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High-frequency surface wave radar (HFSWR) has become the cornerstone of maritime surveillance because of its low-cost maintenance and coverage of wide area. However, when it comes to the extraction of key areas, such as vessel-target detection and vessel-path tracking, the HFSWR signal is strongly interfered by clutters and noise, which makes maritime surveillance a challenging task. This article proposes a hierarchical key-area perception model for maritime surveillance harnessing range-Doppler (RD) image from HFSWR, Laplacian kernel, a linear classifier (LC), and a subnet-based multilayer representation learning framework (SMRLF). First, a weak LC with a Laplacian kernel is utilized to capture the plausible vessel regions (PVRs). Then, a novel SMRLF is proposed to localize the vessel targets from the PVRs. To handle the noise, a maximum correntropy criterion with variable centers (MCC-VC) is incorporated in the subnet-based learning model. A thorough experimental analysis on cross-domain samples from radar dataset to scene classification dataset shows that the proposed HKPM performs competitively. The model shows a superior performance over most of the state-of-the-art vessel-target detection algorithms with a vessel-target detection accuracy of 94%. The extended analysis on image classification problem proves that the proposed model has great adaptivity and scalability. [ABSTRACT FROM AUTHOR]

Gradient descent optimization of learning has become a paradigm for training deep convolutional neural networks (DCNN). However, utilizing other learning strategies in the training process of the DCNN has rarely been explored by the deep learning (DL) community. This serves as the motivation to introduce a non-iterative learning strategy to retrain neurons at the top dense or fully connected (FC) layers of DCNN, resulting in, higher performance. The proposed method exploits the Moore-Penrose Inverse to pull back the current residual error to each FC layer, generating well-generalized features. Further, the weights of each FC layers are recomputed according to the Moore-Penrose Inverse. We evaluate the proposed approach on six most widely accepted object recognition benchmark datasets: Scene-15, CIFAR-10, CIFAR-100, SUN-397, Places365, and ImageNet. The experimental results show that the proposed method obtains improvements over 30 state-of-the-art methods. Interestingly, it also indicates that any DCNN with the proposed method can provide better performance than the same network with its original Backpropagation (BP)-based training. [ABSTRACT FROM AUTHOR]

Machine learning algorithms such as those for object classification in images, video content analysis, and human action recognition are used to extract meaningful information from data recorded by image sensors and cameras. Among the existing machine learning algorithms for such purposes, extreme learning machines (ELMs) and online sequential ELMs (OS-ELMs) are well known for their computational efficiency and performance when processing large datasets. The latter approach was derived from the ELM approach and optimized for real-time application. However, OS-ELM classifiers are computationally demanding, and the existing state-of-the-art computing platforms are not efficient enough for embedded systems, especially for applications with strict requirements in terms of low power consumption, high throughput, and low latency. This paper presents the implementation of an ELM/OS-ELM in a customized system-on-a-chip field-programmable gate array-based architecture to ensure efficient hardware acceleration. The acceleration process comprises parallel extraction, deep pipelining, and efficient shared memory communication. [ABSTRACT FROM AUTHOR]

The automatic detection of foreground (FG) objects in videos is a demanding area of computer vision, with essential applications in video-based traffic analysis and surveillance. New solutions have attempted exploiting deep neural network (DNN) for this purpose. In DNN, learning agents, i.e., features for video FG object segmentation is nontrivial, unlike image segmentation. It is a temporally processed decision-making problem, where the agents involved are the spatial and temporal correlations of the FG objects and the background (BG) of the scene. To handle this and to overcome the conventional DL models’ poor delineation at the borders of FG regions due to fixed-view receptive filed-based learning, this work introduces a Multi-view Receptive Field Encoder-Decoder Convolutional Neural Network called MvRF-CNN. The main contribution of the model is harnessing multiple views of convolutional (conv) kernels with residual feature fusions at early, mid and late stages in an encoder-decoder (EnDec) architecture. It enhances the ability of the model to learn condition-invariant agents resulting in highly delineated FG masks when compared to the existing approaches from heuristic- to DL-based techniques. The model is trained with sequence-specific labeled samples to predict scene-specific pixel-level labels of FG objects in near static scenes with a minute dynamism. The experimental study on 37 video sequences from traffic and surveillance scenarios that include complex environments, viz. dynamic background, camera jittery, intermittent object motion, scenes with cast shadows, night videos, and lousy weather proves the effectiveness of the model. The study covers two input configurations: a 3-channel (RGB) single frame and a 3-channel double-frame with a BG such that two consecutive grayscale frames stacked with a prior BG model. The ablation investigations are also conducted to show the importance of transfer learning (TL) and mid-fusion approaches for enhancing the segmentation performance and the model's robustness on failure modes: when there is lack of manually annotated hard ground truths (HGT) and testing the model under non-scene-specific videos. In overall, the model achieves a figure-of-merit of ${95\%}$ and 42 $FPS$ of mean average performance. [ABSTRACT FROM AUTHOR]

As one of fundamental texture classification methods, LBP-based descriptors have attracted considerable attention due to the efficiency, simplicity, and high performance. However, most of binary pattern methods cannot effectively capture the texture information with scale changes. Inspired by this, this paper proposes a multi-scale threshold integration encoding strategy for texture classification. The essence of this strategy is to introduce the multi-scale local texture information in the view of thresholding. Based on this, we propose the local multi-scale center pattern, local multi-scale sign pattern, and local multi-scale magnitude pattern to extract and describe the multi-scale local texture information. Then, the three sub-patterns are jointly combined to generate the final descriptor for texture classification tasks. The experimental results on three popular texture databases significantly demonstrate that the proposed texture descriptor is very discriminative and powerful for visual texture classification tasks. [ABSTRACT FROM AUTHOR]

