Search

This paper addresses the issue of detecting the use of information hiding at anti-copying 2D barcodes. Prior hidden information detection schemes have their roots in either heuristic-based or Machine Learning (ML). However, prior heuristics-based schemes lack a rigorous theoretical analysis. Prior ML-based information schemes lack robustness because a printed 2D barcode is very much environmentally dependent. Thus, an information hiding detection scheme trained in one environment often does not work well in another environment. In this paper, we propose two hidden information detection schemes for existing anti-copying 2D barcodes. The first scheme directly uses the pixel distance to detect the use of an information hiding scheme in a 2D barcode, referred to as the Pixel Distance Based Detection (PDBD) scheme. The second scheme first calculates the variance of raw signal and the covariance between the recovered and raw signals, and then based on the variance results, detects the use of information hiding scheme in a 2D barcode, referred to as the Pixel Variance Based Detection (PVBD) scheme. Moreover, we design advanced Illegitimately-Copying (IC) attacks to evaluate the security of two existing anti-copying 2D barcodes. We conduct extensive performance comparisons among the proposed schemes and prior schemes under different capturing devices, e.g., different scanners or camera phones. Our experimental results show that the PVBD scheme can correctly detect the existence of hidden information at both the 2LQR code and the LCAC 2D barcode. Moreover, the successful attacking probability of the proposed IC attacks achieves 0.6538 for the 2LQR code and 1 for the LCAC 2D barcode. [ABSTRACT FROM AUTHOR]

This paper presents a new block-based method for the High Efficiency Video Coding (HEVC) intra coding. First, we have found through analysis and test that the prediction errors on some pixels in each prediction block (PB) that are neighboring to the reference pixels would be no bigger than the corresponding coding errors. Based on this observation, the pixels in each PB are divided into two parts: half pixels are coded via a novel padding technique together with a constrained quantization algorithm (leading to around 3 dB gain under the same bit rate), whereas the other half are reconstructed by linear interpolations along a prediction direction by utilizing the neighboring reference pixels and the first half coded pixels. In the final implementation, a competition mechanism is employed between this new method and the original HEVC intra coding in order to choose the best mode for each PB. Experimental results show that about 2% BD-rate reduction has been achieved both for luma and chroma with respect to the original HEVC intra coding, whereas the encoder complexity increases by 130%, but the decoding time remains nearly unchanged. [ABSTRACT FROM AUTHOR]

In this paper, we present a unified approach for abnormal behavior detection and group behavior analysis in video scenes. Existing approaches for abnormal behavior detection do either use trajectory-based or pixel-based methods. Unlike these approaches, we propose an integrated pipeline that incorporates the output of object trajectory analysis and pixel-based analysis for abnormal behavior inference. This enables to detect abnormal behaviors related to speed and direction of object trajectories, as well as complex behaviors related to finer motion of each object. By applying our approach on three different data sets, we show that our approach is able to detect several types of abnormal group behaviors with less number of false alarms compared with existing approaches. [ABSTRACT FROM PUBLISHER]

Depth image-based rendering (DIBR) techniques play an important role in free viewpoint videos (FVVs), which have a wide range of applications including immersive entertainment, remote monitoring, education, etc. FVVs are usually synthesized by DIBR techniques in a “blind” environment (without a reference video). Thus, an effective reference-free synthesized video quality assessment (VQA) metric is vital. At present, many image quality assessment (IQA) algorithms for DIBR-synthesized images have been proposed, but limited researches have been concerned about the quality assessment of DIBR-synthesized videos. To this end, this paper proposes a novel reference-free VQA method for synthesized videos, which operates in Spatial and Temporal Domains, dubbed as STD. The design fundamental of the proposed STD metric considers the effects of two major distortions introduced by DIBR techniques on the visual quality of synthesized videos. First, considering the geometric distortion introduced by DIBR technologies can increase high-frequency contents of the synthesized frame, the influence of the geometric distortion on the visual quality of a synthesized video can be effectively evaluated by estimating high-frequency energies of each synthesized frame in spatial domain. Second, temporal inconsistency caused by DIBR techniques brings the temporal flicker distortion, which is one of the most annoying artifacts in DIBR-synthesized videos. In temporal domain, we quantify temporal inconsistency by measuring motion differences between consecutive frames. Specifically, optical flow method is first used to estimate the motion field between adjacent frames. Then, we calculate the structural similarity of adjacent optical flow fields and further adopt the structural similarity value to weight the pixel differences of adjacent optical flow fields. Experiments show that the above two features are able to well perceive the visual quality of DIBR-synthesized videos. Furthermore, since the two features are extracted from spatial and temporal domains, respectively, we integrate them using a linear weighting strategy to obtain our STD metric, which proves advantageous over two components and the competing state-of-the-art I/VQA methods. The source code is available at https://github.com/wgc-vsfm/DIBR-video-quality-assessment. [ABSTRACT FROM AUTHOR]

