Search

• A new method for non-blind correlated noise removal. • Convolutional neural network based. • Fully automatic once the autocovariance function of the noise and the noisy image are given. • Performs better than several methods which in addition may require careful parameter tuning. In this paper, removal of additive, signal-independent, correlated noise from images is considered. We consider the non-blind case, meaning that the stationary autocovariance function of the noise is assumed to be known. The denoising method is based on unrolled optimization where in the half-quadratic energy minimization each proximal step is replaced by a learnt convolutional neural network. The proximal steps take place in learnt transform domains. Functions producing the regularization parameters are also learnt. We assume that we have a distribution for autocovariance functions in order to be able to draw samples. For simplicity, we assume that the noise has low-pass spectral character and a typical autocovariance function has a relatively simple form. The experimental results demonstrate that in terms of PSNR values, the method performs better than two classical methods and a method based on a learnt patch prior. [ABSTRACT FROM AUTHOR]

This paper introduces a novel video saliency model for salient object detection in videos. Firstly, we generate multi-level deep features via a symmetrical convolutional neural network, in which the inputs are the current frame and the optical flow image. Then, the multi-level deep features are integrated in a hierarchical manner using a fusion network, which deploys attention module to make a selection for deep features. Lastly, the integrated deep feature is combined with the boundary information originated from shallow layer of the feature extraction networks, and the saliency map is generated by the saliency prediction step. The key advantages of our model lie on the attention module, the hierarchical integration and the boundary information, in which the former one acts as weight filter and is used to select the most salient regions in deep features, the middle one gives an effective integration manner for features from different layers and the last one provides well-defined boundaries for saliency map. Extensive experiments are performed on two challenging video dataset, and the experimental results show that our model consistently outperforms the state-of-the-art saliency models in a large margin. [ABSTRACT FROM AUTHOR]

• A novel top-down framework is presented to address multi-person pose estimation. • We improved the faster R-CNN detector by infusing the soft-NMS and ASDN items. • Heatmaps instead of activation map are used to pinpoint the precise joints location. Human motion recognition based on computer vision plays an important role in many fields, such as video surveillance, virtual reality, and medical care. To solve the inaccurate multi-person pose estimation problem and improve the generalizability of the extracted features, this paper proposes a multi-person pose estimation method based on a deep convolutional neural network. This method mainly relies on a top-down structure which includes two stages. In the first stage, the bounding boxes that are likely to contain people are first detected by an improved faster R-CNN. Individuals in the complex scenario are then tailored by box cropping. In the second stage, we combine heatmap detection with coordinate regression to address the single person pose estimation problem. Specially, a deep convolutional ResNet is employed to produce heatmaps of human body. The precise location of each joint is achieved by the fully connected conditional random field. Experimental results demonstrate our method achieves comparable performance with the state-of-the-art ones. [ABSTRACT FROM AUTHOR]

Deep learning has been widely applied in image processing and computer vision due to its powerful learning capability. Although some learning models have been proposed to suppress noise in images, most of them are developed for Gaussian noise and few are for impulse noise. This paper proposes an image recovery method based on deep convolutional neural networks for impulse noise removal. The proposed framework falls into two components: a classifier network which divides image pixels into noisy and noise-free, and a regression network which is trained for image reconstruction. In the regression network, the noise-free pixels identified by the classifier network together with the original noisy image are used for recovery of the noisy image. Furthermore, batch normalization is embedded to the network to improve denoising performance. Experimental results show that the proposed method can excellently remove impulse noise, providing clear performance improvements over other state-of-the-art denoising methods. [ABSTRACT FROM AUTHOR]