The extreme learning machine (ELM), which was originally proposed for “generalized” single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of regression and classification. Although, it provides promising performance and robustness and has been used for various applications, the single-layer architecture possibly lacks the effectiveness when applied for natural signals. In order to over come this shortcoming, the following work indicates a new architecture based on multilayer network framework. The significant contribution of this paper are as follows: 1) unlike existing multilayer ELM, in which hidden nodes are obtained randomly, in this paper all hidden layers with invertible functions are calculated by pulling the network output back and putting it into hidden layers. Thus, the feature learning is enriched by additional information, which results in better performance; 2) in contrast to the existing multilayer network methods, which are usually efficient for classification applications, the proposed architecture is implemented for dimension reduction and image reconstruction; and 3) unlike other iterative learning-based deep networks (DL), the hidden layers of the proposed method are obtained via four steps. Therefore, it has much better learning efficiency than DL. Experimental results on 33 datasets indicate that, in comparison to the other existing dimension reduction techniques, the proposed method performs competitively better with fast training speeds. [ABSTRACT FROM AUTHOR]

The finite Gaussian mixture model with kernel correlation is a flexible tool that has recently received attention for point set registration. While there are many algorithms for point set registration presented in the literature, an important issue arising from these studies concerns the mapping of data with nonlinear relationships and the ability to select a suitable kernel. Kernel selection is crucial for effective point set registration. We focus here on multiple kernel point set registration. We make several contributions in this paper. First, each observation is modeled using the Student's t-distribution, which is heavily tailed and more robust than the Gaussian distribution. Second, by automatically adjusting the kernel weights, the proposed method allows us to prune the ineffective kernels. This makes the choice of kernels less crucial. After parameter learning, the kernel saliencies of the irrelevant kernels go to zero. Thus, the choice of kernels is less crucial and it is easy to include other kinds of kernels. Finally, we show empirically that our model outperforms state-of-the-art methods recently proposed in the literature. [ABSTRACT FROM AUTHOR]

In this paper, a novel biometric inspired multimedia encryption technique is proposed. For this purpose, a new advent in the definition of fractional Fourier transform, namely, dual parameter fractional Fourier transform (DP-FrFT) is proposed and used in multimedia encryption. The core idea behind the proposed encryption technique is to obtain biometrically encoded bitstream followed by the generation of the keys used in the encryption process. Since the key generation process of encryption technique directly determines the security of the technique. Therefore, this paper proposes an efficient method for generating biometrically encoded bitstream from biometrics and its usage to generate the keys. Then, the encryption of multimedia data is done in the DP-FrFT domain with the help of Hessenberg decomposition and nonlinear chaotic map. Finally, a reliable decryption process is proposed to construct original multimedia data from the encrypted data. Theoretical analyses and computer simulations both confirm high security and efficiency of the proposed encryption technique. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) data are often affected by speckle noise, which originates in the SAR system's coherent nature. In this paper, we introduce a simple and effective algorithm to make the traditional Student's t-mixture model (SMM) more robust to noise. The proposed new modified SMM (MSMM) is applied for SAR image segmentation. SMM has come to be regarded as an alternative to the Gaussian mixture model (GMM) as it is heavy tailed and more robust to outliers. However, a major shortcoming of this method is that it does not take into account the spatial dependencies in the image. Although some existing methods incorporate the spatial relationship between neighboring pixels, they are still not robust enough to noise. The advantages of our method are as follows. First, we introduce MSMM to incorporate the local spatial information and pixel intensity value by considering the conditional probability of an image pixel influenced by the probabilities of pixels in its immediate neighborhood. Furthermore, we introduce the additional parameter α to control the extent of this influence. The larger α indicates the heavier extent of influence in the neighborhoods. Second, the prior probability of an image pixel is influenced by the probabilities of pixels in its immediate neighborhood, which incorporates local spatial and component information. Third, our model is based on the finite mixture model (FMM); it is simple and easy to implement, and the expectation maximization algorithm can be applied for estimation of optimal parameters. Finally, the traditional SMM can be considered as a special case of our model. Thus, our method is general enough for FMM-based techniques. Experimental results on both simulated and real SAR images demonstrate the improved robustness and effectiveness of our approach. [ABSTRACT FROM PUBLISHER]

This paper aims toward improving background suppression from video frames by incorporating multiresolution features in Gaussian mixture model (GMM). GMM has proven its place for background modeling due to its better applicability and robustness compared with other popular methods in literature. However, GMM fails in a number of situations such as noisy and non-stationary background, slow foregrounds, and illumination variation. Extensions to GMM have also been proposed to increase accuracy in expense of increased complexity, decrease in execution speed, and reduced applicability. In view of the above, this paper aims to provide a methodology to assimilate useful multiresolution features with GMM that considerably improves the performance. The contributions of this paper are: 1) a novel framework to incorporate wavelet subbands in GMM to improve its performance; 2) an approach to incorporate variable number of clusters in the aforesaid framework; and 3) a generic platform to use any multiresolution decomposition based GMM for background suppression. Extensive experimentations on several video sequences are performed to verify the improvement in accuracy compared with conventional GMM as well as a number of state-of-the-arts approaches. Along with qualitative and quantitative analysis, justification on the use of multiresolution is provided for clarification. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel algorithm for feature selection and model detection using Student's t-distribution based on the variational Bayesian (VB) approach. First, our method is based on the Student's t-mixture model (SMM) which has heavier tail than the Gaussian distribution and is therefore less sensitive to small numbers of data points and consequent precision-estimates of the components number. Second, the number of components, the local feature saliency and the parameters of the mixture model are simultaneously estimated by Bayesian variational learning. Experimental results using synthetic and real data demonstrate the improved robustness of our approach. [ABSTRACT FROM AUTHOR]

