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Craters are among the most abundant features on the surface of many planets with great importance for planetary scientists. They reveal chronology information about planets and may be used for autonomous spacecraft navigation and landing. Although numerous research efforts have been carried out in the field of crater detection, existing crater detection algorithms (CDAs) are only helpful in a limited number of applications. A promising crater detection approach involves two main steps: 1) hypothesis generation (HG) and 2) hypothesis verification (HV). During HG, potential crater locations are detected. The validity of the hypothesized crater locations is then tested in a HV step. In this context, we discuss some commonly used algorithms for HG such as highlight-shadow region detection and Hough transform as well as our novel and enhanced algorithms based on interest point detection and convex grouping. A key objective of this paper is to analyze their performance while paying special attention to how they affect the accuracy of the verification step. To deal with different size craters, we focus on multiscale HG. For HV, we have chosen convolutional neural networks which have recently achieved state-of-the-art performance in many computer vision applications. Due to the variation of test sets in the literature, it is often challenging to compare the performance of different CDAs in a fair way. In this paper, we present a comprehensive performance evaluation and comparison of CDAs. Each algorithm has been trained/tested using common data sets generated by a systematic approach. [ABSTRACT FROM AUTHOR]

This paper presents a method to improve the traditional mixture of deformable part models (MDPM) method from the learning perspective. First, an object part configuration learning algorithm based on group sparsity constraint is introduced to automatically discover the object part number, size, and location. The algorithm imposes two additional regularization terms in addition to the standard hinge loss function. The first term focuses on automatic part selection and the second term focuses on automatic part placement. Second, this paper introduces an improved MDPM training framework. The framework applies a learned transformation to normalize the prediction score from each individual deformable part model (DPM) into a pseudo probability such that the partition of the entire object appearance feature space becomes less sensitive to the prior distributions of different DPMs. Finally, the two proposed improvements are combined and formulated under the expectation-maximization framework. We evaluate our method mainly using the PASCAL VOC2007 and VOC2010 detection benchmarks and show that the proposed learning algorithms could increase the detection mean AP score by 2.4% and 0.9%, respectively, on these two data sets when using the proposed part selection method and the training algorithm. We also present further in-depth analysis of the proposed algorithm in the experiments. [ABSTRACT FROM PUBLISHER]

Underwater environments create a challenging channel for communications. In this paper, we design a novel receiver system by exploring the machine learning technique–Deep Belief Network (DBN) – to combat the signal distortion caused by the Doppler effect and multi-path propagation. We evaluate the performance of the proposed receiver system in both simulation experiments and sea trials. Our proposed receiver system comprises of DBN based de-noising and classification of the received signal. First, the received signal is segmented into frames before the each of these frames is individually pre-processed using a novel pixelization algorithm. Then, using the DBN based de-noising algorithm, features are extracted from these frames and used to reconstruct the received signal. Finally, DBN based classification of the reconstructed signal occurs. Our proposed DBN based receiver system does show better performance in channels influenced by the Doppler effect and multi-path propagation with a performance improvement of 13.2dB at 10−3 Bit Error Rate (BER). [ABSTRACT FROM AUTHOR]

Common spatial pattern (CSP) is a classic method commonly used in multichannel electroencephalogram (EEG) signal processing, which aims to extract effective features for binary classification by solving spatial filters that maximize the ratio of filtered dispersion between two classes. The aim of this paper is to improve the performance of the conventional CSP method, which will be badly influenced by noises. The recently proposed quantized minimum error entropy (QMEE) criterion is applied to structure a new objective function instead of the ${L} _{2}$ -norm in the conventional CSP. Quantization is utilized to reduce the computational complexity. The new objective function is optimized by a gradient-based iterative algorithm. The desirable performance of the QMEE-based CSP method, namely CSP-QMEE, is demonstrated with a toy example and two real EEG datasets, including Dataset IIb of the brain–computer interfaces (BCIs) Competition IV (three channels) and Dataset IIIa of the BCI Competition III (60 channels). The new method can achieve satisfactory performance compared to existing methods on all datasets. The promising results in this paper suggest that the CSP-QMEE may become a powerful tool for BCIs. [ABSTRACT FROM AUTHOR]

Accompanied with the rapid increase of the demand for routine examination of leucorrhea, efficiency and accuracy become the primary task. However, in super depth of field (SDoF) system, the problem of automatic detection and localization of cells in leucorrhea micro-images is still a big challenge. The changing of the relative position between the cell center and focus plane of microscope lead to variable cell morphological structure in the two-dimensional image, which is an important reason for the low accuracy of current deep learning target detection algorithms. In this paper, an object detection method based on Retinanet in state of super depth of field is proposed, which can achieve high precision detecting of leucorrhea components by the SDoF feature aggregation module. Compared with the current mainstream algorithms, the mean average accuracy (mAP) index has been improved significantly, the mAP index is 82.7% for SDoF module and 83.0% for SDoF+ module, with an average increase of more than 10%. These improved features can significantly improve the efficiency and accuracy of the algorithm. The algorithm proposed in this paper can be integrated into the leucorrhea automatic detection system. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel backtrackless aligned-spatial graph convolutional network (BASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrary-sized graphs into fixed-sized backtrackless aligned grid structures and define a new spatial graph convolution operation associated with the grid structures. We show that the proposed BASGCN model not only reduces the problems of information loss and imprecise information representation arising in existing spatially-based graph convolutional network (GCN) models, but also bridges the theoretical gap between traditional convolutional neural network (CNN) models and spatially-based GCN models. Furthermore, the proposed BASGCN model can both adaptively discriminate the importance between specified vertices during the convolution process and reduce the notorious tottering problem of existing spatially-based GCNs related to the Weisfeiler-Lehman algorithm, explaining the effectiveness of the proposed model. Experiments on standard graph datasets demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Feature template matching (FTM) was proposed by Zhang and Qiu in 2011 for spectrum sensing in cognitive radio. Theoretical analysis for FTM is, however, missing in the literature. This paper will address this issue. Another new direction suggested by this paper is a nonlinear version of FTM, which is called kernel FTM (KFTM). The proposed nonlinear algorithm is performed on data mapped to a kernel space usually with higher dimension by a general nonlinear mapping. The high-dimensional data are cheaply operated with the so-called kernel trick. Furthermore, higher order statistics is considered in the proposed algorithm. Simulations using the real-world measurements of digital television (DTV) signal show that a gain of more than 4 dB can be achieved for KFTM over its linear counterpart FTM. Theoretical analysis is conducted for KFTM (that can be directly applied to FTM). For the first time, a theoretical justification is also performed for FTM. The concentration inequalities of KFTM, which are valid for arbitrary dimensions, are established as a result of concentration of measure phenomenon. The closed-form expressions of the probability distributions (under null hypothesis) are derived for both FTM and KFTM under different kernel functions. The obtained closed-form results agree with simulations. The closed-form expressions of the decision thresholds for a target false-alarm probability are obtained as well. The thresholds are noise power independent; thus, the proposed algorithm overcomes the difficulty of noise uncertainty. [ABSTRACT FROM PUBLISHER]