In the area of change detection, there were a rare number of optimization methods. Most of the optimization methods that are used by change detection are morphological transformation or median filtering, which cannot best optimize change detection algorithm. In this paper, a general post-processing algorithm for change detection is proposed. We believe that some problems cannot be avoided in the area of change detection such as 1) region of moving object generated by change detection is slightly larger than the ground-truth and 2) there are always some disjoint and small regions that are independent from the moving objects. To address the problem, our method can optimize the change detection algorithm bases on the idea of edge detection, which can remove the wrong edge or pixel. In the experiments, more than 20 change detection algorithms that include the best algorithm in ChangeDetection.net are selected. Most of these change detection algorithms are optimized by the proposed method on PWC, Precision, and FMeasure, where, our optimized algorithm named FgSegNet_v2 is better than all other algorithms in the CDnet. The best-optimized margin of PWC is 0.64, and the fast speed is 548FPS on CPU. Our approach can better resolve the afore-mentioned problems that cannot be avoided and is general and fast. The experiments can be reproduced with C++ on Github https://github.com/walty19950301/CDA-contour-optimizer. [ABSTRACT FROM AUTHOR]

Recent advances on adaptive steganography imply that the security of steganography can be improved by exploiting the mutual impact of modifications between adjacent cover elements, such as pixels of images, which is called a nonadditive distortion model. In this paper, we propose a framework for nonadditive distortion steganography by defining joint distortion on pixel blocks. To reduce the complexity for minimizing joint distortion, we design a coding method to decompose the joint distortion (abbreviated to DeJoin) into distortion on individual pixels; thus, the message can be efficiently embedded with syndrome-trellis codes. We prove that DeJoin can approach the lower bound of joint distortion. As an example, we define joint distortion according to the principle of synchronizing modification direction and then design steganographic algorithms with DeJoin. The experimental results show that the proposed method outperforms previous nonadditive distortion steganography when resisting the state-of-the-art steganalysis. [ABSTRACT FROM PUBLISHER]

As one of the most popular image oversegmentations, superpixel has been commonly used as supporting regions for primitives to reduce computations in various computer vision tasks. In this paper, we propose a novel superpixel segmentation approach based on a distance function that is designed to balance among boundary adherence, intensity homogeneity, and compactness (COM) characteristics of the resulting superpixels. Given an expected number of superpixels, our method begins with initializing the superpixel seed positions to obtain the initial labels of pixels. Then, we optimize the superpixels iteratively based on the defined distance measurement. We update the positions and intensities of superpixel seeds based on the three-sigma rule. The experimental results demonstrate that our algorithm is more effective and accurate than previous superpixel methods and achieves a comparable tradeoff between superpixel COM and adherence to object boundaries. [ABSTRACT FROM PUBLISHER]

This paper presents the VLSI architecture and hardware implementation of a highly efficient deblocking filter (DBF) for High Efficiency Video Coding systems. In order to reduce the number of data accesses and thus to enhance the timing efficiency, novel data structures and memory access schemes for image pixels are proposed. Furthermore, a novel edge-fetching order is presented to strike a balance between the processing throughput and complexity. Based on the proposed structure and access pattern, a six-stage pipelined two-line DBF engine with low-latency data access sequence is designed, aiming to achieve high processing throughput while at the same time maintaining low complexity. The detailed storage structure and data access scheme are illustrated and VLSI architecture for the DBF engine is depicted in this paper. In addition, the proposed DBF is implemented using TSMC 90-nm standard cell library. The experimental results based on postlayout estimations show that the proposed design can achieve 60 frames/s for a frame resolution of $4096\times 2048$ pixels (ultra high definition resolution) assuming an operating frequency of 100 MHz. Moreover, this design occupies an area complexity of 466.5 kGE with a power consumption of 26.26 mW. In comparison with prior designs targeting similar system specification and throughput, the proposed design results in a significantly reduced area complexity. [ABSTRACT FROM PUBLISHER]