In this paper, a new mixture model for image segmentation is presented. We propose a new way to incorporate spatial information between neighboring pixels into the Gaussian mixture model based on Markov random field (MRF). In comparison to other mixture models that are complex and computationally expensive, the proposed method is fast and easy to implement. In mixture models based on MRF, the M-step of the expectation-maximization (EM) algorithm cannot be directly applied to the prior distribution \piij for maximization of the log-likelihood with respect to the corresponding parameters. Compared with these models, our proposed method directly applies the EM algorithm to optimize the parameters, which makes it much simpler. Experimental results obtained by employing the proposed method on many synthetic and real-world grayscale and colored images demonstrate its robustness, accuracy, and effectiveness, compared with other mixture models. [ABSTRACT FROM PUBLISHER]

In this paper, a security solution during communication and transmission of fingerprint data is proposed in the form of a novel encryption technique based on reversible hidden transform (RHT) and fractional wavelet packet transform (FrWPT). The core idea of the proposed technique is to change the gray values in the spatial domain using RHT followed by the deformation of FrWPT coefficients by singular value decomposition and chaotic map. Hence, security solution relies on both spatial and frequency domains. Finally, a reliable decryption scheme is also presented to reconstruct the original fingerprint image from the encrypted image. Experimental results and security analysis demonstrate the efficiency and robustness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, we present a new algorithm for pixel labeling and image segmentation based on the standard Gaussian mixture model (GMM). Unlike the standard GMM where pixels themselves are considered independent of each other and the spatial relationship between neighboring pixels is not taken into account, the proposed method incorporates this spatial relationship into the standard GMM. Moreover, the proposed model requires fewer parameters compared with the models based on Markov random fields. In order to estimate model parameters from observations, instead of utilizing an expectation–maximization algorithm, we employ gradient method to minimize a higher bound on the data negative log-likelihood. The performance of the proposed model is compared with methods based on both standard GMM and Markov random fields, demonstrating the robustness, accuracy, and effectiveness of our method. [ABSTRACT FROM AUTHOR]

In this paper, a novel image fusion algorithm based on framelet transform is presented. The core idea is to decompose all the images to be fused into low and high-frequency bands using framelet transform. For fusion, two different selection strategies are developed and used for low and high-frequency bands. The first strategy is adaptive weighted average based on local energy and is applied to fuse the low-frequency bands. In order to fuse high-frequency bands, a new strategy is developed based on texture while exploiting the human visual system characteristics, which can preserve more details in source images and further improve the quality of fused image. Experimental results demonstrate the efficiency and better performance than existing image fusion methods both in visual inspection and objective evaluation criteria. [ABSTRACT FROM AUTHOR]

In this correspondence paper, the wavelet transform, which is an important tool in signal and image processing, has been generalized by coalescing wavelet transform and fractional random transform. The new transform, i.e., fractional random wavelet transform (FrRnWT) inherits the excellent mathematical properties of wavelet transform and fractional random transform. Possible applications of the proposed transform are in biometrics, image compression, image transmission, transient signal processing, etc. In this correspondence paper, biometrics is chosen as the primary application; and hence, a new technique is proposed for securing fingerprints during communication and transmission over insecure channel. [ABSTRACT FROM PUBLISHER]

Finite mixture model based on the Student's-t distribution, which is heavily tailed and more robust than Gaussian, has recently received great attention for image segmentation. A new finite Student's-t mixture model (SMM) is proposed in this paper. Existing models do not explicitly incorporate the spatial relationships between pixels. First, our model exploits Dirichlet distribution and Dirichlet law to incorporate the local spatial constrains in an image. Secondly, we directly deal with the Student's-t distribution in order to estimate the model parameters, whereas, the Student's-t distributions in previous models are represented as an infinite mixture of scaled Gaussians that lead to an increase in complexity. Finally, instead of using expectation maximization (EM) algorithm, the proposed method adopts the gradient method to minimize the higher bound on the data negative log-likelihood and to optimize the parameters. The proposed model is successfully compared to the state-of-the-art finite mixture models. Numerical experiments are presented where the proposed model is tested on various simulated and real medical images. [ABSTRACT FROM PUBLISHER]

The paper addresses the problem of 3-D Euclidean structure and motion recovery from video sequences based on perspective factorization. It is well known that projective depth recovery and camera calibration are two essential and difficult steps in metric reconstruction. We focus on the difficulties and propose two new algorithms to improve the performance of perspective factorization. First, we propose to initialize the projective depths via a projective structure reconstructed from two views with large camera movement, and optimize the depths iteratively by minimizing reprojection residues. The algorithm is more accurate than previous methods and converges quickly. Second, we propose a self-calibration method based on the Kruppa constraint to deal with more general camera model. The Euclidean structure can be recovered from factorization of the normalized tracking matrix. Extensive experiments on synthetic data and real sequences are performed to validate the proposed method and good improvements are observed. [ABSTRACT FROM AUTHOR]