Partial discharge (PD) detection is an effective way to find defects and diagnose the insulation condition of power equipment. During manufacturing and operating, there may exist multi-source PD signals in the equipment, which could seriously affect the accuracy of subsequent defect analyses. In this paper, a universal separation methodology using linear prediction analysis (LPA) and the isolation forest algorithm (IFA) is introduced. By approximating the present waveform point by a linear combination of several points in the past, a 12-D linear prediction cepstrum coefficient (LPCC) feature space can be established which can accurately characterize PD waveforms. In this paper, we applied principal component analysis (PCA) to reduce the feature space to 2-D space. The IFA was adopted to separate multi-source PD signals which quantified the degree of clustering and added one more parameter based on the original features. Thus, the proposed method can separate multiple PD sources effectively even if the difference in the features of different types of PDs is small. In addition, the proposed algorithm easily removed the noise points during the separation process. The algorithm was applied to different types of PD signals on 35-kV transformers and a three-source PD data set on a 252-kV gas-insulated switchgear (GIS) platform. The separation results confirm that the proposed algorithm can effectively separate and distinguish various PD signals. [ABSTRACT FROM AUTHOR]

Video-based human action recognition is currently one of the most active research areas in computer vision. Various research studies indicate that the performance of action recognition is highly dependent on the type of features being extracted and how the actions are represented. Since the release of the Kinect camera, a large number of Kinect-based human action recognition techniques have been proposed in the literature. However, there still does not exist a thorough comparison of these Kinect-based techniques under the grouping of feature types, such as handcrafted versus deep learning features and depth-based versus skeleton-based features. In this paper, we analyze and compare 10 recent Kinect-based algorithms for both cross-subject action recognition and cross-view action recognition using six benchmark datasets. In addition, we have implemented and improved some of these techniques and included their variants in the comparison. Our experiments show that the majority of methods perform better on cross-subject action recognition than cross-view action recognition, that the skeleton-based features are more robust for cross-view recognition than the depth-based features, and that the deep learning features are suitable for large datasets. [ABSTRACT FROM AUTHOR]

This paper demonstrates that machine learning can be used to create effective algorithm selectors that select between power system control algorithms depending on the state of a network, achieving better performance than always using the same algorithm for every state. Also presented is a novel method for creating algorithm selectors that consider two objectives. The method is used to develop algorithm selectors for power flow management algorithms on versions of the IEEE 14- and 57-bus networks, and a network derived from a real distribution network. The selectors choose from within a diverse set of power flow management algorithms, including those based on constraint satisfaction, optimal power flow, power flow sensitivity factors, and linear programming. The network state-based algorithm selectors offer performance benefits over always using the same power flow management algorithm for every state, in terms of minimizing the number of overloads while also minimizing the curtailment applied to generators. [ABSTRACT FROM AUTHOR]

Detection of salient objects from images is gaining increasing research interest in recent years as it can substantially facilitate a wide range of content-based multimedia applications. Based on the assumption that foreground salient regions are distinctive within a certain context, most conventional approaches rely on a number of hand-designed features and their distinctiveness is measured using local or global contrast. Although these approaches have been shown to be effective in dealing with simple images, their limited capability may cause difficulties when dealing with more complicated images. This paper proposes a novel framework for saliency detection by first modeling the background and then separating salient objects from the background. We develop stacked denoising autoencoders with deep learning architectures to model the background where latent patterns are explored and more powerful representations of data are learned in an unsupervised and bottom-up manner. Afterward, we formulate the separation of salient objects from the background as a problem of measuring reconstruction residuals of deep autoencoders. Comprehensive evaluations of three benchmark datasets and comparisons with nine state-of-the-art algorithms demonstrate the superiority of this paper. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of semantic segmentation, where the possible class labels are from a predefined set. We exploit top-down guidance, i.e., the coarse localization of the objects and their class labels provided by object detectors. For each detected bounding box, figure–ground segmentation is performed and the final result is achieved by merging the figure–ground segmentations. The main idea of the proposed approach, which is presented in our preliminary work, is to reformulate the figure–ground segmentation problem as sparse reconstruction pursuing the object mask in a nonparametric manner. The latent segmentation mask should be coherent subject to sparse error caused by intra-category diversity; thus, the object mask is inferred by making use of sparse representations over the training set. To handle local spatial deformations, local patch-level masks are also considered and inferred by sparse representations over the spatially nearby patches. The sparse reconstruction coefficients and the latent mask are alternately optimized by applying the Lasso algorithm and the accelerated proximal gradient method. The proposed formulation results in a convex optimization problem; thus, the global optimal solution is achieved. In this paper, we provide theoretical analysis of the convergence and optimality. We also give an extended numerical analysis of the proposed algorithm and a comprehensive comparison with the related semantic segmentation methods on the challenging PASCAL visual object class object segmentation datasets and the Weizmann horse dataset. The experimental results demonstrate that the proposed algorithm achieves a competitive performance when compared with the state of the arts. [ABSTRACT FROM PUBLISHER]

