate tables simultaneously. TableSegNet consists of a deep convolution path to detect table regions in low resolution and a shallower path to locate table locations in high resolution and split the detected regions into individual tables. To improve the detection and separation capacity, TableSegNet uses convolution blocks of wide kernel sizes in the feature extraction process and an additional table-border class in the main output. With only 8.1 million parameters and trained purely on document images from the beginning, TableSegNet has achieved state-of-the-art F1 score at the IoU threshold of 0.9 on the ICDAR2019 and the highest number of correctly detected tables on the ICDAR2013 table detection datasets.

Continual learning has attracted much attention in recent years, and many continual learning methods based on deep neural networks have been proposed. However, several important problems about these methods may lead to high decision cost and affect the practical application of continual learning networks. First, continual learning networks treat all categories equally, although the unbalance of misclassification cost happens in real-world cases. Second, there is a trade-off between learning new knowledge and keep old knowledge, which leads to the forgetting of old knowledge (i.e., the catastrophic forgetting). Third, even if low confidence of a sample, the continual learning methods based on the neural network will still give a clear classification result. We propose a sequential three-way decision model for continual learning to address these problems, named Incremental Sequential Three-Way Decision model (ISTWD). Introducing cost-sensitive sequential three-way decision to continual learning network, ISTWD reduces the decision cost of continual learning, which may alleviate the potentially high cost caused by the accuracy loss in continual learning. Besides, ISTWD includes a checkpoint procedure to judge whether the process of continual learning should stop. Experimental results on CIFAR-100 and Tiny-ImageNet verify the effectiveness of our method.

Human detection is a technology that detects human shapes in the image and ignores everything else. However, modern person detectors have some inefficiencies in detecting pedestrians during video surveillance at night, and the accuracy rate is still insufficient. Therefore, this paper aims to increase the accuracy rate for automatic human detection at night from thermal infrared (TIR) images and real-time video sequences. For this purpose, a new architecture is proposed to enhance the backbone of the Tiny-yolov3 network. The enhanced network used the YOLOv3 algorithm’s tasks with the K-means clustering method to extract more complex features of a person. This network was pre-trained on the MS. COCO dataset to obtain the initial weights. Through the comparison with other related methods showed that the experimental results have achieved the significantly improved performance of human detection from thermal imaging in terms of accuracy, speed, and detection time. The method has achieved a high accuracy rate (90%) compared with the TF-YOLOv3 (88%) trained on the DHU Night Dataset. Although the method has achieved an accuracy rate equal to the YOLOv3-Human (90%), the detection time (4.88 ms) is less, Furthermore, the method has a higher accuracy rate (49.8%) than the YOLO (29.36%) and TF-YOLOv3 (29.8%) with lower detection time (8 ms) on the FLIR Dataset. In addition, the model has achieved a good TP detection for multiple small size of person. By improving the performance of human detection in thermal imaging at night, the method will be able to detect intruders in the night surveillance system.

Heterogeneous graph representation learning is designed to learn meaningful representation vectors from heterogeneous networks in few dimensions to extract the structure and features of the attributes of these networks. The embedding vector is the basis of and crucial to complex network analysis, and can be used in such downstream tasks as classification, clustering, link prediction, and recommendation. Key issues in heterogeneous graph neural networks pertain to ways to define heterogeneous neighbors and ways to aggregate them. Although considerable research has been devoted to homogeneous and heterogeneous network representation, the effective combination of information on the network structure and the attributes of nodes, especially effective use of meta-paths containing specific semantic information, remains rare. Here a meta-path-based heterogeneous graph neural network model is proposed. The meta-path is applied to sample the heterogeneous neighbors of each node in the network, and aggregate features of the same types of nodes to form type-related embedding. A multi-head attention mechanism is then applied to aggregate information on neighbors of different types of nodes and the model is trained by reducing context loss. Experiments on classification, clustering, link prediction, and recommendation tasks verified the validity of this model, which significantly improved the results of baseline methods.

Concept-cognitive learning (CCL) is to make machines like human beings have the ability of summarizing and reasoning. Automatically learn and find concepts from given information clues is a research focus of CCL. The existing researches mainly focuses on the concept learning methods in classical and fuzzy formal contexts, but there are few researches on the CCL of interval-valued contexts. In view of the universality of interval values in practical applications, we study the mechanism of CCL in interval-valued formal contexts. Firstly, we propose interval-valued formal contexts and a pair of dual cognitive operators as the fundamental foundation of concept learning. Then we mine the relationship between interval-valued information granules and concepts from cognitive learning and granular computing perspective. Then we systematically study the mechanism of interval-valued CCL from the establishment of interval-valued information granules (IvIGs) and its mathematical properties, and the transformation between different information granules (IGs) and clue oriented concept learning. Moreover, three algorithms are established to automatically learn concepts from different clue information. Finally, we download eight public data sets to verify the effectiveness and feasibility of the proposed algorithms from the perspective of the size of extension of concepts, running time of concept learning algorithms and the number of concepts learned by the concept learning algorithms. The experimental comparison indicates that the proposed algorithms are effective and feasible for interval-valued CCL.