In video coding, block based full search motion estimation algorithm has been widely used, but it suffers from high computational requirements. In order to reduce the computations, this paper proposes a novel edge based partial distortion search (EPDS) algorithm which reduces the computation of each distortion measure by using partial distortion search. In this algorithm, the entire macroblock (MB) is divided into different sub-blocks and the calculation order of partial distortion is determined based on the edge strength of sub-blocks. This algorithm adaptively changes the early termination threshold for every accumulated partial sum of absolute difference. In the proposed method, only selected numbers of search points are considered for candidate motion vectors. An efficient early termination method, which is based on the dynamic threshold, is also proposed to decide whether a search point has met the Rate-Distortion (RD) cost criterion so that the best search point can be determined early. Simulation results show that the proposed method offers a remarkable improvement in computational speed when compared to full search (FS) and normalized partial distortion search (NPDS) algorithms. The proposed method is 115 times faster than FS, 10 times faster than NPDS and 2 times faster than the dual halfway stop NPDS (DHS-NPDS) on an average. PSNR degradation of the proposed algorithm is negligible and in the region of 0.01 dB. The proposed method can be easily applied to many mobile video application areas such as digital cameras and DMB (Digital Multimedia Broadcasting) phones. [ABSTRACT FROM AUTHOR]

The variable block-size motion estimation (ME) process is the H.264/AVC encoder's most time-consuming function. This letter proposes to reduce the complexity of the ME process with an early termination algorithm that features an adaptive threshold based on the statistical characteristics of rate-distortion (RD) cost regarding current block and previously processed blocks and modes. In this method, most motion searches can be stopped early, with a large number of search points saved. A region-based search is also suggested to further reduce the computation required for full search ME. A search point reduction scheme for the fast motion estimation of H.264/AVC is also introduced, and the experimental results illustrate how the proposed method reduces ME times for full search and fast motion estimation by about 77 and 31%, respectively, despite the insignificant degradation of RD performance. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of 3-D reconstruction of nonrigid objects from uncalibrated image sequences. Under the assumption of affine camera and that the nonrigid object is composed of a rigid part and a deformation part, we propose a stratification approach to recover, the structure of nonrigid objects by first reconstructing the structure in affine space and then upgrading it to the Euclidean space. The novelty and main features of the method lies in several aspects. First, we propose a deformation weight constraint to the problem and prove the invariability between the recovered structure and shape bases under this constraint. The constraint was not observed by previous studies. Second, we propose a constrained power factorization algorithm to recover the deformation structure in affine space. The algorithm overcomes some limitations of a previous singular-value-decomposition-based method. It can even work with missing data in the tracking matrix. Third, we propose to separate the rigid features from the deformation ones in 3-D affine space, which makes the detection more accurate and robust. The stratification matrix is estimated from the rigid features, which may relax the influence of large tracking errors in the deformation part. Extensive experiments on synthetic data and real sequences validate the proposed method and show improvements over existing solutions. [ABSTRACT FROM AUTHOR]

An accurate nonlinearity compensation technique for voltage source inverter (VSI) inverters is presented in this paper. Because of the nonlinearity introduced by the dead time, turn-on/off delay, snubber circuit and voltage drop across power devices, the output voltage of VSI inverters is distorted seriously in the low output voltage region. This distortion influences the output torque of IM motors for constant V/f drives. The nonlinearity of the inverter also causes 5th and 7th harmonic distortion in the line current when the distributed energy system operates in the grid-connected mode, i.e., when the distributed energy system is parallel to a large power system through the VSI inverter. Therefore, the exact compensation of this nonlinearity in the VSI inverter over the entire range of output voltage is desirable. In this paper, the nonlinearity of VSI inverter output voltage and the harmonic distortion in the line current are analyzed based on an open-loop system and a L-R load. By minimizing the harmonic component of the current in a d-axis and q-axis synchronous rotating reference frame, the exact compensation factor was obtained. Simulations and experimental results in the low frequency and low output voltage region are presented. [ABSTRACT FROM AUTHOR]

This paper presents a method for tracking a human head based on the integration of camera saccade and chromatic shape fitting, which are implemented as functional modules in an active tracking system. Head motion is detected in the saccade module by extracting edges from two successive images. The position of the head in the current image is approximated as the centroid of the apparition formed by the moving edges of the target. A visual position cue is used to drive a pan/tilt camera to perform real-time saccade keeping the target in the foveal area in the image. The shape-fitting module is invoked to extract more information from the target. The shape of the target is modeled as an ellipse whose position, orientation and size are dynamically determined by shape fitting, and implemented with a color registration technique. In the proposed method, quasi real-time pursuit is achieved using a Pentium II computer in an uncontrolled environment with arbitrary relative motion between the target and camera. [ABSTRACT FROM AUTHOR]

The sequential fusion estimation for multirate multisensor dynamic systems with heavy-tailed noises and unreliable measurements is an important problem in dynamic system control. This work proposes a sequential fusion algorithm and a detection technique based on Student’s $t$ -distribution and the approximate $t$ -filter. The performance of the proposed algorithm is analyzed and compared with the Gaussian Kalman filter-based sequential fusion and the $t$ -filter-based sequential fusion without detection technique. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective and robust to unreliable measurements. The $t$ -filter-based sequential fusion algorithm is shown to be the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm. [ABSTRACT FROM AUTHOR]