After deploying a large number of smart meters, utilities are challenged with managing a massive set of interval energy consumption data and decoding the information into meaningful measures that can help them. These new tasks need a more detailed understanding of customers than was previously sought because customers vary widely in their usage, needs, and suitability for different programs and pricing packages. Defining and describing different customer segments will equip decision makers with information to advance not only in pricing and program marketing but also resource allocation and program development. Therefore, among many applications, customer lifestyle segmentation can unlock potential energy-savings and can help utilities understand operating requirements and better coordinate energy resources for grid management. This paper focuses on how to segment customers’ lifestyles based on their electricity consumption data and provides suggestions on which lifestyle groups can be good candidates for certain energy programs based on the segmentation result. [ABSTRACT FROM AUTHOR]

Compared with other applications in computer vision, convolutional neural networks (CNNs) have underperformed on pedestrian detection. A breakthrough was made very recently using sophisticated deep CNN (DCNN) models, with a number of handcrafted features or explicit occlusion handling mechanism. In this paper, we show that by reusing the convolutional feature maps of a DCNN model as image features to train an ensemble of boosted decision models, we are able to achieve the best reported accuracy without using specially designed learning algorithms. We empirically identify and disclose important implementation details. We also show that pixel labeling may be simply combined with a detector to boost the detection performance. By adding complementary handcrafted features such as optical flow, the DCNN-based detector can be further improved. We advance the state-of-the-art results by lowering the log-average miss rate from 11.7% to 8.9% on the Caltech data set and from 11.2% to 8.6% on the Inria data set. We also achieve a comparable result to state-of-the-art approaches on the KITTI data set. [ABSTRACT FROM AUTHOR]

The automatic detection of buried threats in ground penetrating radar (GPR) data is an active area of research due to GPR's ability to detect both metal and nonmetal subsurface objects. Recent work on algorithms designed to distinguish between threats and nonthreats in GPR data has utilized computer vision methods to advance the state-of-the-art detection and discrimination performance. Feature extractors, or descriptors, from the computer vision literature have exhibited excellent performance in representing 2-D GPR image patches and allow for robust classification of threats from nonthreats. This paper aims to perform a broad study of feature extraction methods in order to identify characteristics that lead to improved classification performance under controlled conditions. The results presented in this paper show that gradient-based features, such as the edge histogram descriptor and the scale invariant feature transform, provide the most robust performance across a large and varied data set. These results indicate that various techniques from the computer vision literature can be successfully applied to target detection in GPR data and that more advanced techniques from the computer vision literature may provide further performance improvements. [ABSTRACT FROM PUBLISHER]

Image quality assessment is a critical problem for image compression, which can be utilized as a guidance for image compression and codec evaluation. In this paper, we propose a full reference image quality assessment (IQA) algorithm to measure the perceptual quality of compressed images. The proposed IQA model utilizes a data-driven transform, multi-stage Karhunen-Loeve Transform (MS-KLT), as a feature extractor to decompose both reference and compressed images into feature domain, where the importance of feature distortions in different spectral components to human visual system (HVS) is easy to distinguish. Accordingly, an efficient weighting strategy is proposed to reflect the importance of feature distortions based on the energy of transformed coefficients. Considering HVS characteristics, weighted spatial masking effect is derived from both local and global perspectives. In addition, to avoid influences of random noises, a local adaptive low-pass filtering process is applied as a pre-processing operation. Extensive experimental results on popular datasets show that our proposed method correlates better with the subjective results compared with the state-of-the-art algorithms. Moreover, the proposed method behaves more robustly compared with existing methods, and achieves top-ranking performance on different IQA datasets. [ABSTRACT FROM AUTHOR]

In this paper, we present a framework for performing automatic analysis of Land Use Zones based on Location-Based Social Networks (LBSNs). We model city areas using a hierarchical structure of POIs extracted from foursquare. We encode such structures in kernel machines, e.g., Support Vector Machines, using a new Tree Kernel, i.e., the Hierarchical POI Kernel (HPK), which can take the importance of the individual POIs into account during the substructure matching. This way, HPK projects structures in the space of all their possible substructures such that each dimension corresponds to a semantic structural feature, weighted according to the discriminative power of POIs. We generated four different datasets for the following cities: Barcelona, Lisbon, Amsterdam and Milan, where we trained and tested our models. The results show that our approach largely outperforms previous work and standard baseline built on simple features, such as counts of different POIs. Finally, we apply a mining algorithm to extract the most relevant features (tree fragments) from the implicit TK space according to the weights the kernel machine assigned to them. Our approach can produce an explicit set of representative features that can be used to classify and characterize urban areas. [ABSTRACT FROM AUTHOR]