Recognition of human poses and actions is crucial for autonomous systems to interact smoothly with people. However, cameras generally capture human poses in 2D as images and videos, which can have significant appearance variations across viewpoints that make the recognition tasks challenging. To address this, we explore recognizing similarity in 3D human body poses from 2D information, which has not been well-studied in existing works. Here, we propose an approach to learning a compact view-invariant embedding space from 2D body joint keypoints, without explicitly predicting 3D poses. Input ambiguities of 2D poses from projection and occlusion are difficult to represent through a deterministic mapping, and therefore we adopt a probabilistic formulation for our embedding space. Experimental results show that our embedding model achieves higher accuracy when retrieving similar poses across different camera views, in comparison with 3D pose estimation models. We also show that by training a simple temporal embedding model, we achieve superior performance on pose sequence retrieval and largely reduce the embedding dimension from stacking frame-based embeddings for efficient large-scale retrieval. Furthermore, in order to enable our embeddings to work with partially visible input, we further investigate different keypoint occlusion augmentation strategies during training. We demonstrate that these occlusion augmentations significantly improve retrieval performance on partial 2D input poses. Results on action recognition and video alignment demonstrate that using our embeddings without any additional training achieves competitive performance relative to other models specifically trained for each task., Accepted to International Journal of Computer Vision (IJCV). Code is available at https://github.com/google-research/google-research/tree/master/poem. Video synchronization results are available at https://drive.google.com/corp/drive/folders/1nhPuEcX4Lhe6iK3nv84cvSCov2eJ52Xy. arXiv admin note: text overlap with arXiv:1912.01001

In the recommender system, the user’s historical behavior data is one of the most important sources of the system’s input data. According to the user’s feedback mechanism, behavior data can be divided into explicit feedback data and implicit feedback data. However, most recommendation algorithms focus separately on explicit feedback or implicit feedback. How to combine explicit and implicit feedback for recommendation tasks has always been a research problem. In recent years, deep learning technology has dominated the research on recommendation algorithms. But even the latest neural network-based recommendation algorithm cannot exceed classic methods (such as matrix factorization) in most cases. In this work, we propose a new collaborative filtering framework with neural network architecture. On the one hand, we use both explicit feedback data and implicit feedback data as input to learn multiple representations of users and items. On the other hand, we use multi-task learning to optimize our framework and use two relatively simple auxiliary tasks to enhance the generalization ability of our framework. Extensive experiments on five real-world datasets show significant improvements in our proposed framework over the state-of-the-art methods and vanilla matrix factorization.

A Skeleton that is extracted by a skeletonization algorithm from a binary image is useful for object description, matching, recognition and compression. The parallel thinning algorithm, one of the skeletonization algorithms is well known to have computational effciency. The main contribution of this paper is that we proposed a novel fully parallel thinning algorithm based on a comprehensive investigation of the well-known Zhang-Suen (ZS)-series algorithms and the one-pass thinning algorithm (OPTA)-series algorithms, which not only has good performance in terms of (8,4) connectivity preservation and single-pixel thickness, but also has the following qualities: it is more robust to the boundary noise than the OPTA-series algorithms and it is faster than the ZS-series algorithms in terms of thinning speed, as confirmed by the experiments presented in this paper.

Condition assessment is an essential process to comprehend the condition of the water pipelines and facilitate the maintenance as well as the renewal plans. Nowadays, varied in-pipe inspection platforms equipped with closed-circuit cameras are employed to capture the internal condition of the water pipelines. However, the automated platform often faces the challenge to negotiate with the installed valves during the inspection. To ensure continuous inspection, the platform needs identify the valve automatically and activate the control mechanism to pass through it. Thus, the valves need to be detected to facilitate the negotiation and ensure that the control mechanism can take an action in time. This paper focuses on real-time valve detection using Jetson TX2™ and a lightweight algorithm, namely YOLOv3-tiny. The performance of the implementation is compared with state-of-the-art real-time detection models. The experimental results demonstrate that YOLOv3-tiny has a high detection speed in frame per second for valve detection and outperforms the state-of-the-art real-time algorithms. Hence, the deployment YOLOv3-tiny into the embedded system will aid the automated platform to accomplish the uninterrupted inspection and enhance the capability for the condition assessment of the water pipelines.

The use of automation has dramatically increased in various industries and domains including our daily lives. The manufacturing industry is no exception, as it continuously seeks to produce high-quality products at scale. A system devoid of a vision system can be considered a relatively blind tool that cannot cope with the uncertainty of handling components of different geometries that are in different positions. The growing use of vision systems is helping several application areas and contributing significantly in various stages of the production cycle and making the process more efficient and confident. This study aims to evaluate, through techniques of pattern recognition statistics, the use of a low-cost vision system that allows the accurate recognition of the target position without the aid of additional devices. We use metal parts that have the same thickness. These parts are measured using digital static pictures and are compared with the measurements performed using appropriately calibrated metrology tools. The experimental results validate the application of the proposed system in a real-world welding process.