Salient Object Detection aims to detect the most visually distinctive objects in an image. We solve this problem by introducing the average pool to explore the multi-level deep average pool convolution features different from the max pool information. Based on the U-net structure, we propose an Average- and Max-Pool Network (AMPNet) that leverages the average- and max-pool modules to integrate the multi-level complementary contextual features in the spatial and channel-wise dimensions, respectively. The complementary contextual features generated by our network can improve the completeness of detected objects. It has been observed that the non-salient regions are misrecognized as the salient objects because of the redundant information contained in the multi-level convolution features. To address the problem, two top-down feedback paths are introduced based on the above two modules, and their top-level semantic guidance information is fully utilized to improve the accuracy of salient objects detection. Finally, we apply the Feature Fusion Module and Deep Supervision Mechanism to further improve the performance of the network over different datasets. Experimental results on six benchmark datasets show that our network is on par with state-of-the-art approaches. Our method runs at more than 45 FPS (based on VGG) and 35 FPS (based on ResNet) on a single GPU and meets real-time requirements. [ABSTRACT FROM AUTHOR]

Salient object detection is a hot spot of current computer vision. The emergence of the convolutional neural network (CNN) greatly improves the existing detection methods. In this paper, we present 3MNet, which is based on the CNN, to make the utmost of various features of the image and utilize the contour detection task of the salient object to explicitly model the features of multi-level structures, multiple tasks and multiple channels, so as to obtain the final saliency map of the fusion of these features. Specifically, we first utilize contour detection task for auxiliary detection and then utilize use multi-layer network structure to extract multi-scale image information. Finally, we introduce a unique module into the network to model the channel information of the image. Our network has produced good results on five widely used datasets. In addition, we also conducted a series of ablation experiments to verify the effectiveness of some components in the network. [ABSTRACT FROM AUTHOR]

The face super-resolution method is used for generating high-resolution images from low-resolution ones for better visualization. The super-resolution generative adversarial network (SRGAN) can generate a single super-resolution image with realistic textures, which is a groundbreaking work. Based on SRGAN, we proposed improved face super-resolution generative adversarial networks. The super-resolution image details generated by SRGAN usually have undesirable artifacts. To further improve visual quality, we delve into the key components of the SRGAN network architecture and improve each part to achieve a more powerful SRGAN. First, the SRGAN employs residual blocks as the core of the very deep generator network G. In this paper, we decide to employ dense convolutional network blocks (dense blocks), which connect each layer to every other layer in a feed-forward fashion as our very deep generator networks. Moreover, in the past few years, generative adversarial networks (GANs) have been applied to solve various problems. Despite its superior performance, it is difficult to train. A simple and effective regularization method called spectral normalization GAN is used to solve this problem. We have experimentally confirmed that our proposed method is superior to the other existing method in training stability and visual improvements. [ABSTRACT FROM AUTHOR]

Wildfires have devastating consequences on ecological systems and human lives. Accurate and fast wildfire detection is crucial to reduce damage. The existing smoke detection algorithms using convolution neural network are mostly based on the classification of smoke images or patches, whereas the traditional smoke detection algorithms are often necessary to extract multiple features for integration. With the methods mentioned above, false positive is always an insurmountable problem in wildfire smoke detection. Moreover, there are few studies on the detection of wildfire smoke. Thus, to detect the wildfire smoke more intelligent, a 3D parallel fully convolutional network for wildfire smoke detection is proposed to segment the smoke regions in video sequences. Wildfire smoke detection is considered as a segmentation problem in this paper. There are more than 90 videos including various scenes used for training and test. Experiments have demonstrated that our architecture can segment smoke regions accurately and eliminate the interference of natural scenes. Smoke targets in multiple scenes can be detected accurately and quickly. [ABSTRACT FROM AUTHOR]

Face recognition with variable illumination and pose is an important and challenging task in computer vision. In order to solve the problem that the accuracy of face recognition reduces with illumination and pose changes, this paper proposes a method via AMVP (AWULBP_MHOG_VGG_PCA) features and WSRC. In the proposed method, we need to extract AWULBP_MHOG and VGG_PCA features, respectively. As for AWULBP_MHOG features, firstly, variable illumination is normalized for face images. And uniform local binary pattern (ULBP) and multiple histogram of oriented gradient (MHOG) features are extracted from each block, which are called ULBP_MHOG features. Then, we use information entropy to obtain adaptively weighted ULBP_MHOG (AWULBP_MHOG) features. As for VGG_PCA features, we use the pre-trained VGG-Face model to extract VGG features from original face images. And PCA is used to reduce the dimension of VGG features to obtain VGG_PCA features. Then, AWULBP_MHOG and VGG_PCA features are combined to form AMVP (AWULBP_MHOG_VGG_PCA) features. Finally, test face images can be classified using weighted sparse represent (WSRC). The comparison experiments with different blocks, classifiers and features have been conducted on the ORL, Yale, Yale B and CMU-PIE databases. Experimental results prove that our method can improve the accuracy effectively for illumination and pose variable face recognition. [ABSTRACT FROM AUTHOR]

With the growing use of fingerprint identification systems in recent years, preventing fingerprint identification systems from being spoofed by artificial fake fingerprints has become a critical problem. In this paper, we put forward a novel method to detect fingerprint liveness based on BP neural network, which is used for the first time in the fingerprint liveness detection. Moreover, different from traditional detection methods, we propose a scheme to construct the input data and corresponding category labels. More effective and efficient texture features of fingerprints, which are used as the input data of the BP neural network, are computed to improve classification performance and obtain a better pre-trained network model. After a variety of preprocessing operations and image compression operations, gradient values in the horizontal and vertical directions are computed by using Laplacian operator, and difference co-occurrence matrices are constructed from the obtained gradient values. Then, the input data of neural network model are built based on two DCMs. The pre-trained neural network models with diverse neuron nodes are learnt. Different experiments based on different parameters for the BP neural network have been conducted. Finally, classification accuracy of testing fingerprints is predicted based on the pre-trained networks. Experimental results on the LivDet 2013 show that the classification performance of our proposed method is effective and meanwhile provides a better detection accuracy compared with the majority of previously published results. [ABSTRACT FROM AUTHOR]