During the running of electric locomotives, a large amount of operating data will be stored through the Analog-Digital Converter (ADC) and the recording oscillograph of the on-board Traction Control Unit (TCU). Analysis based on mass data to detect slipping is of great significance for saving energy, improving the performance and safety of the traction control system of electric locomotives. Therefore, after analyzing and summarizing the advantages of Empirical Wavelet Transform (EWT) and Fuzzy Entropy (FE) algorithm in data processing and the classification theory of Support Vector Machine (SVM), this paper proposed a SVM slipping detection method based on EWT and FE algorithm. Firstly, feature extraction is carried out on the locomotive wheelset velocity data collected by the on-board velocity sensor, that is, the feature vector of data is extracted by calculating fuzzy entropy value after EWT, and the velocity data is converted into feature matrix for slipping-detection of SVM. Then considering the defects of traditional optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the parameter optimization of SVM, Genetic Particle Swarm Optimization Algorithm (GAPSO) is proposed to optimize the parameters of SVM classification model, and the slipping detection model of SVM is established through training samples, which realizes high-precision slipping detection. Finally, combined with a large number of locomotive data, the comparative analysis test is carried out, the results show that the feature extraction method based on EWT and FE algorithm has excellent ability of mode decomposition and feature extraction, GAPSO has efficient optimization ability, and the proposed slipping detection method can accurately detect slipping. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) clustering is a challenging task due to the complex characteristics in HSI data, such as spatial-spectral structure, high-dimension, and large spectral variability. In this paper, we propose a novel deep spatial-spectral subspace clustering network (DS3C-Net), which explores spatial-spectral information via the multi-scale auto-encoder and collaborative constraint. Considering the structure correlations of HSI, the multi-scale auto-encoder is first designed to extract spatial-spectral features with different-scale pixel blocks which are selected as the inputs. Then, the collaborative constrained self-expressive layers are introduced between the encoder and decoder, to capture the self-expressive subspace structures. By designing a self-expressiveness similarity constraint, the proposed network is trained collaboratively, and the affinity matrices of the feature representation are learned in an end-to-end manner. Based on the affinity matrices, the spectral clustering algorithm is utilized to obtain the final HSI clustering result. Experimental results on three widely used hyperspectral image datasets demonstrate that the proposed method outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Objective: Cervical cancer, as one of the most frequently diagnosed cancers in women, is curable when detected early. However, automated algorithms for cervical pathology precancerous diagnosis are limited. Methods: In this paper, instead of popular patch-wise classification, an end-to-end patch-wise segmentation algorithm is proposed to focus on the spatial structure changes of pathological tissues. Specifically, a triple up-sampling segmentation network (TriUpSegNet) is constructed to aggregate spatial information. Second, a distribution consistency loss (DC-loss) is designed to constrain the model to fit the inter-class relationship of the cervix. Third, the Gauss-like weighted post-processing is employed to reduce patch stitching deviation and noise. Results: The algorithm is evaluated on three challenging and public datasets: 1) MTCHI for cervical precancerous diagnosis, 2) DigestPath for colon cancer, and 3) PAIP for liver cancer. The Dice coefficient is 0.7413 on the MTCHI dataset, which is significantly higher than the published state-of-the-art results. Conclusion: Experiments on the public dataset MTCHI indicate the superiority of the proposed algorithm on cervical pathology precancerous diagnosis. In addition, the experiments on two other pathological datasets, i.e., DigestPath and PAIP, demonstrate the effectiveness and generalization ability of the TriUpSegNet and weighted post-processing on colon and liver cancers. Significance: The end-to-end TriUpSegNet with DC-loss and weighted post-processing leads to improved segmentation in pathology of various cancers. [ABSTRACT FROM AUTHOR]

A human contact network (HCN) consists of individuals moving around and interacting with each other. In HCN, it is essential to detect malicious users who break the data delivery through terminating the data delivery or tampering with the data. Since malicious users will pay more but gain less when breaking the data delivery of opportunistic contacts, we focus on the non-opportunistic contacts that occur more frequently and stably. It is observed that people contact with each other more frequently if they have more social features in common. In this paper, we build up topology structure for HCN based on social features, and propose a graph theoretical comparison detection model to perform malicious users detection. Then we present an adaptive detection scheme based on Hamiltonian cycle decomposition. Also, we define comparison-0-string and comparison-1-string to improve the detection efficiency. Moreover, we perform scenario simulations on real data to realize the detected process of malicious users. Experiments show that, when the number of malicious users is bounded by the dimension of HCN, our scheme has a detection rate of 100% with both false positive rate and false negative rate being 0%, and the running cost is also very low when compared to baseline approaches. When the number of malicious users exceeds the bound, the detection rate of our scheme decreases slowly, while the false positive rate and false negative rate increase slowly, but they are still better than the baseline approaches. [ABSTRACT FROM AUTHOR]

Time–frequency analysis (TFA) is considered as a useful tool to extract the time-variant features of the nonstationary signal. In this paper, a new method called demodulated high-order synchrosqueezing transform (DHST) is proposed. The DHST introduces a two-step algorithm, namely, demodulated transform and high-order synchrosqueezing method to achieve a compact time–frequency representation (TFR) while enabling the reconstruction of the signal from TFR. The performance of the proposed DHST method in this paper is validated by both the simulated and experimental signals including bat echolocation and a vibration signal. The results show that the proposed TFA method is more effective in processing the nonstationary signals with fast varying instantaneous frequency. [ABSTRACT FROM AUTHOR]

This paper proposes a suite of methods to realize high precision pose measurement in 3-D Cartesian space based on a multicamera microscopic vision system. Since it is inefficient to develop a specific image algorithm for each kind of object and the imaging condition might be unsatisfactory, we propose a method of contour primitives of interest extraction, which allows flexible reconfiguration for novel object image and owns robustness under different imaging conditions. The object is detected in grayscale image based on a template of contour primitives. Edges are extracted according to derivatives along the normal vectors of these contour primitives. The positions and directional derivatives of these edges are used for feature extraction and autofocus, respectively. The point features and line features extracted from multiview images are utilized to measure 3-D vectors and orientations, respectively, based on image Jacobian matrices. Cameras’ linear motions are considered in the imaging model, so that the measurement range is expanded beyond the limitation of microscopes’ shallow depths of field. The affine epipolar constraint and focused planes intersection constraint between cameras are applied to improve the real time performances of image feature extraction and multicamera autofocus, respectively. A series of experiments are conducted to verify the effectiveness of the proposed methods. The root mean square errors of pose measurement are evaluated as 3 ${ {\mu } }\text{m}$ in position and 0.05° in orientation, while the measurement range is about 5000 $ {\mu }\text{m}$ in position and 20° in orientation. [ABSTRACT FROM AUTHOR]

Photo aesthetic quality evaluation is a challenging task in artificial intelligence systems. In this paper, we propose a biologically inspired aesthetic descriptor that mimicks humans sequentially perceiving visually/semantically salientref refid="fnote1"/ id="fnote1" asterisk="no"paraIn general, visually salient regions are perceived by low-level visual features, such as the high contrast between the foreground and the background objects; while semantically salient regions are perceived by high-level visual features such as human faces.pararegions in a photo. In particular, a weakly supervised learning paradigm is developed to project the local image descriptors into a low-dimensional semantic space. Then, each graphlet can be described by multiple types of visual features, both in low-level and in high-level. Since humans usually perceive only a few salient regions in a photo, a sparsity-constrained graphlet ranking algorithm is proposed that seamlessly integrates both the low-level and the high-level visual cues. Top-ranked graphlets are those visually/semantically prominent local aesthetic descriptors in a photo. They are sequentially linked into a path that simulates humans actively viewing process. Finally, we learn a probabilistic aesthetic measure based on such actively viewing paths (AVPs) from the training photos. Experimental results show that: 1) the AVPs are 87.65% consistent with real human gaze shifting paths, as verified by the eye-tracking data and 2) our aesthetic measure outperforms many of its competitors. [ABSTRACT FROM AUTHOR]