Medical image segmentation often suffers from the challenges of class imbalance, blurred target boundaries, and small data. How to establish a framework to automatically segment medical images with these problems is an important task. Although there have been some studies on the issue, there is still a large room for improving the efficiency and quality of medical service. This paper utilizes the powerful ability of deep learning to extract features, and develops a two-stage decoding network with boundary attention (TSD-BA), which can locate the regions of interest in the target locating stage and obtain more spatial structure features in the detail refinement stage. Specifically, a deep fusion model (DFM) is used to aggregate high-level semantic features for accurately capturing the position of targets. Subsequently, a boundary attention module (BAM) is applied to further excavate the boundary features. Moreover, data augmentation and transfer learning are employed to avoid overfitting caused by small datasets. Finally, a pixel position aware (PPA) loss is introduced to focus on hard pixels and mitigate the class imbalance issues. Numerous experimental results indicate that the proposed TSD-BA achieves the best performance compared with state-of-the-art approaches.

The SARS-CoV-2 virus causes a respiratory disease in humans, known as COVID-19. The confirmatory diagnostic of this disease occurs through the real-time reverse transcription and polymerase chain reaction test (RT-qPCR). However, the period of obtaining the results limits the application of the mass test. Thus, chest X-ray computed tomography (CT) images are analyzed to help diagnose the disease. However, during an outbreak of a disease that causes respiratory problems, radiologists may be overwhelmed with analyzing medical images. In the literature, some studies used feature extraction techniques based on CNNs, with classification models to identify COVID-19 and non-COVID-19. This work compare the performance of applying pre-trained CNNs in conjunction with classification methods based on machine learning algorithms. The main objective is to analyze the impact of the features extracted by CNNs, in the construction of models to classify COVID-19 and non-COVID-19. A SARS-CoV-2 CT data-set is used in experimental tests. The CNNs implemented are visual geometry group (VGG-16 and VGG-19), inception V3 (IV3), and EfficientNet-B0 (EB0). The classification methods were k-nearest neighbor (KNN), support vector machine (SVM), and explainable deep neural networks (xDNN). In the experiments, the best results were obtained by the EfficientNet model used to extract data and the SVM with an RBF kernel. This approach achieved an average performance of 0.9856 in the precision macro, 0.9853 in the sensitivity macro, 0.9853 in the specificity macro, and 0.9853 in the F1 score macro.

Human actions are sequential, and structured patterns of the body parts and their movements. In this paper, we present a hybrid two-stream convolutional neural network (H2SCNN) for the recognition of actions from sequences by exploring the statistical information like skeletons. This aims to exploit the skeletons completely and identify the actions properly by merging the different motion related features. These features include motion and joint features. The framework calculates the distance between consecutive sequences to form the temporal information required for the recognition process. The proposed H2SCNN is based on two stages. The neighbourhood feature model will be used to process both inputs individually in the first step. In the second stage, it performs ensemble learning and takes advantage of the diversity of multiple features by fusing them together. The multi-task ensemble learning model helps the system to improve the prediction ability of H2SCNN. Experiments on the benchmark dataset have shown the superiority of the proposed model with other recent approaches.

Image binarization of uneven lighted images, using thresholding techniques, is still a challenging task. Adaptive thresholding methods are the widely adopted approaches for binarization of uneven lighting images. However, the efficacy of these adaptive thresholding methods is highly sensitive to the criteria function used for measuring the bimodal property of the gray level distribution of a local region. In this paper, we propose Gaussian Mixture Model (GMM) which is based on adaptive thresholding for binarizing uneven lighting images. The proposed GMM based criteria function efficiently partitioning the uneven light images into bimodal and unimodal subimages with low uneven light effect. At first, the bimodal subimages are binarized using Otsu’s thresholding approach, followed by unimodal subimages being thresholded using the bilinear interpolation of neighbouring thresholds of bimodal subimages. Next a fast Expectation Maximization(EM) algorithm is developed to reduce the computational complexity of the GMM. Experimental results on different uneven light images demonstrate that the proposed adaptive thresholding outperforms the other considered methods with an avg. misclassification error of 1.68 % and an average computation time of 1.50 seconds. The computational time can be further reduced by a specially purposed hardware and parallel processing of each subimages for real time applications.

High-dimensional small sample data are prone to the curse of dimensionality and overfitting and contain many irrelevant and redundant features. In order to solve these feature selection problems, a new Two-stage Hybrid Feature Selection Algorithm (Ts-HFSA) is proposed. The first stage uses the Filter method combined with the Wrapper method to adaptively remove irrelevant features. In the second stage, a De-redundancy Algorithm of Fusing Approximate Markov Blanket with L1 Regular Term (DA2MBL1) is used to solve the AMB’s problem of information loss when deleting redundant features and potential redundancy in the subset of features obtained by AMB. The experimental results on multiple UCI public data sets and datasets from the material foundation of Chinese medicine showed that the Ts-HFSA better deleted irrelevant features and redundant features, found smaller and higher quality feature subsets, and improved stability, indicating that it offers more advantages than AMB, FCBF, RF, GBDT, XGBoost, Lasso, and CI_AMB. Moreover, in the face of data of the material foundation of Chinese medicine, with higher feature dimensions and fewer sample sizes, Ts-HFSA performed better, which can also improve the precision of the model after greatly reducing the dimension. The results indicated that Ts-HFSA is an effective method for feature selection of high-dimensional small samples and an excellent research method for the material foundation of Chinese medicine.