• We present a novel discriminative model to learn similarity measurement between source domain and target domain. • We freeze part of the network layers to extract well-defined representations from source domain to target domain. • We use auxiliary images labelled by perspective-class to improve categorization performance. Fine-grained categorization is challenging due to its small inter-class and large intra-class variance. Moreover, requiring domain expertise makes fine-grained labelled data much more expensive to acquire. Existing models predominantly require extra information such as bounding box and part annotation in addition to the image category labels, which involves heavy manual labor. In this paper, we propose a novel hierarchical deep transfer learning model, based on a compression convolutional neural network. Our model transfers the learned image representation from large-scale labelled fine-grained datasets to micro fine-grained datasets, which avoids using expensive annotations and realizes visual categorization task effectively. Firstly, we introduce a cohesion domain to measure correlation degree between source domain and target domain. Secondly, the source-domain convolutional neural network is adjusted according to its metrical feedback, in order to select task-specific features that are suitable for transferring to the target domain. Finally, we make most of perspective-class labels, which are inherent attributes of fine-grained data for multi-task learning and learn all the attributes through joint learning to extract more discriminative representations. The proposed model not only economizes training time effectively and achieves high categorization accuracy, but also verifies that the inter-domain feature transition can accelerate learning and optimization. [ABSTRACT FROM AUTHOR]

• Radial feature descriptors improve cell classification and image recommendation. • Textural variations along cellular extra-nuclear narrow band describe cytoplasm. • Hybrid method contains unsupervised and supervised real-time machine learning. • Nuclei segmentation based on hierarchical clustering handles cell clumps and overlap. • Random Forest for classification and CNN for recommendation improve cell analytics. This paper introduces computational tools for cell classification into normal and abnormal, as well as content-based-image-retrieval (CBIR) for cell recommendation. It also proposes the radial feature descriptors (RFD), which define evenly interspaced segments around the nucleus, and proportional to the convexity of the nuclear boundary. Experiments consider Herlev and CRIC image databases as input to classification via Random Forest and bootstrap; we compare 14 different feature sets by means of False Negative Rate (FNR) and Kappa (κ), obtaining FNR = 0.02 and κ = 0.89 for Herlev, and FNR = 0.14 and κ = 0.78 for CRIC. Next, we sort and rank cell images using convolutional neural networks and evaluate performance with the Mean Average Precision (MAP), achieving MAP = 0.84 and MAP = 0.82 for Herlev and CRIC, respectively. Cell classification show encouraging results regarding RFD, including its sensitivity to intensity variation around the nuclear membrane as it bypasses cytoplasm segmentation. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A powerful network called Asymmetric Filtering-based Dense Convolutional Neural Network (AF D-CNN) addresses person re-identification problem. • Joint Bayesian and re-ranking techniques are used to obtain the ranking lists which do not need dimensionality reduction. • Extensive experiments conducted on several datasets show that the proposed method achieves the state-of-the-art performance. Abstract Person re-identification aims at matching individuals across multiple camera views under surveillance systems. The major challenges lie in the lack of spatial and temporal cues, which makes it difficult to cope with large variations of lighting conditions, viewing angles, body poses and occlusions. How to extract multimodal features including facial features, physical features, behavioral features, color features, etc is still a fundamental problem in person re-identification. In this paper, we propose a novel Convolutional Neural Network, called Asymmetric Filtering-based Dense Convolutional Neural Network (AF D-CNN) to learn powerful features, which can extract different levels' features and take advantage of identity information. Moreover, instead of using typical metric learning methods, we obtain the ranking lists by merging Joint Bayesian and re-ranking techniques which do not need dimensionality reduction. Finally, extensive experiments show that our proposed architecture performs well on four popular benchmark datasets (CUHK01, CUHK03, Market-1501, DukeMTMC-reID). [ABSTRACT FROM AUTHOR]

Highlights • An edge detection network architecture based on deep convolutional network is presented. • A novel loss function is proposed to balance positive and negative samples. • The fast learning model is discovered (only 5.4k iteration) for edge detection. • The better generalization ability is verified at different dataset. Abstract In this paper, we propose an edge detector based on feature re-extraction (FRE) of a deep convolutional neural network to effectively utilize features extracted from each stage, and design a new loss function. The proposed detector is mainly composed of three modules: backbone, side-output, and feature fusion. The backbone module provides preliminary feature extraction; the side-output module makes network architecture more robustly map features from different stages of the backbone network to edge-pixel space by applying residual learning, and the feature fusion module generates the edge map. Generalization ability on the same distribution is verified using the BSDS500 dataset, achieving optimal dataset scale (ODS) F-score = 0.804. Cross-distribution generalization ability is verified on the NYUDv2 dataset, achieving ODS F-score = 0.701. In addition, we find that freezing backbone network can significantly speed up training process, without much overall accuracy loss (ODS F-score of 0.791 after 5.4k iterations). [ABSTRACT FROM AUTHOR]