Vision-based lane detection is a critical component of modern automotive active safety systems. Although a number of robust and accurate lane estimation (LE) algorithms have been proposed, computationally efficient systems that can be realized on embedded platforms have been less explored and addressed. This paper presents a framework that incorporates contextual cues for LE to further enhance the performance in terms of both computational efficiency and accuracy. The proposed context-aware LE framework considers the state of the ego vehicle, its surroundings, and the system-level requirements to adapt and scale the LE process resulting in substantial computational savings. This is accomplished by synergistically fusing data from multiple sensors along with the visual data to define the context around the ego vehicle. The context is then incorporated as an input to the LE process to scale it depending on the contextual requirements. A detailed evaluation of the proposed framework on real-world driving conditions shows that the dynamic and static configuration of the lane detection process results in computation savings as high as 90%, without compromising on the accuracy of LE. [ABSTRACT FROM AUTHOR]

Person attribute recognition aims to identify the attribute labels from the pedestrian images. Extracting contextual relation from the images and attributes, including the spatial-semantic relations, the spatial context and the semantic correlation, is beneficial to enhance the discrimination of the features for recognizing the attributes. Thus, this work proposes a sequence contextual relation learning (SCRL) method to capture these relations. It first embeds the images and attributes into sequences in two branches. Then SCRL flexibly learns the contextual relation from the sequences with the parallel attention model structure, which integrates the inter-attention and intra-attention models. The inter-attention module is utilized to extract the spatial-semantic relations, while the intra-attention is designed to gain the spatial context and the semantic correlation. Both attention modules are comprised of several parallel attention units and each unit can obtain the pairwise relations in one subspace. Therefore, they obtain the relations in multiple subspaces, which can improve the comprehensiveness of the relation learning. Additionally, for the sake of better extraction of spatial-semantic relations, this paper employs connectionist temporal classification (CTC) loss which is capable of driving the network to enforce monotonic alignment between the image and attribute. It can also accelerate the convergence of the network by the algorithm in it. Extensive experiments on five public datasets, i.e., Market-1501 attribute, Duke attribute, PETA, RAP and PA-100K datasets, demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Incipient fault detection and diagnosis (FDD) is an important measure to improve the efficient, safe and stable operation of high-speed trains. This paper proposes a data-driven FDD method, namely deep slow feature analysis and belief rule base method (DSFA-BRB), for the running gears of high-speed trains. The method uses two kinds of statistics to perform fault detection on the multi-dimensional data of the running gears. In addition, the characteristics of more accurate data are extracted, which greatly reduces the complexity of constructing a diagnostic and quantitative model. Further, by constructing a BRB model combining expert knowledge and data, it is possible to avoid misjudgment caused by data incompleteness. Compared with the traditional methods, the DSFA-BRB algorithm has better performance in reducing fault alarm probability. Finally, the validity of the algorithm is verified by the actual running gears system. [ABSTRACT FROM AUTHOR]

In professional/consumer domains, video databases are broadly applied, facilitating quick searching by fast region analysis, to provide an indication of the video contents. For realtime and cost-efficient implementations, it is important to develop algorithms with high accuracy and low computational complexity. In this paper, we analyze the accuracy and computational complexity of newly developed approaches for semantic region labeling and salient region detection, which aim at extracting spatial contextual information from a video. Both algorithms are analyzed by their native DSP computations and memory usage to prove their practical feasibility. In the analyzed semantic region labeling approach, color and texture features are combined with their related vertical image position to label the key regions. In the salient region detection approach, a discrete cosine transform (DCT) is employed, since it provides a compact representation of the signal energy and the computation can be implemented at low cost. The techniques are applied to two complex surveillance use cases, moving ships in a harbor region and moving cars in traffic surveillance videos, to improve scene understanding in surveillance videos. Results show that our spatial contextual information methods quantitatively and qualitatively outperform other approaches with up to 22% gain in accuracy, while operating at several times lower complexity. [ABSTRACT FROM AUTHOR]

Detection of the high impedance fault caused by vegetation is one of the biggest challenges brought by the usage of covered conductors in medium voltage overhead power lines. One of the accompanying events of long-term contact of vegetation with the XLPE insulation is partial discharge which damages the insulation. Current systems for the detection of partial discharge have two major problems: the price and difficult installation. In this paper, an approach for the detection of the partial discharge from the data collected by the antenna is described. This approach is focused on the small computational demand and low false positive rate. Thanks to the small computational requirements, it can be run on the remote gateway devices which are collecting the data from the antenna. It is composed of four steps: outlier detection, outlier clustering, feature extraction, and classification. It is shown that this approach greatly improves the detection rate and lowers false positives compared to the previous algorithm used for partial discharge detection based on the data from an antenna, making it fit to use in the production environment. [ABSTRACT FROM AUTHOR]

In context to high-dimensional clustering, the concept of feature weighting has gained considerable importance over the years to capture the relative degrees of importance of different features in revealing the cluster structure of the dataset. However, the popular techniques in this area either fail to perform feature selection or do not preserve the simplicity of Lloyd’s heuristic to solve the $k$ k -means problem and the like. In this paper, we propose a Lasso Weighted $k$ k -means ($LW$ L W - $k$ k -means) algorithm, as a simple yet efficient sparse clustering procedure for high-dimensional data where the number of features ($p$ p ) can be much higher than the number of observations ($n$ n ). The $LW$ L W - $k$ k -means method imposes an $\ell _1$ ℓ 1 regularization term involving the feature weights directly to induce feature selection in a sparse clustering framework. We develop a simple block-coordinate descent type algorithm with time-complexity resembling that of Lloyd’s method, to optimize the proposed objective. In addition, we establish the strong consistency of the $LW$ L W - $k$ k -means procedure. Such an analysis of the large sample properties is not available for the conventional sparse $k$ k -means algorithms, in general. $LW$ L W - $k$ k -means is tested on a number of synthetic and real-life datasets and through a detailed experimental analysis, we find that the performance of the method is highly competitive against the baselines as well as the state-of-the-art procedures for center-based high-dimensional clustering, not only in terms of clustering accuracy but also with respect to computational time. [ABSTRACT FROM AUTHOR]