Pain management is gaining the attention of clinical practitioners to relieve patients from pain in an effective manner. Pain management is primarily dependent on pain measurement. Researchers have proposed various techniques to measure pain from facial expressions improving the accuracy and efficiency of the traditional pain measurement such as self-reporting and visual analog scale. Developments in the field of deep learning have further enhanced the pain assessment technique. Despite of the state of the art performance of deep learning algorithms, adaptation to new subjects is still a problem due to availability of a few samples of the same. Authors have addressed this issue by employing a model agnostic meta-learning algorithm for pain detection and fast adaptation of the trained algorithm for new subjects using only a few labeled images. The model is pre-trained with labeled images of subjects with five pain levels to acquire meta-knowledge in the presented work. This meta-knowledge is then used to adapt the model to a new learning task in the form of a new subject. The proposed model is evaluated on a benchmark dataset, i.e., UNBC McMaster pain archive database. Experimental results show that the model can be very easily adapted to new subjects with the accuracy of $$96\%$$ and $$98\%$$ for 1-shot and 5-shot learning respectively, proving the potential of the proposed algorithm for clinical use.

Inverse reinforcement learning features a reward function based on reward features and positive demonstrations. When complex learning tasks are performed, the entire state space is used to form the set of reward features, but this large set results in a long computational time. Retrieving important states from the full state space addresses this problem. This study formulated a method that extracts critical features by combining negative and positive demonstrations of searching for critical states from the entire state space to increase learning efficiency. In this method, two types of demonstrations are used: positive demonstrations, which are given by experts and agents imitate, and negative demonstrations, which demonstrate incorrect motions to be avoided by agents. All significant features are extracted by identifying the critical states over the entire state space. This is achieved by comparing the difference between the negative and positive demonstrations. When these critical states are identified, they form the set of reward features, and a reward function is derived that enables agents to learn a policy using reinforcement learning quickly. A speeding car simulation was used to verify the proposed method. The simulation results demonstrate that the proposed approach allows an agent to search for a positive strategy and that the agent then displays intelligent expert-like behavior.

Ghosting artifacts caused by moving objects and misalignments are a key challenge in constructing high dynamic range (HDR) images. Current methods first register the input low dynamic range (LDR) images using optical flow before merging them. This process is error-prone, and often causes ghosting in the resulting merged image. We propose a novel dual-attention-guided end-to-end deep neural network, called DAHDRNet, which produces high-quality ghost-free HDR images. Unlike previous methods that directly stack the LDR images or features for merging, we use dual-attention modules to guide the merging according to the reference image. DAHDRNet thus exploits both spatial attention and feature channel attention to achieve ghost-free merging. The spatial attention modules automatically suppress undesired components caused by misalignments and saturation, and enhance the fine details in the non-reference images. The channel attention modules adaptively rescale channel-wise features by considering the inter-dependencies between channels. The dual-attention approach is applied recurrently to further improve feature representation, and thus alignment. A dilated residual dense block is devised to make full use of the hierarchical features and increase the receptive field when hallucinating missing details. We employ a hybrid loss function, which consists of a perceptual loss, a total variation loss, and a content loss to recover photo-realistic images. Although DAHDRNet is not flow-based, it can be applied to flow-based registration to reduce artifacts caused by optical-flow estimation errors. Experiments on different datasets show that the proposed DAHDRNet achieves state-of-the-art quantitative and qualitative results.

Semantic information in the human brain is organized into multiple networks, but the fine-grain relationships between them are poorly understood. In this study, we compared semantic maps obtained from two functional magnetic resonance imaging experiments in the same participants: one that used silent movies as stimuli and another that used narrative stories. Movies evoked activity from a network of modality-specific, semantically selective areas in visual cortex. Stories evoked activity from another network of semantically selective areas immediately anterior to visual cortex. Remarkably, the pattern of semantic selectivity in these two distinct networks corresponded along the boundary of visual cortex: for visual categories represented posterior to the boundary, the same categories were represented linguistically on the anterior side. These results suggest that these two networks are smoothly joined to form one contiguous map.

This study proposes a new approach of $$H_{\infty }$$ deconvolution filtering of 2D system described by Fornasini–Marchesini model and Tchebichef moments. The challenge of this method is to generate an unknown 2D signal by transmission channel. This canal is mobilized by convolution system and deconvolution filter to rebuild the output signal. To resolve this problem, firstly, we use the Tchebichef moments to extract the feature vectors of a medicinal Cannabis sativa plant for generating the input system with the minimum information. Next, we implement the system with the model of Fornasini–Marchesini for convolution and deconvolution. However, the free matrix variables are used to eliminate coupling between Lyapunov matrix and system matrices to obtain sufficient conditions in linear matrix inequality form to ensure the desired stability and performance of the error systems. Experimental results show that the new approach for $$H_{\infty }$$ deconvolution filtering of 2D systems described by the Fornasini–Marchesini model and Tchebichef moments achieves good performance than the recent works.