Cost computation, cost aggregation, disparity optimization, and disparity refinement are the four main steps for stereo matching. While the first three steps have been widely investigated, few efforts have been taken on disparity refinement. In this paper, we propose a color image-guided disparity refinement method to further remove the boundary-inconsistent regions on disparity map. First, the origins of boundary-inconsistent regions are analyzed. Then, these regions are detected with the proposed hybrid-superpixel-based strategy. Finally, the detected boundary-inconsistent regions are refined by a modified weighted median filtering method. Experimental results on various stereo matching conditions validate the effectiveness of the proposed method. Furthermore, depth maps obtained by active depth acquisition devices like Kinect can also be well refined with our proposed method. [ABSTRACT FROM PUBLISHER]

Both a high spatial and a high temporal resolution of images and videos are desirable in many applications, such as entertainment systems, monitoring manufacturing processes, or video surveillance. Due to the limited throughput of pixels per second, however, there is always a tradeoff between acquiring sequences with a high spatial resolution at a low temporal resolution or vice versa. In this paper, a modified sensor concept is proposed which is able to acquire both a high spatial and a high temporal resolution. This is achieved by dynamically reading out only a subset of pixels in a non-regular order to obtain a high temporal resolution. A full high spatial resolution is then obtained by performing a subsequent 3D reconstruction of the partially acquired frames. The main benefit of the proposed dynamic readout is that for each frame, different sampling points are available, which is advantageous since this information can significantly enhance the reconstruction quality of the proposed reconstruction algorithm. Using the proposed dynamic readout strategy, gains in the peak-signal-to-noise ratio (PSNR) of up to 8.55 dB are achieved compared with a static readout strategy. Compared with the other state-of-the-art techniques, such as frame rate up-conversion or super-resolution, which are also able to reconstruct sequences with both a high spatial and a high temporal resolution, average gains in PSNR of up to 6.58 dB are possible. [ABSTRACT FROM AUTHOR]

Pairwise prediction-error expansion (pairwise PEE) is a recent technique for the high-dimensional reversible data hiding. However, in the absence of adaptive embedding, its potential has not been fully exploited. In this paper, we propose the adaptive pixel pairing (APP) and the adaptive mapping selection for the enhancement of pairwise PEE. Our motivation is twofold: building a sharper 2D histogram and designing the effective 2D mapping for it. In APP, we consider to increase the similarity between pixels in a pair, by excluding the rough pixels from pairing and only putting the smooth pixels into pairs. In this way, the pixels in a pair have a larger possibility of being equal, and thus the resulted 2D prediction-error histogram (PEH) has lower entropy. Next, the adaptive mapping selection mechanism is introduced to properly determine the optimal modification, based on “whether it fits for the resulted PEH” rather than heuristic experience. The experimental results show that the proposed method has a significant improvement over the pairwise PEE. [ABSTRACT FROM AUTHOR]

Depth image-based rendering (DIBR) techniques have recently drawn more attention in various 3D applications. In this paper, a real-time high-quality DIBR system that consists of disparity estimation and view synthesis is proposed. For disparity estimation, a local approach that focuses on depth discontinuities and disparity smoothness is presented to improve the disparity accuracy. For view synthesis, a method that contains view interpolation and extrapolation is proposed to render high-quality virtual views. Moreover, the system is designed with an optimized parallelism scheme to achieve a high throughput, and can be scaled up easily. It is implemented on an Altera Stratix IV FPGA at a processing speed of 45 frames per second for 1080p resolution. Evaluated on selected image sets of the Middlebury benchmark, the average error rate of the disparity maps is 6.02%; the average peak signal to noise ratio and structural similarity values of the virtual views are 30.07 dB and 0.9303, respectively. The experimental results indicate that the proposed DIBR system has the top-performing processing speed and its accuracy performance is among the best of state-of-the-art hardware implementations. [ABSTRACT FROM AUTHOR]

Change/motion detection is a challenging problem in video analysis and surveillance system. Recently, the state-of-the-art methods using the sample-based background model have demonstrated astonishing results with this problem. However, they are ineffective in the dynamic scenes that contain complex motion patterns. In this paper, we introduce a novel data-driven approach that combines the sample-based background model with a feature extractor obtained by training a triplet network. We construct the network by three identical convolutional neural networks, each of which is called a motion feature network. Our network can automatically learn motion patterns from small image patches and transform input images of any size into feature embeddings for high-level representations. The sample-based background model of each pixel is then employed by using the color information and the extracted feature embeddings. We also propose an approach to generate triplet examples from CDNet 2014 for training our network model from scratch. The offline trained network can be used on the fly without re-training on any video sequence before each execution. Therefore, it is feasible for real-time surveillance systems. In this paper, we show that our method outperforms the other state-of-the-art methods on CDNet 2014 and other benchmarks (BMC and Wallflower). [ABSTRACT FROM AUTHOR]