Recently, recommender systems are applied to provide personalized recomendation for healthcare wearables. However, due to the sparsity problem, traditional recommendation algorithms are difficult to achieve desired performance. Considering that consumers often buy and rate other types of items on E-commerce platforms, we can leverage significant information in the auxiliary domains to improve the recommendation performance of healthcare wearables, which can be regarded as cross-domain recommendation. However, traditional cross-domain recommendation model cannot fully represent user’s characteristics and fail to consider the leaks of original auxiliary domain ratings during the information transfer process. To overcome the two shortcomings, this paper proposes a Privacy-Preserving Cross-Domain Healthcare Wearables Recommendation algorithm (PPCDHWRec). Firstly, user’s characteristics are divided into domain-dependent features and domain-independent features, which complement each other and fully depict the user’s characteristics. Secondly, inspired by the latent factor model, we factorize the original rating information of each auxiliary domain by Funk-SVD and Orthogonal Nonnegative Matrix Tri-Factorization (ONMTF) model, to obtain user’s domain-dependent and domain-independent features, respectively. Finally, the Factorization Machine algorithm is used to fuse the obtained user’s features with the target domain information to provide the recommendation results. By hiding the item latent factors obtained in the factorization process, PPCDHWRec ensures that the original information cannot be inferred from the transferred user hidden vector. Hence, PPCDHWRec is a privacy-preserving recommendation model. Experiments on two groups of auxiliary domains, having high and low correlations with target domain, show the effectiveness of PPCDHWRec. [ABSTRACT FROM AUTHOR]

Wavelet transform using Haar filter is a fast process for decomposing an image into low and high frequency sub-bands, which is an important step in multi-focus image fusion. This transformation alone is usually not sufficient to produce satisfying fused images, and therefore additional tools like focused region decision map and feature extraction are needed to enhance the fusion quality. In this paper, we propose a multi-focus image fusion algorithm that is suitable for efficient hardware implementation. The algorithm speed is significantly improved by utilizing Haar wavelet and computationally simple fusion rules. To further improve the circuit power and delay, we limit the logic operations to additions, subtractions, and multiplications only. Experiments on different benchmarks demonstrate that our proposed algorithm can reduce the fusion time by up to 99.97% compared with the state-of-the-art competing methods without compromising the fusion quality. In addition, the hardware implementation of the proposed algorithm reduces the circuit area and delay by 79.5% and 92.5%, respectively, when compared to the most competitive algorithm in this paper while maintaining similar fusion quality. [ABSTRACT FROM AUTHOR]

Event recommendation is an essential means to enable people to find attractive upcoming social events, such as party, exhibition, and concert. While growing line of research has focused on suggesting events to individuals, making event recommendation for a group of users has not been well studied. In this paper, we aim to recommend upcoming events for a group of users. We formalize group recommendation as a ranking problem and propose a group event recommendation framework GERF based on learning-to-rank technique. Specifically, we first analyze different contextual influences on user's event attendance, and extract preference of user to event considering each contextual influence. Then, the preference scores of the users in a group are taken as the features for learning-to-rank to model the preference of the group. Moreover, a fast pairwise learning-to-rank algorithm, Bayesian group ranking, is proposed to learn ranking model for each group. Our framework is easily to incorporate additional contextual influences, and can be applied to other group recommendation scenarios. Extensive experiments have been conducted to evaluate the performance of GERF on two real-world datasets and demonstrate the appealing performance of our method on both accuracy and time efficiency. [ABSTRACT FROM AUTHOR]

An improved algorithm with feature selection and neural network classification is proposed in this paper to investigate the fault diagnosis problem of rotating machinery. The feature vectors are constructed by extracting the time- and frequency-domain characteristics of the overall machine under multiple operating conditions. To strengthen the fault diagnostic ability, an improved sensitive feature selection algorithm is proposed by improving the distance evaluation (DE) method and reconstructing a low-dimensional sensitive feature sample with selectively chosen parameters from multidimensional feature vectors. The recursive least square backpropagation (RLS-BP) neural network algorithm is used for fault diagnosis by classifying the feature vectors of normal signal and faulty signals. The effectiveness of the proposed method is verified via hardware experiments using wind turbine drivetrain diagnostics simulator (WTDDS) by comparing with conventional feature vector construction methods and neural network algorithm. [ABSTRACT FROM AUTHOR]

Networks are prevalent and often collected from multiple sources in many high-impact domains, which facilitate many emerging applications that require the connections across multiple networks. Network alignment (i.e., to find the node correspondence between different networks) has become the very first step in many applications and thus has been studied in decades. Although some existing works can use the attribute information as part of the alignment process, they still have certain limitations. For example, some existing network alignment methods can use node attribute similarities as part of the prior alignment information, whereas most of them solely explore the topology consistency without the consistency among attributes of the underlying networks. On the other hand, traditional graph matching methods encode both the node and edge attributes (and possibly the topology) into an affinity matrix and formulate it as a constrained nonconvex quadratic maximization problem. However, these methods cannot scale well to the large-scale networks. In this paper, we propose a family of network alignment algorithms (FINAL) to efficiently align the attributed networks. The key idea is to leverage the node/edge attribute information to guide the (topology-based) alignment process. We formulate this problem as a convex quadratic optimization problem, and develop effective and efficient algorithms to solve it. Moreover, we derive FINAL On-Query, an online variant of FINAL that can find similar nodes for the query nodes across networks. We perform extensive evaluations on real networks to substantiate the superiority of our proposed approaches. [ABSTRACT FROM AUTHOR]

Infrared and visible image fusion (IVIF) expects to obtain images that retain thermal radiation information from infrared images and texture details from visible images. In this paper, a model-based convolutional neural network (CNN) model, referred to as Algorithm Unrolling Image Fusion (AUIF), is proposed to overcome the shortcomings of traditional CNN-based IVIF models. The proposed AUIF model starts with the iterative formulas of two traditional optimization models, which are established to accomplish two-scale decomposition, i.e., separating low-frequency base information and high-frequency detail information from source images. Then the algorithm unrolling is implemented where each iteration is mapped to a CNN layer and each optimization model is transformed into a trainable neural network. Compared with the general network architectures, the proposed framework combines the model-based prior information and is designed more reasonably. After the unrolling operation, our model contains two decomposers (encoders) and an additional reconstructor (decoder). In the training phase, this network is trained to reconstruct the input image. While in the test phase, the base (or detail) decomposed feature maps of infrared/visible images are merged respectively by an extra fusion layer, and then the decoder outputs the fusion image. Qualitative and quantitative comparisons demonstrate the superiority of our model, which can robustly generate fusion images containing highlight targets and legible details, exceeding the state-of-the-art methods. Furthermore, our network has fewer weights and faster speed. [ABSTRACT FROM AUTHOR]