This paper presents a Function-as-a-Service (FaaS) approach for deploying managed cloud functions onto heterogeneous cloud infrastructures. Current FaaS systems, such as AWS Lambda, allow domain-specific functionality, such as AI, HPC and image processing, to be deployed in the cloud while abstracting users from infrastructure and platform concerns. Existing approaches, however, use a single type of resource configuration to execute all function requests. In this paper, we present a novel FaaS approach that allows cloud functions to be effectively executed across heterogeneous compute resources, including hardware accelerators such as GPUs and FPGAs. We implement heterogeneous scheduling to tailor resource selection to each request, taking into account performance and cost concerns. In this way, our approach makes use of different processor types and quantities (e.g. 2 CPU cores), uniquely suited to handle different types of workload, potentially providing improved performance at a reduced cost. We validate our approach in three application domains: machine learning, bio-informatics, and physics, and target a hardware platform with a combined computational capacity of 24 FPGAs and 12 CPU cores. Compared to traditional FaaS, our approach achieves a cost improvement for non-uniform traffic of up to 8.9 times, while maintaining performance objectives.

As an effective algorithm in feature learning, autoencoder (AE) and its variants have been widely applied in machine learning. To overcome the expensive time consumption in backpropagation learning and parameters iterative tuning, extreme learning machine (ELM) is combined with AE, developed as ELM-based AE (ELM-AE) in unsupervised feature learning. However, random projection of ELM makes the learned features not stable for the final target recognition. On the other hand, considering to enhance high-order nonlinear expression in ELM-AE, common methods increase the computation overhead. Therefore, this paper proposes to construct a new ELM-AE based on approximation of hybrid high-order polynomial functions. The proposed model will keep fast learning speed by linearization of the high-order nonlinear expression, be robust to random projection issue, and learn discriminative features for pattern recognition. Two feature learning application scenarios, feature reconstruction and dimension reduction, are discussed based on different ELM-AE models. Experiments on publicly available datasets including small and large datasets demonstrate the proposed model’s feasibility and superiority in feature learning.

Quantification and recognition of geochemical patterns are extremely important for geochemical prospecting and can facilitate a better understanding of regional metallogenesis. Recognition of such patterns with deep learning (DL) algorithms has attracted considerable attention, as these algorithms can generally extract high-level geochemical features and thus create models in which geochemical patterns are fully exploited. These DL algorithms are usually constructed to be compatible and consistent with the underlying data, but their interpretability and pertinent physical constraints (such as granitic intrusions related to magmatic-hydrothermal mineralization) are generally overlooked. This paper introduces a physically constrained variational autoencoder (VAE) architecture to identify geochemical patterns associated with tungsten polymetallic mineralization. We first identify physical constraints from geological characteristics and metallogenic regulation via the methods of fry analysis, standard deviation ellipses, and fractal analysis to reveal the controlling function of granitic intrusions on mineralization. Subsequently, we construct the physical constraints based on the nonlinear controlling function and add the geological constraints into the VAE loss function as a penalty term. After optimization of the network parameters, the well-trained physically constrained VAE architecture can recognize the geochemical anomaly patterns that the conventional VAE cannot. These extracted geochemical anomaly patterns generally show a strong spatial relationship with the granitic intrusions. In addition, the performance measures involving the receiver operating characteristic curve and success-rate further indicate that the generalization accuracy of the conventional VAE can be enhanced via physics-based regularization. These results suggest that the proposed physically constrained VAE, which integrates physical knowledge into the VAE loss function, not only improves the geochemical pattern recognition performance but also demonstrates consistency with geological and physical domain knowledge.

Even though most existing monocular 3D human pose estimation methods achieve very competitive performance, they are limited in estimating heterogeneous human body parts with the same decoder architecture. In this work, we present an approach to build a part-aware 3D human pose estimator to better deal with these heterogeneous human body parts. Our proposed method consists of two learning stages: (1) searching suitable decoder architectures for specific parts and (2) training the part-aware 3D human pose estimator built with these optimized neural architectures. Consequently, our searched model is very efficient and compact and can automatically select a suitable decoder architecture to estimate each human body part. In comparison with previous state-of-the-art models built with ResNet-50 network, our method can achieve better performance and reduce 64.4% parameters and 8.5% FLOPs (multiply-adds). We validate the robustness and stability of our searched models by conducting extensive and rigorous ablation experiments. Our method can advance state-of-the-art accuracy on both the single-person and multi-person 3D human pose estimation benchmarks with affordable computational cost.