Although numerous trackers have been designed to adapt to the nonstationary image streams that change over time, it remains a challenging task to facilitate a tracker to accurately distinguish the target from the background in every frame. This paper proposes a robust superpixel-based tracker via depth fusion, which exploits the adequate structural information and great flexibility of mid-level features captured by superpixels, as well as the depth-map's discriminative ability for the target and background separation. By introducing graph-regularized sparse coding into the appearance model, the local geometrical structure of data is considered, and the resulting appearance model has a more powerful discriminative ability. Meanwhile, the similarity of the target superpixels' neighborhoods in two adjacent frames is also incorporated into the refinement of the target estimation, which helps a more accurate localization. Most importantly, the depth cue is fused into the superpixel-based target estimation so as to tackle the cluttered background with similar appearance to the target. To evaluate the effectiveness of the proposed tracker, four video sequences of different challenging situations are contributed by the authors. The comparison results demonstrate that the proposed tracker has more robust and accurate performance than seven ones representing the state-of-the-art. [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]

With the development of display technology, bit-depth expansion (BDE) has emerged as a basic process to display low-bit-depth image and video resources on high-bit-depth monitors. Most current BDE methods are based on traditional algorithms, and the few existing methods based on deep neural networks still suffer from loss of pixel-level details or from high computational cost. This paper proposes a lightweight but efficient BDE network that can effectively improve the capacity of shallow network by introducing a residual-block-in-residual-block structure. Furthermore, the proposed network adopts residual network architecture and dilated convolution to balance the preservation of pixel-level information and the expansion of the receptive field. Hence, the proposed method can also totally remove significant artifacts from very low-bit-depth images. Experimental results demonstrate that the proposed method can achieve performance comparable to or even better than that of some state-of-the-art methods while having much lighter architecture and fewer parameters. [ABSTRACT FROM AUTHOR]

Deep learning-based methods especially using convolutional neural network (CNN) and generative adversarial network (GAN) have achieved certain success for the task of image inpainting. The previous methods usually try to generate the content in the missing areas from scratch. However, these methods have difficulty in producing salient image structures that appear natural and consistent with the neighborhood, especially when the missing area is large. In this paper, we address the challenge by introducing edges into the convolutional GAN-based inpainting. We split the inpainting task into two steps: first edge generation, then edge-based image generation. We adopt CNN to accomplish the two steps and use GAN-based training, thus our method is named E2I: generative inpainting from edge to image. Specifically, we adopt a deep network-based edge detector to achieve an edgeness map of an incomplete image, then we fill-in the missing areas in the edgeness map, and finally generate the missing pixels with the assistance of the complete edgeness map. We verify the proposed method on three challenging image datasets: Places2, ImageNet, and CelebA. We also compare our method with the state-of-the-arts on the Places2 test set. Our experimental results demonstrate the superior performance of our method in producing more plausible inpainting results. [ABSTRACT FROM AUTHOR]

As one of the fundamental technologies for scene understanding, semantic segmentation has been widely explored in the last few years. Light field cameras encode the geometric information by simultaneously recording the spatial information and angular information of light rays, which provides us with a new way to solve this issue. In this paper, we propose a high-quality and challenging urban scene dataset, containing 1074 samples composed of real-world and synthetic light field images as well as pixel-wise annotations for 14 semantic classes. To the best of our knowledge, it is the largest and the most diverse light field dataset for semantic segmentation. We further design two new semantic segmentation baselines tailored for light field and compare them with state-of-the-art RGB, video and RGB-D-based methods using the proposed dataset. The outperforming results of our baselines demonstrate the advantages of the geometric information in light field for this task. We also provide evaluations of super-resolution and depth estimation methods, showing that the proposed dataset presents new challenges and supports detailed comparisons among different methods. We expect this work inspires new research direction and stimulates scientific progress in related fields. The complete dataset is available at https://github.com/HAWKEYE-Group/UrbanLF. [ABSTRACT FROM AUTHOR]