We present a conditional random field approach to tracking-by-detection in which we model pairwise factors linking pairs of detections and their hidden labels, as well as higher order potentials defined in terms of label costs. To the contrary of previous papers, our method considers long-term connectivity between pairs of detections and models similarities as well as dissimilarities between them, based on position, color, and as novelty, visual motion cues. We introduce a set of feature-specific confidence scores, which aim at weighting feature contributions according to their reliability. Pairwise potential parameters are then learned in an unsupervised way from detections or from tracklets. Label costs are defined so as to penalize the complexity of the labeling, based on prior knowledge about the scene like the location of entry/exit zones. Experiments on PETS'09, TUD, CAVIAR, Parking Lot, and Town Center public data sets show the validity of our approach, and similar or better performance than recent state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In a smart house connected to a smart grid via advanced metering infrastructure, a nonintrusive load monitor (NILM) that identifies individual appliances by disaggregating composite electric load signal from the minimal number of sensors installed at the main distribution board in the field can be regarded as a part of a home/building energy management system. This type of load monitoring technique, not only for domestic but also for industrial applications, is relevant to electricity energy management and conservation issues. In this paper, an improved time–frequency analysis-based NILM composed of three system components, including data acquisition, transient feature extraction, and load identification, is proposed. The improved NILM proposed in this paper incorporates a multiresolution S-transform-based transient feature extraction scheme with a modified 0–1 multidimensional knapsack algorithm-based load identification method to identify individual household appliances that may either be energized simultaneously or be identified under similar real power consumption. For the load identification process, an ant colony optimization algorithm is employed to perform combinatorial search that is formulated as a modified 0–1 multidimensional knapsack problem. As shown from the experimental results, the improved NILM strategy proposed in this paper is confirmed to be feasible. [ABSTRACT FROM PUBLISHER]

This paper addresses the key problems of nonstationarity and influence of artifacts in electroencephalogram (EEG)-based brain–computer-interface (BCI) systems. The nonstationary nature of EEG data arises due to the physiological/instrumental differences in intra/intersession of the data generation process. This paper proposes a robust common spatial pattern feature extraction algorithm (RoCSP) to overcome the effects of artifacts and a self-regulated interval type-2 neuro-fuzzy inference system (SRIT2NFIS) to handle this inherent nonstationarity. Combined together, this approach is referred to as RoCSP-SRIT2NFIS. The RoCSP algorithm provides better features than the CSP algorithm by excluding those trials that are affected by the artifacts. SRIT2NFIS uses the features generated by the RoCSP algorithm as input and handles the nonstationarity as an uncertainty using the interval type-2 fuzzy sets in the antecedent of fuzzy rules. A self-regulatory learning mechanism is used to evolve the structure automatically and learn the parameters of the network. A regularized projection-based learning algorithm and a modified rule addition criterion are also proposed to improve the generalization performance of SRIT2NFIS. Using benchmark datasets, performance evaluation has been carried out, and the results indicate that, compared with other existing algorithms, RoCSP-SRIT2NFIS produces higher classification accuracy of 3–5% in simple tasks like left-right classification and 6–8% in complex tasks like foot–tongue classification. In addition, a statistical analysis of the performance results indicates that RoCSP-SRIT2NFIS performs better and is more suitable for an efficient BCI. [ABSTRACT FROM PUBLISHER]

Smart textiles provide an opportunity to simultaneously record various electrophysiological signals, e.g., ECG, from the human body in a non-invasive and continuous manner. Accurate processing of ECG signals recorded using textile sensors is challenging due to the very low signal-to-noise ratio (SNR). Signal processing algorithms that can extract ECG signals out of textile-based electrode recordings, despite low SNR are needed. Presently, there are no textile ECG datasets available to develop, test and validate these algorithms. In this paper we attempted to model textile ECG signals by adding the textile sensor noise to open access ECG signals. We employed the linear predictive coding method to model different features of this noise. By approximating the linear predictive coding residual signals using Kernel Density Estimation, an artificial textile ECG noise signal was generated by filtering the residual signal with the linear predictive coding coefficients. The synthetic textile sensor noise was added to the MIT-BIH Arrhythmia Database (MITDB), thus creating Textile-like ECG dataset consisting of 108 trials (30 min each). Furthermore, a Python code for generating textile-like ECG signals with variable SNR was also made available online. Finally, to provide a benchmark for the performance of R-peak detection algorithms on textile ECG, the five common R-peak detection algorithms: Pan & Tompkins, improved Pan & Tompkins (in Biosppy), Hamilton, Engelse, and Khamis, were tested on textile-like MITDB. This work provides an approach to generating noisy textile ECG signals, and facilitating the development, testing, and evaluation of signal processing algorithms for textile ECGs. [ABSTRACT FROM AUTHOR]

Developmental dysplasia of the hip (DDH) is a common and serious disease in infants. Hip landmark detection plays a critical role in diagnosing the development of neonatal hip in the ultrasound image. However, the local confusion and the regional weakening make this task challenging. To solve these challenges, we explore the stable hip structure and the distinguishable local features to provide dependencies for hip landmark detection. In this paper, we propose a novel architecture named Dependency Mining ResNet (DM-ResNet), which investigates end-to-end dependency mining for more accurate and much faster hip landmark detection. First of all, we convert the landmark detection to the heatmap estimation by ResNet to build a strong baseline architecture for fast and accurate detection. Secondly, a dependency mining module is explored to mine the dependencies and leverage both the local and global information to decline the local confusion and strengthen the weakening region. Thirdly, we propose a simple but effective local voting algorithm (LVA) that seeks trade-off between long-range and short-range dependencies in the hip ultrasound image. Besides, a dataset with 2000 annotated hip ultrasound images is constructed in our work. It is the first public hip ultrasound dataset for open research. Experimental results show that our method achieves excellent precision in hip landmark detection (average point error of 0.719mm and successful detection rate within 1mm of 79.9%). [ABSTRACT FROM AUTHOR]