Human activity recognition (HAR) based on wearable devices has found wide applications in fitness, health care, etc. Given the personalized wearing styles of such devices and distinctive motion patterns, the activities of daily living normally vary from person to person in terms of strength, amplitude, speed, category, etc. The specialization of a universal HAR model to a specific subject without experiencing catastrophic forgetting is a significant challenge. In this paper, we propose a novel incremental learning method, namely, an adaptive coefficient-based kernelized and regularized network (KeRNet-AC), for personalized activity recognition. During the adaptation stage of the model training process, KeRNet-AC consistently monitors the probable ill-conditioned degree of the generated solution, which we believe is strongly correlated with the catastrophic forgetting problem, and automatically makes the solution well conditioned. To reduce the computational complexity of KeRNet-AC, we also introduce an active data selection principle into KeRNet-AC. This variation is called A-KeRNet-AC. To evaluate the performance of KeRNet-AC and A-KeRNet-AC, we conduct extensive experiments on five public activity datasets. The experimental results demonstrate that KeRNet-AC outperforms related state-of-the-art methods in most cases and that A-KeRNet-AC can quickly perform model training and activity prediction. Moreover, the performance of the proposed methods steadily improves during the adaptation stage and ultimately converges without degradation, demonstrating the strong potential of KeRNet-AC and A-KeRNet-AC for personalized activity recognition.

Due to the rapid increase in exchange of text information via internet network, the security and the reliability of the digital content has become a major research issue. The main challenges faced by researchers are content authentication, integrity verification and tampering detection of digital contents. In this paper, these issues are addressed with great emphasis on text information, which is natural language dependent. Hence, a novel intelligent zero-watermarking approach is proposed for content authentication and tampering detection of Arabic text contents. In the proposed approach, both the embedding and extracting of the watermark are logically implemented, which causes no change on the digital text. This is achieved by using fourth-level-order and alphanumeric mechanism of Markov model as a soft computing technique for the analysis of Arabic text to obtain the features of the given text which is considered as the digital watermark. This digital watermark is later used for the detection of any tampering attack on the received Arabic text. An extensive set of experiments using four datasets of varying lengths proves the effectiveness of the proposed approach in terms of robustness, effectiveness and applicability under multiple random locations of insertion, reorder and deletion attacks. Compared with baseline approaches, the proposed approach has improved performance regarding watermark robustness and tampering detection accuracy.

Unconstrained face alignment is still a challenging problem due to the large poses, partial occlusions and complicated illuminations. To address these issues, in this paper, we propose a mixed attention hourglass network (MAttHG) to learn more discriminative representations by modeling the correlated relationships between features. Specifically, by integrating the attention module from features of different levels in the stacked hourglass networks, MAttHG can capture rich contextual correlations, which can be further used to combine local features to better model the spatial position relationship of facial landmarks. Furthermore, by combining the hourglass network and the attention module, MAttHG can effectively model the global attention and local attention to enhance the facial shape constraints for robust face alignment. Moreover, a head pose prediction module is designed to adaptively adjust the weight of each sample in the training set and redefine the loss function for addressing the problem of data imbalance. Experimental results on challenging benchmark datasets demonstrate the superiority of our MAttHG over state-of-the-art face alignment methods.

Knowledge distillation has been widely used to produce portable and efficient neural networks which can be well applied on edge devices for computer vision tasks. However, almost all top-performing knowledge distillation methods need to access the original training data, which usually has a huge size and is often unavailable. To tackle this problem, we propose a novel data-free approach in this paper, named Dual Discriminator Adversarial Distillation (DDAD) to distill a neural network without the need of any training data or meta-data. To be specific, we use a generator to create samples through dual discriminator adversarial distillation, which mimics the original training data. The generator not only uses the pre-trained teacher’s intrinsic statistics in existing batch normalization layers but also obtains the maximum discrepancy from the student model. Then the generated samples are used to train the compact student network under the supervision of the teacher. The proposed method obtains an efficient student network which closely approximates its teacher network, without using the original training data. Extensive experiments are conducted to demonstrate the effectiveness of the proposed approach on CIFAR, Caltech101 and ImageNet datasets for classification tasks. Moreover, we extend our method to semantic segmentation tasks on several public datasets such as CamVid, NYUv2, Cityscapes and VOC 2012. To the best of our knowledge, this is the first work on generative model based data-free knowledge distillation on large-scale datasets such as ImageNet, Cityscapes and VOC 2012. Experiments show that our method outperforms all baselines for data-free knowledge distillation.

Due to the rich and comprehensive information of multiview data, multi-view learning has been attracted widely attention. Efficiently exploiting multiview data to select discriminative features to improve classification performance is very important in multi-view learning. Most existing supervised methods learn an entire projection matrix by concatenating multiple views into a long vector, thus they often ignore the relationship between views. To solve this problem, in this paper, we propose a novel sparse robust multiview feature selection model, which simultaneously considers the robustness, individuality and commonality of views via adaptive-weighting strategy. The model adopts the soft capped-norm loss to calculate the residual in each view to effectively reduce the impact of noises and outliers. Moreover, the model employs the adaptive-weighting strategy to show the individuality and commonality of views without introducing extra parameters. In addition, it introduces structured sparsity regularization to select the discriminative features. An efficient iterative algorithm is proposed to individually learn each block of the projection matrix with low computational complexity, and the convergence of the proposed optimization algorithm is verified theoretically and experimentally. The comparative experiments are conducted on multiview datasets with several state-of-the-art algorithms, and the experimental results show that the proposed method gets better performance than the others.