Most of the existing image steganographic approaches embed the secret information imperceptibly into a cover image by slightly modifying its content. However, the modification traces will cause some distortion in the stego-image, especially when embedding color image data that usually contain thousands of bits, which makes successful steganalysis possible. In this paper, we propose a novel coverless steganographic approach without any modification for transmitting secret color image. In our approach, instead of modifying a cover image to generate the stego-image, steganography is realized by using a set of proper partial duplicates of a given secret image as stego-images, which are retrieved from a natural image database. More specifically, after dividing each database image into a number of non-overlapping patches and indexing those images based on the features extracted from these patches, we search for the partial duplicates of the secret image in the database to obtain the stego-images, each of which shares one or several visually similar patches with the secret image. At the receiver end, by using the patches of the stego-images, our approach can approximately recover the secret image. Since the stego-images are natural ones without any modification traces, our approach can resist all of the existing steganalysis tools. Experimental results and analysis prove that our approach not only has strong resistance to steganalysis, but also has desirable security and high hiding capability. [ABSTRACT FROM AUTHOR]

To remedy some challenging cases in saliency detection such as complex background and multiple objects. A new saliency object detection approach is proposed via integrating reconstruction and prior knowledge. This paper first segments each image into super pixels using over-segmentation algorithm. Then, the reconstruction saliency map and prior saliency map are generated by reconstruction and prior, respectively. The reconstruction involves dense reconstruction and sparse reconstruction. When the saliency object appears on the image boundaries, the detection can be more accurate via dense reconstruction. In addition, if there is complex background in natural scene image, the sparse reconstruction can be more robust and suppress the background effectively. The prior adopts background prior and center prior, which can highlight the saliency object uniformly. The reconstruction saliency map and prior saliency map are nonlinearly integrated to generate the final saliency map. The proposed method is compared with the other five state-of-the-art algorithms based on comprehensive metrics. The experimental results demonstrate that the proposed algorithm has superior saliency detection performance and low average elapsing time. [ABSTRACT FROM AUTHOR]

Face hallucination is of great importance in many applications. In this paper, a novel two-stage framework is proposed for hallucinating high-resolution (HR) face image from the given low-resolution (LR) one. In contrast to the existing methods, where the finer details are ignored, our framework pays more attention to the further local details enhancement. In the first stage, the local position-patch-based method with locality constraint is introduced to obtain the initial estimate image. In order to generate more reasonable face image and reduce noise, our method only represents the input LR patches over the similar training patches in the same position. In the second stage, the initial estimate image rather than residual image is directly used as the input to obtain the final HR image via local position-patch-based method. Besides, contextual information of position-patch is taken into consideration to generate more precise details in the second stage. Extensive experiments on the open face database illustrate that the proposed method achieves superior performance in comparison with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Currently, a huge amount of visual data such as digital images and videos have been collected by visual sensor nodes, that is, camera nodes, and distributed on visual sensor networks. Among the visual data, there are a lot of near-duplicate images, which cause a serious waste of limited storage, computing, and transmission resources of visual sensor networks and a negative impact on users’ experience. Thus, near-duplicate image elimination is urgently demanded. This article proposes a fast and accurate near-duplicate elimination approach for visual sensor networks. First, a coarse-to-fine clustering method based on a combination of global feature and local feature is proposed to cluster near-duplicate images. Then in each near-duplicate group, we adopt PageRank algorithm to analyze the contextual relevance among images to select and reserve seed image and remove the others. The experimental results prove that the proposed approach achieves better performances in the aspects of both efficiency and accuracy compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this paper, we consider the challenging problem of finding shared information in multiple data streams simultaneously. The standard statistical method for doing this is the well-known canonical correlation analysis (CCA) approach. We begin by developing an online version of the CCA and apply it to reservoirs of an echo state network in order to capture shared temporal information in two data streams. We further develop the proposed method by forcing it to ignore shared information that is created from static values using derivative information. We finally develop a novel multi-set CCA method which can identify shared information in more than two data streams simultaneously. The comparative effectiveness of the proposed methods is illustrated using artificial and real benchmark datasets. [ABSTRACT FROM AUTHOR]

Human action recognition (HAR) is one of most important tasks in video analysis. Since video clips distributed on networks are usually untrimmed, it is required to accurately segment a given untrimmed video into a set of action segments for HAR. As an unsupervised temporal segmentation technology, subspace clustering learns the codes from each video to construct an affinity graph, and then cuts the affinity graph to cluster the video into a set of action segments. However, most of the existing subspace clustering schemes not only ignore the sequential information of frames in code learning, but also the negative effects of noises when cutting the affinity graph, which lead to inferior performance. To address these issues, we propose a sequential order-aware coding-based robust subspace clustering (SOAC-RSC) scheme for HAR. By feeding the motion features of video frames into multi-layer neural networks, two expressive code matrices are learned in a sequential order-aware manner from unconstrained and constrained videos, respectively, to construct the corresponding affinity graphs. Then, with the consideration of the existence of noise effects, a simple yet robust cutting algorithm is proposed to cut the constructed affinity graphs to accurately obtain the action segments for HAR. The extensive experiments demonstrate the proposed SOAC-RSC scheme achieves the state-of-the-art performance on the datasets of Keck Gesture and Weizmann, and provides competitive performance on the other 6 public datasets such as UCF101 and URADL for HAR task, compared to the recent related approaches. [ABSTRACT FROM AUTHOR]