Neural network pruning techniques can be effective in accelerating neural network models, making it possible to deploy them on edge devices. In this paper, we propose to prune neural networks using data variance. Unlike other existing methods, this method is somewhat robust and does not invalidate our criteria depending on the number of data batches and the number of training sessions. We also propose a pruning compensation technique. This technique fuses the pruned convolutional information into the remaining convolutional kernel close to it. This fusion operation can effectively help retain the pruned information. We evaluate the proposed method on a number of standard datasets and compare it with several current state-of-the-art methods. Our method always achieves better performance. For example, on Tiny ImageNet, our method can prune 54.2% FLOPs of ResNet50 while obtaining a 0.22% accuracy improvement. • A data-driven network pruning method with high robustness is proposed. • The proposed data-driven pruning method consumes less resources and is highly efficient. • A pruning compensation technique is proposed to reduce the negative impact of pruning. • The effectiveness of the proposed method is validated on several datasets. [ABSTRACT FROM AUTHOR]

• AMSFF-Net has ability to pay more attention to the informative features at different multi-stream resolution levels using pixel attention mechanism. • AMSFF-Net has ability to capture semantic information and sharp textural details from the extracted features and retain high-quality image from coarse-to-fine using mixed-convolution attention mechanism. • A sharp image can be recovered by the good kernel estimation. Mixed convolution attention mechanism highlights the task-relevant features and retain the dehazed images with sharp texture details and more vibrant colors. • Deep semantic loss highlights the boundary information and the spatial correlation in the deep features. In this paper, an end-to-end convolutional neural network is proposed to recover haze-free image named as Attention-Based Multi-Stream Feature Fusion Network (AMSFF-Net). The encoder-decoder network structure is used to construct the network. An encoder generates features at three resolution levels. The multi-stream features are extracted using residual dense blocks and fused by feature fusion blocks. AMSFF-Net has ability to pay more attention to informative features at different resolution levels using pixel attention mechanism. A sharp image can be recovered by the good kernel estimation. Further, AMSFF-Net has ability to capture semantic and sharp textural details from the extracted features and retain high-quality image from coarse-to-fine using mixed-convolution attention mechanism at decoder. The skip connections decrease the loss of image details from the larger receptive fields. Moreover, deep semantic loss function emphasizes more semantic information in deep features. Experimental findings prove that the proposed method outperforms in synthetic and real-world images. [ABSTRACT FROM AUTHOR]

Image denoising requires both spatial details and global contextualized information to recover a clean version from the deteriorative one. Previous deep convolution networks usually focus on modeling the local feature and stacked convolution blocks to expand the receptive field, which can catch the long-distance dependencies. However, contrary to the expectation, the extracted local feature incapacity recovers the global details by traditional convolution while the stacked blocks hinder the information flow. To tackle these issues, we introduce the M atrix Factorization D enoising Module (MD) to model the interrelationship between the global context aggregating process and the reconstructed process to attain the context details. Besides, we redesign a new basic block to ease the information flow and maintain the network performance. In addition, we conceive the F eature Fu sion Module (FFU) to fuse the information from the different sources. Inspired by the multi-stage progressive restoration architecture, we adopt two-stage convolution branches progressively reconstructing the denoised image. In this paper, we propose an original and efficient neural convolution network dubbed MFU. Experimental results on various image denoising datasets: SIDD, DND, and synthetic Gaussian noise datasets show that our MFU can produce comparable visual quality and accuracy results with state-of-the-art methods. • We propose MFU, an effective and efficient end-to-end multi-stage progressive convolution network with a new basic block for image denoising. • We represent the global structured information as the low-rank matrix and use the MD module to learn the contextually-enriched and globally structured feature. • Designing a module (FFU) fuses the multi-source information to promote the network reconstruct the image. [ABSTRACT FROM AUTHOR]