This paper develops a weak-label-based global and local multi-view multi-label learning with three-way clustering (WL-GLMVML-ATC) to solve multi-view multi-label data sets and exploit more authentic global and local label correlations of both the whole data set and each view simultaneously. Different from the traditional learning methods, WL-GLMVML-ATC pays more attention to the solutions of weak-label cases and uncertain relationships of clusters with the usage of Universum and active three-way clustering. According to Universum notion, even though the size of labeled instances is much more smaller than the unlabeled ones, the useful sample information can still be enhanced. Through the active three-way clustering strategy, the belongingness of instances to a cluster depend on the probabilities of uncertain instances belonging to core regions. This strategy brings a more authentic local label correlation since many traditional methods suppose that instances and the corresponding clusters always exhibit certain relationships such as belong-to definitely and not belong-to definitely. This hypothesis is not ubiquitous in real-world applications. According to the experiments, we can see WL-GLMVML-ATC (1) achieves a better performance, be superior to the classical multi-view learning methods and multi-label learning methods in statistical, advances the development of these learning methods in final; (2) won’t add too much running time; (3) has a good convergence and ability to process multi-view multi-label data sets.

Kirsten Ras (KRAS) mutation identification has great clinical significance to formulate the rectal cancer treatment scheme. Recently, the development of deep learning does much help to improve the computer-aided diagnosis technology. However, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we proposed a joint network named segmentation-based multi-scale attention model (SMSAM) to predict the mutation status of KRAS gene in rectal cancer. More specifically, the network performs segmentation and prediction tasks at the same time. The two tasks mutually transfer knowledge between each other by sharing the same encoder. Meanwhile, two universal multi-scale attention blocks are introduced to ensure that the network more focuses on the region of interest. Besides, we also proposed an entropy branch to provide more discriminative features for the model. Finally, the method is evaluated on internal and external datasets. The results show that the comprehensive performance of SMSAM is better than the existing methods. The code and model have been publicly available.

In real data sets, objects are usually measured by multiple scales under the same attribute. Many information systems are given dominance relations on account of various factors which make classical equivalence relations change accordingly. This paper investigates the optimal scale selection for multi-scale ordered decision systems based on evidence theory. Five concepts of optimal scales related to rough set theory and the Dempster–Shafer theory of evidence in multi-scale ordered information/decision systems are first defined. Relationships are then clarified among $$\ge$$ -optimal scale, $$\ge$$ -lower approximation and $$\ge$$ -upper approximation optimal scales as well as $$\ge$$ -belief and $$\ge$$ -plausibility optimal scales in multi-scale ordered information systems and consistent multi-scale ordered decision systems respectively. Finally, in inconsistent multi-scale ordered decision systems, by introducing a notion of $$\ge$$ -generalized decision optimal scale, relationships among different types of optimal scales are also examined.

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG [Abstract] Currently, optical coherence tomography is one of the most used medical imaging modalities, offering cross-sectional representations of the studied tissues. This image modality is specially relevant for the analysis of the retina, since it is the internal part of the human body that allows an almost direct examination without invasive techniques. One of the most representative cases of use of this medical imaging modality is for the identification and characterization of intraretinal fluid accumulations, critical for the diagnosis of one of the main causes of blindness in developed countries: the Diabetic Macular Edema. The study of these fluid accumulations is particularly interesting, both from the point of view of pattern recognition and from the different branches of health sciences. As these fluid accumulations are intermingled with retinal tissues, they present numerous variants according to their severity, and change their appearance depending on the configuration of the device; they are a perfect subject for an in-depth research, as they are considered to be a problem without a strict solution. In this work, we propose a comprehensive and detailed analysis of the patterns that characterize them. We employed a pool of 11 different texture and intensity feature families (giving a total of 510 markers) which we have analyzed using three different feature selection strategies and seven complementary classification algorithms. By doing so, we have been able to narrow down and explain the factors affecting this kind of accumulations and tissue lesions by means of machine learning techniques with a pipeline specially designed for this purpose. Instituto de Salud Carlos III, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project, Ayudas para la formación de profesorado universitario (FPU), grant ref. FPU18/02271; Ministerio de Ciencia e Innovación, Government of Spain through the research project with reference PID2019-108435RB-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, Grupos de Referencia Competitiva, grant ref. ED431C 2020/24 and through the postdoctoral grant contract ref. ED481B 2021/059; Axencia Galega de Innovación (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%). Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Funding for open access charge: Universidade da Coruña/CISUG Xunta de Galicia; ED431C 2020/24 Xunta de Galicia; ED481B 2021/059 Xunta de Galicia; IN845D 2020/38 Xunta de Galicia; ED431G 2019/01