Large-scale and deep sky survey missions are rapidly collecting a large amount of stellar spectra, which necessitate the estimation of atmospheric parameters directly from spectra and make it feasible to statistically investigate latent principles in a large data set. We present a technique for estimating parameters Teff, log g, and [Fe/H] from stellar spectra. With this technique, we first extract features from stellar spectra using the LASSO algorithm; then, the parameters are estimated from the extracted features using the support vector regression. On a subsample of 20,000 stellar spectra from the Sloan Digital Sky Survey (SDSS) with reference parameters provided by the SDSS/SEGUE Spectroscopic Parameter Pipeline, estimation consistency are 0.007458 dex for log Teff (101.609921 K for Teff), 0.189557 dex for log g, and 0.182060 for [Fe/H], where the consistency is evaluated by mean absolute error. Prominent characteristics of the proposed scheme are sparseness, locality, and physical interpretability. In this work, each spectrum consists of 3821 fluxes, and 10, 19, and 14 typical wavelength positions are detected, respectively, for estimating Teff, log g, and [Fe/H]. It is shown that the positions are related to typical lines of stellar spectra. This characteristic is important in investigating physical indications from analysis results. Then, stellar spectra can be described by the individual fluxes on the detected positions (PD) or local integration of fluxes near them (LI). The aforementioned consistency is the result based on features described by LI. If features are described by PD, consistency is 0.009092 dex for log Teff (124.545075 K for Teff), 0.198928 dex for log g, and 0.206814 dex for [Fe/H]. [ABSTRACT FROM AUTHOR]

Due to the inevitable wavelength‐dependent light absorption and forward/backward scattering, underwater images usually suffer severe color distortion and are hazy. It has become quite necessary to improve the visual quality of underwater images for both underwater observation and operation. Traditional enhancement methods and existing deep learning‐based approaches to underwater image enhancement usually produce unsatisfactory results for photographs taken in complicated, wild underwater scenes. In such scenes, complex and diverse degradation‐enhancement mappings are often difficult to model, especially since there are very limited samples available for learning. Inspired by the success of color‐transfer techniques, it is found that clear template image‐assisted color transfer is a promising strategy for underwater image enhancement, including not only color correction but also contrast and visibility improvement. Therefore, instead of directly learning the complex deep enhancement models, it is proposed to select proper color‐transfer templates by learning the latent consistency between the templates and the raw underwater images. The proposed new enhancement strategy alleviates the problem caused by incomplete color‐correction models and provides more stable enhancements by utilizing color transfer with consideration of global color distribution consistency and local visual contrast. Comprehensive experiments conducted on UIEB, RUIE, URPC and SQUID datasets demonstrate the good performance and great potential of the proposed new underwater image enhancement strategy. [ABSTRACT FROM AUTHOR]

At present, many studies have shown that partitioning the gait sequence and its feature map can improve the accuracy of gait recognition. However, most models just cut the feature map at a fixed single scale, which loses the dependence between various parts. So, our paper proposes a structure called Part Feature Relationship Extractor (PFRE) to discover all of the relationships between each parts for gait recognition. The paper uses PFRE and a Convolutional Neural Network (CNN) to form the RPNet. PFRE is divided into two parts. One part that we call the Total-Partial Feature Extractor (TPFE) is used to extract the features of different scale blocks, and the other part, called the Adjacent Feature Relation Extractor (AFRE), is used to find the relationships between each block. At the same time, the paper adjusts the number of input frames during training to perform quantitative experiments and finds the rule between the number of input frames and the performance of the model. Our model is tested on three public gait datasets, CASIA-B, OU-LP and OU-MVLP. It exhibits a significant level of robustness to occlusion situations, and achieves accuracies of 92.82% and 80.26% on CASIA-B under BG # and CL # conditions, respectively. The results show that our method reaches the top level among state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The pose estimation is the critical technology in industrial robot. Nowadays, many machine vision-based approaches have applied the technology and achieved excellent results. However, the rapid detection of the pose estimation in complex multiscene environments is still a challenge, due to the interference of multiangle light and multibackground. To address these issues, this article proposes a practical robotic grasping method by using the 6-D pose estimation with protective correction. In this method, the synthetic dataset by self-production is used to train the improved deep object pose estimation network and then use the standard perspective-n-point algorithm to estimate the 6-DoF pose for each object instance. Meanwhile, in order to prevent grasp collisions cause by misrecognition, we propose the corrected grasping pose algorithm for protective correction by measured translation and predicted translation. Finally, the proposed grasping method has an average grasping success rate of 83.3% for the three objects under normal light, and the network for single-image detection speed has been to 1.490 frames/s. The code is available at https://github.com/aimiplus/Practical_Robotic_Grasping_Method. [ABSTRACT FROM AUTHOR]