A large-scale deployment of phasor measurement units (PMUs) that reveal the inherent physical laws of power systems from a data perspective enables an enhanced awareness of power system operation. However, the high-granularity and non-stationary nature of PMU data and imperfect data quality could bring great technical challenges for real-time system event identification. To address these challenges, this paper proposes a two-stage learning-based framework. In the first stage, a Markov transition field (MTF) algorithm is exploited to extract the latent data features by encoding temporal dependency and transition statistics of PMU data in graphs. Then, a spatial pyramid pooling (SPP)-aided convolutional neural network (CNN) is established to efficiently and accurately identify power events. The proposed method fully builds on and is also tested on a large real-world dataset from several tens of PMU sources (and the corresponding event logs), located across the U.S., with a time span of two consecutive years. The numerical results validate that our method has high identification accuracy while showing good robustness against poor data quality. [ABSTRACT FROM AUTHOR]

In this paper, an initial attempt to exploit the benefits of machine learning in solving relay selection for dualhop networks has been made. From data-driven perspective, we convert relay selection to multiclass-classification problem and propose a decision-tree-based selection scheme. Input features are generated by binary quantization to the equivalent channel state information of each relay, so that the dependence of relay selection on maximization algorithm is removed. To optimize quantization parameter, we derive and maximize two splitting criteria—information gain and Gini index. Experimental results show that decision-tree-based scheme achieves comparable performance to optimal selection in terms of average end-to-end rate while reducing computational complexity and feedback amount. [ABSTRACT FROM AUTHOR]

To deal with the registration of infrared and visible image with significant difference in contrast and large geometric distortion in electro-optical pod, a robust and fast registration method by using common structural features is proposed in this paper. First, to adjust the scales of input images, a geometric transformation model is built based on parameters of the cameras. Then, a global structural feature extraction strategy is developed by using phase congruency and significance ranking space. Those features are not only invariant to the scale and contrast but also embody the structural of the input images maximally and uniformly. Finally, to enhance the robustness to contrast, blurring, and incompleteness of structures, an adaptive feature matching method with kernelized correlation filter is presented. Experimental results demonstrate that the proposed method has better performance than several state-of-the-art methods in robustness and precision, and also confirm its validity in the real environment. [ABSTRACT FROM AUTHOR]

Mismatch removal is a key step in many computer vision problems that involve point matching. The existing methods for checking geometric consistency mainly focus on similarity or affine transformations. In this paper, we propose a novel mismatch removal method that can cope with the projective transformation between two corresponding point sets. Our approach is based on the augmented homogeneous coordinates matrix constructed from the coordinates of anchor matches, whose degeneracy can indicate the correctness of anchor matches. The set of anchor matches is initially all the matches and is iteratively updated by calculating the difference between the estimated matched points, which can be easily computed in a closed form, and the actually matched points and removing those with large differences. Experimental results on synthetic 2D point matching data sets and real image matching data sets verify that our method achieves the highest $F$ -score among all the methods under similarity, affine, and projective transformations with noises and outliers. Our method can also achieve faster speed than all other iterative methods. Those non-iterative methods with slight advantage in speed are not competitive in accuracy when compared with ours. We also show that the set of anchor matches is stable through the iteration and the computation time grows very slowly with respect to the number of matched points. When applied to mismatch removal in partial-duplicate image search, our method achieves the best retrieval precision, and its computing time is also highly competitive. [ABSTRACT FROM AUTHOR]

One of the most popular feature extraction algorithms for brain–computer interfaces (BCI) is common spatial patterns (CSPs). Despite its known efficiency and widespread use, CSP is also known to be very sensitive to noise and prone to overfitting. To address this issue, it has been recently proposed to regularize CSP. In this paper, we present a simple and unifying theoretical framework to design such a regularized CSP (RCSP). We then present a review of existing RCSP algorithms and describe how to cast them in this framework. We also propose four new RCSP algorithms. Finally, we compare the performances of 11 different RCSP (including the four new ones and the original CSP), on electroencephalography data from 17 subjects, from BCI competition datasets. Results showed that the best RCSP methods can outperform CSP by nearly 10% in median classification accuracy and lead to more neurophysiologically relevant spatial filters. They also enable us to perform efficient subject-to-subject transfer. Overall, the best RCSP algorithms were CSP with Tikhonov regularization and weighted Tikhonov regularization, both proposed in this paper. [ABSTRACT FROM AUTHOR]

Automatic color classification for solar cells is challenging because of the tiny color difference and low contrast. To address this problem, a color feature selection and classification frame is proposed in this paper. First, an intuitive multi-color space feature performance evaluation scheme is presented to select the optimal color subspaces that help to enormously enlarge the tiny color difference of solar cell images. And the optimal color subspaces can also be illustrated by employing multi-color space visualization method with combinational Mosaic images. Second, a nine-dimensional feature vector consisting of mean, variance, and skewness in the three optimal subspaces is extracted by utilizing the serial fusion technique. Third, an improved Gaussian mixture model in supervised manner for color classification is proposed by employing a k-means method based on adjacent rules, which helps to eliminate isolated points and enhances the classification performance. Finally, experimental results show that the overall performance of the proposed method achieves 97.9%, and outperform other algorithms especially for the tiny color difference of solar cell images. [ABSTRACT FROM AUTHOR]

We compile baselines, along with dataset split, for multimodal sentiment analysis. In this paper, we explore three different deep-learning-based architectures for multimodal sentiment classification, each improving upon the previous. Further, we evaluate these architectures with multiple datasets with fixed train/test partition. We also discuss some major issues, frequently ignored in multimodal sentiment analysis research, e.g., the role of speaker-exclusive models, the importance of different modalities, and generalizability. This framework illustrates the different facets of analysis to be considered while performing multimodal sentiment analysis and, hence, serves as a new benchmark for future research in this emerging field. [ABSTRACT FROM AUTHOR]