Mongolian is a language spoken in Inner Mongolia, China. In the recognition process, due to the shooting angle and other reasons, the image and text will be deformed, which will cause certain difficulties in recognition. This paper propose a triplet attention Mogrifier network (TAMN) for print Mongolian text recognition. The network uses a spatial transformation network to correct deformed Mongolian images. It uses gated recurrent convolution layers (GRCL) combine with triplet attention module to extract image features for the corrected images. The Mogrifier long short-term memory (LSTM) network gets the context sequence information in the feature and finally uses the decoder’s LSTM attention to get the prediction result. Experimental results show the spatial transformation network can effectively recognize deformed Mongolian images, and the recognition accuracy can reach 90.30%. This network achieves good performance in Mongolian text recognition compare with the current mainstream text recognition network. The dataset has been publicly available at https://github.com/ShaoDonCui/Mongolian-recognition .

Representation learning for video is increasingly gaining attention in the field of computer vision. For instance, video prediction models enable activity and scene forecasting or vision-based planning and control. In this article, we investigate the combination of differentiable physics and spatial transformers in a deep action conditional video representation network. By this combination our model learns a physically interpretable latent representation and can identify physical parameters. We propose supervised and self-supervised learning methods for our architecture. In experiments, we consider simulated scenarios with pushing, sliding and colliding objects, for which we also analyze the observability of the physical properties. We demonstrate that our network can learn to encode images and identify physical properties like mass and friction from videos and action sequences. We evaluate the accuracy of our training methods, and demonstrate the ability of our method to predict future video frames from input images and actions.

Optical marker-based motion capture (MoCap) remains the predominant way to acquire high-fidelity articulated body motions. We introduce DeMoCap, the first data-driven approach for end-to-end marker-based MoCap, using only a sparse setup of spatio-temporally aligned, consumer-grade infrared-depth cameras. Trading off some of their typical features, our approach is the sole robust option for far lower-cost marker-based MoCap than high-end solutions. We introduce an end-to-end differentiable markers-to-pose model to solve a set of challenges such as under-constrained position estimates, noisy input data and spatial configuration invariance. We simultaneously handle depth and marker detection noise, label and localize the markers, and estimate the 3D pose by introducing a novel spatial 3D coordinate regression technique under a multi-view rendering and supervision concept. DeMoCap is driven by a special dataset captured with 4 spatio-temporally aligned low-cost Intel RealSense D415 sensors and a 24 MXT40S camera professional MoCap system, used as input and ground truth, respectively.

Multiple Object Tracking (MOT) has attracted increasing interests in recent years, which plays a significant role in video analysis. MOT aims to track the specific targets as whole trajectories and locate the positions of the trajectory at different times. These trajectories are usually applied in Action Recognition, Anomaly Detection, Crowd Analysis and Multiple-Camera Tracking, etc. However, existing methods are still a challenge in complex scene. Generating false (impure, incomplete) tracklets directly affects the performance of subsequent tasks. Therefore, we propose a novel architecture, Siamese Bi-directional GRU, to construct Cleaving Network and Re-connection Network as trajectory post-processing. Cleaving Network is able to split the impure tracklets as several pure sub-tracklets, and Re-connection Network aims to re-connect the tracklets which belong to same person as whole trajectory. In addition, our methods are extended to Multiple-Camera Tracking, however, current methods rarely consider the spatial-temporal constraint, which increases redundant trajectory matching. Therefore, we present Position Projection Network (PPN) to convert trajectory position from local camera-coordinate to global world-coodrinate, which provides adequate and accurate temporal-spatial information for trajectory association. The proposed technique is evaluated over two widely used datasets MOT16 and Duke-MTMCT, and experiments demonstrate its superior effectiveness as compared with the state-of-the-arts.

With the growing demands of data products, the selection of satellite image data products becomes a challenging decision issue for customers. The objective of this study is to propose a practically sound decision-making approach for solving the satellite image data products selection problems. First, the influencing factors of selecting satellite image data products are identified. Then, hybrid evaluation information is recommended to represent these criteria. That is, numerical and interval-valued quantification is used for quantitative criteria, and picture fuzzy numbers (PFNs) are considered to express qualitative criteria. To reflect decision makers’ preferences, a non-linear optimization is implemented to treat criteria weights with constraints. Thereafter, some penalty functions are defined and the artificial fish swarm algorithm (AFSA) is improved to calculate weight values. Furthermore, six main parameters of AFSA are analyzed. Compared with other commonly used algorithms (such as genetic algorithm (GA) and particle swarm optimization (PSO)), the largest advantage of AFSA is its high robustness of parameters and initial values. Finally, the traditional multi-attributive border approximation area comparison (MABAC) is modified with likelihood measures to obtain the best data product in hybrid evaluation environments. Furthermore, the feasibility and effectiveness of the proposed approach is validated by comparing with existing methods in some representative literature. The results demonstrate that the proposed method is feasible and can provide useful guidelines for the selection and pricing of satellite image data products.