Image steganalysis based on convolutional neural networks(CNN) has attracted great attention. However, existing networks lack attention to regional features with complex texture, which makes the ability of discrimination learning miss in network. In this paper, we described a new CNN designed to focus on useful features and improve detection accuracy for spatial-domain steganalysis. The proposed model consists of three modules: noise extraction module, noise analysis module and classification module. A channel attention mechanism is used in the noise extraction module and analysis module, which is realized by embedding the SE(Squeeze-and-Excitation) module into the residual block. Then, we use convolutional pooling instead of average pooling to aggregate features. The experimental results show that detection accuracy of the proposed model is significantly better than those of the existing models such as SRNet, Zhu-Net and GBRAS-Net. Compared with these models, our model has better generalization ability, which is critical for practical application. • An end-to-end spatial steganalysis network CCNet is proposed. • Channel attention mechanism is introduced to focus on the region suitable for embedding secret information. • Convolution is used instead of average pooling to avoid the loss of steganographic information. • The proposed model is verified to have higher accuracy through comparative experiments. [ABSTRACT FROM AUTHOR]

Person re-identification(ReID) is an intelligent video surveillance technology that retrieves the same person from different cameras. This task is extremely challenging due to changes in person poses, different camera views, and occlusion. In recent years, person ReID based on deep learning technology has received widespread attention due to the rapid development and excellent performance of deep learning. In this paper, we first divide person ReID based on deep learning approaches into seven types, i.e., fused hand-crafted features deep model, representation learning model, metric learning model, part-based deep model, video-based model, GAN-based model, unsupervised model. Furthermore, we launched a brief overview of the seven types. Then, we introduce some examples of commonly used datasets, compare the performance of some algorithms on image and video datasets in recent years, and analyze the advantages and disadvantages of various methods. Finally, we summarize the possible future research directions of person ReID technology. • Dividing person ReID based on deep learning approaches into seven types. • Introducing some examples of commonly used datasets. • Comparing the performance of some algorithms on image and video datasets in recent years. • Analyzing the advantages and disadvantages of various methods. • Summarizing the possible future research directions of person ReID technology. [ABSTRACT FROM AUTHOR]

• An improved ASM architecture to extract handcrafted and deep features for AIFR. • ASM-CNN is a superior accessible solution for Rank-1 recognition. • It provides stronger performance as a classifier with ASM-CNN over CNN. Scientific efforts have expanded in age-invariant face recognition (AIFR). Matching faces of large age difference is, therefore, a problem, mostly because of a substantial disparity in the appearance of both young and old age. Owing to age, both the appearance and shape of the face are impaired, making recognition of the face the most challenging task. In recent years, AIFR has become a very common and demanding task. The set of feature extraction and classification algorithm is of prime importance in this field. As the numbers of features obtained from the datasets are large, there is a need to introduce a dimensionality reduction method to map high dimensionality feature space to low variance filter to form the final integrated face age model to be used in the classification process. In this paper, we introduced a novel concept of an improved Active Shape Model (ASM) in conjunction with a specially designed 7-layered Convolutional Neural Network (CNN) in order to accomplish a combination of feature extraction and classification in a single unit. The study approach involves conducting extensive experiments to evaluate the proposed system's performance using three standard datasets: FG-NET, LAG, and CACD. The results reveal that the proposed method outperforms state-of-the-art approaches and achieves excellent accuracy in face recognition across age. The maximum accuracies achieved by demonstrated ASM-CNN methodology for FG-NET, LAG, and CACD databases are 95.02%, 91.76 % and 99.4 % respectively. [ABSTRACT FROM AUTHOR]

• We propose spectral pyramid transform to model actions sufficiently. • We design a sampling strategy to make actions with various duration aligned. • We propose a feature pyramid to fuse CNN features and spectral features. In this paper, we propose a novel Deep Spectral Feature Pyramid in the Frequency domain (DSFP) to share the merits of deep features and spectral approaches for long-term action recognition. More specifically, in the spatial domain, deep features of sparse sampled frames are extracted by Convolutional Neural Networks (CNNs) to cover long-term temporal structure. In the frequency domain, appearance features of sampled frames are partitioned recursively along the time dimension and spectral transform is applied to each partitioned feature respectively. All coefficients of partitioned features are then concatenated into a video-level feature to better model the spatio-temporal structure of actions in the form of a pyramid. So DSFP could model actions from both microcosmic and macroscopic aspects. Extensive experiments conducted on two challenging action benchmarks UCF101 and HMDB51 show that our proposed DSFP is effective for spatio-temporal representation of actions and achieves comparable performance with the state-of-the-arts. [ABSTRACT FROM AUTHOR]