In underwater scenes, degraded underwater images caused by wavelength-dependent light absorption and scattering present huge challenges to vision tasks. Underwater image enhancement has attracted much attention due to the significance of vision-based applications in marine engineering and underwater robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, almost all existing approaches focus only on the enhancement of independent images. Considering that images photographed in the same underwater scene usually share similar degradation, related images can provide rich complementary information for each other’s enhancement. In this paper, we propose an Underwater Image Co-enhancement Network (UICoE-Net) based on an encoder-decoder Siamese architecture. For joint learning, we introduced correlation feature matching units into the multiple layers of our Siamese encoder-decoder structure in order to communicate the mutual correlation of the two branches. Extensive experiments using the Underwater Image Enhancement Benchmark (UIEB), Underwater Image Co-enhancement Dataset (UICoD) collected from an underwater video dataset with ground-truth reference and Stereo Quantitative Underwater Image Dataset (SQUID) dataset demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Baggage reidentification (re-ID) expects to retrieve the unique baggage across multiple nonoverlapping cameras between the precheck and postcheck areas. This critical challenge task lies in two folds: intraclass dissimilarity by pose variations and interclass similarity due to the identical style. In this article, we solve the mentioned problems by a selective multibranch deep network (SMBNet) architecture with the attention mechanism. Specifically, SMBNet incorporates one branch for global feature representations and multiple-part representations corresponding to different local regions. Meanwhile, we plugged the attention block into the part branch to further help the network capture the dependence of the context on local features. Depending on the class response to each part, we propose an adaptive part selection (APS) to automatically select the appropriate part branch and joint with the global one to build a comprehensive baggage descriptor. It effectively blurs unreliable parts and significantly improves representational performance. Finally, we couple classification supervision and material signal in triplet loss to learn a discriminative embedding space that can compact class and material-invariant features and better separate specific representations. Extensive experiments demonstrate the superiority of SMBNet, which achieves 84.7% mean average precision (mAP) and 82.8% Rank-1 on the MVB dataset and significantly outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Artificial neural network training algorithms aim to optimize the network parameters regarding the pre-defined cost function. Gradient-based artificial neural network training algorithms support iterative learning and have gained immense popularity for training different artificial neural networks end-to-end. However, training through gradient methods is time-consuming. Another family of training algorithms is based on the Moore–Penrose inverse, which is much faster than many other gradient methods. Nevertheless, most of those algorithms are non-iterative and thus do not support mini-batch learning in nature. This work extends two non-iterative Moore–Penrose inverse-based training algorithms to enable online sequential learning: a single-hidden-layer autoencoder training algorithm and a sub-network-based classifier training algorithm. We further present an approach that uses the proposed autoencoder for self-supervised dimension reduction and then uses the proposed classifier for supervised classification. The experimental results show that the proposed approach achieves satisfactory classification accuracy on many benchmark datasets with extremely low time consumption (up to 50 times faster than the support vector machine on CIFAR 10 dataset). [ABSTRACT FROM AUTHOR]

In this paper, we present a systematic framework for recognizing human actions without relying on impractical assumptions, such as processing of an entire video or requiring a large look-ahead of frames to label an incoming video. As a secondary goal, we examine incremental learning as an overlooked obstruction to the implementation of reliable real-time recognition. Assuming weak appearance constancy, the shape of an actor is approximated by adaptively changing intensity histograms to extract pyramid histograms of oriented gradient features. As action progresses, the shape update is carried out by adjustment of a few blocks within a tracking window to closely track evolving contours. The nonlinear dynamics of an action are learned using a recursive analytic approach, which transforms training into a simple linear representation. Such a learning strategy has two advantages: 1) minimized error rates, and significant savings in computational time; and 2) elimination of the widely accepted limitations of batch-mode training for action recognition. The effectiveness of our proposed framework is corroborated by experimental validation against the state of the art. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of structure and motion recovery from uncalibrated image sequences. It has been empirically proven that image measurement uncertainties can be modeled spatially and temporally by virtue of reprojection residuals. Consequently, a spatial-and-temporal-weighted factorization (STWF) algorithm is proposed to handle significant noise contained in the tracking data. This paper presents three novelties and contributions. First, the image reprojection residual of a feature point is demonstrated to be generally proportional to the error magnitude associated with the image point. Second, the error distributions are estimated from a different perspective, that of the reprojection residuals. The image errors are modeled both spatially and temporally to cope with different kinds of uncertainties. Previous studies have considered only the spatial information. Third, based on the estimated error distributions, an STWF algorithm is proposed to improve the overall accuracy and robustness of traditional approaches. Unlike existing approaches, the proposed technique does not require prior information of image measurement and is easy to implement. Extensive experiments on synthetic data and real images validate the proposed method. [ABSTRACT FROM AUTHOR]

For onshore high-frequency surface-wave radar (HFSWR), target parameter estimation focuses mainly on distance and velocity demodulation, then on azimuth determination. However, these parameters are difficult to solve accurately for shipborne HFSWR targets due to additional modulation on the echo signal introduced by the forward and six-degree-of-freedom (6-DOF) movement of the platform, causing target points spread and shift in radar Doppler spectra. To overcome this difficulty, the influence of platform motion on target detection is mathematically analyzed in terms of echo signal processing, and then theoretical equations are derived to correct the bias in measurements of the target’s state and features. Furthermore, to meet the requirement of shipborne HFSWR installed in limited space, the direction of arrival (DOA) estimation of irregular radar arrays with unequal intervals and arbitrary numbers of antennas is also analyzed. With the derived formulas, the parameters of the shipborne HFSWR target, including the range, radial velocity, and azimuth, can be accurately estimated with the help of inertial navigation system (INS) data. Moreover, both the simulation and the field experiment results validate the theoretical analysis and the derived equations. [ABSTRACT FROM AUTHOR]