Although modality-specific feature compensation becomes a prevailing paradigm for Visible-Infrared Person Re-Identification (VI-ReID) to learn features, it, performance-wise, is not promising, especially when compared to modality-shared feature learning. In this paper, by revisiting the modality-specific feature compensation based models, we reveal that the reasons for being under-performed are: (1) generated images of one modality from another modality may be poor in quality; (2) such existing models usually achieve the modality-specific feature compensation just via simple pixel-level fusion strategies; (3) generated images cannot fully replace corresponding missing ones, which brings in extra modality discrepancy. To address these issues, we propose a new Two-Stage Modality Enhancement Network (TSME) for VI-ReID. Concretely, it first considers the modality discrepancy for cross-modality style translation and optimizes the structures of image generators by involving a new Deeper Skip-connection Generative Adversarial Networks (DSGAN) to generate high-quality images. Then, it presents an attention mechanism based feature-level fusion module, i.e., Pair-wise Image Fusion (PwIF) module, and an auxiliary learning module, i.e., Invoking All-Images (IAI) module, to better exploit the generated and original images for reducing modality discrepancy from the perspectives of feature fusion and feature constraints, respectively. Comprehensive experiments are carried out to demonstrate the success of TSME in tackling the modality discrepancy issue exposed in VI-ReID. [ABSTRACT FROM AUTHOR]

As a specific data hiding technique, reversible data hiding (RDH) has recently received extensive attention. By this technique, both the embedded data and the original cover image can be exactly extracted from the marked image. In our previous work, a general expansion-shifting model for RDH is proposed by introducing the so-called reversible embedding function (REF). With REF, RDH can be designed and the corresponding rate-distortion formulations can be established, providing an approach to optimize the reversible embedding performance. In this paper, by extending our previous work, optimal REF for one-dimensional histogram is investigated, and all optimal REF are derived in this case when the maximum modification to the cover pixel is limited as a small value. Moreover, based on the derived optimal REF for one-dimensional histogram and multiple histograms modification, a practical RDH scheme is presented and it is experimental verified better than some state-of-the-art algorithms in terms of capacity-distortion performance. [ABSTRACT FROM AUTHOR]

While the existing reserving room before encryption (RRBE) based reversible data hiding in encrypted image (RDHEI) schemes can achieve decent embedding capacity, the capacity of the existing vacating room by encryption (VRBE) based schemes is relatively low. To address this issue, this paper proposes a generalized framework for high-capacity RDHEI for both the RRBE and VRBE cases. First, an efficient embedding room generation algorithm (ERGA) is designed to produce large embedding room using pixel prediction and entropy encoding. Then, we propose two RDHEI schemes, one for RRBE, another for VRBE. In the RRBE scenario, the image owner generates the embedding room with ERGA and encrypts the preprocessed image using stream cipher with two encryption keys. Then, the data hider locates the embedding room and embeds the additional encrypted data. In the VRBE scenario, the cover image is encrypted by an improved block modulation and permutation encryption algorithm, where the spatial redundancy in the plain-text image is greatly preserved. Then, the data hider applies ERGA on the encrypted image to generate the embedding room and conducts data embedding. For both schemes, receivers with different authentication keys can conduct either error-free data extraction or error-free image recovery. The experimental results show that the two proposed schemes outperform many state-of-the-art RDHEI schemes. Besides, they can ensure high security level, where the original image can be hardly discovered from the encrypted version before or after data hiding by unauthorized users. [ABSTRACT FROM AUTHOR]

A low-complexity depth-reliability-based stereo-matching algorithm and an efficient scanline memory-merging implementation scheme are proposed in this paper. The developed algorithm analyzes the accuracy of disparity results by using simple local window-based methods and preserves reliable information only. A bidirectional depth propagation flow is then adopted to fill the unreliable segments by using reliable information. Moreover, a set of predefined function-specific reliability variables are extracted to further improve depth quality in the occluded and smooth regions, which can reduce 39% bad pixels obtained by applying the basic $7\times 7$ window-based matching. The proposed scanline memory-merging scheme along with data prefetching can lead to 32.7% savings on the scanline memory area and relax the requirements of external frame buffer size and bandwidth. Experimental results show that the implemented stereo-matching hardware has a gate count of 223 k including the scanline memory, and can achieve up to 70 frames/s for $480\times 540$ resolution ( $2\times 2$ downsampling of FullHD side-by-side 3-D format) with 56 disparity levels. [ABSTRACT FROM PUBLISHER]

In this paper we address the problem of semantic segmentation. Inspired by the significant role of the context information, in this task our solution makes use of semantically meaningful overlapping object hypotheses augmented by contextual information, which is obtained from a novel background mining procedure. More precisely, a fully connected conditional random field is considered over a set of overlapping segment hypotheses and the unlabeled background regions are learned from a training set and applied in the unary terms corresponding to the foreground regions. The final segmentation result is obtained via maximum a posteriori inference, in which the segments are merged based on a sequential aggregation followed by morphological hole filling and superpixel refinement serving as postprocessing. Moreover, by incorporating other kinds of contextual cues, such as global image classification and object detection cues, a new state-of-the-art performance is achieved by our proposed solution as experimentally verified on the challenging PASCAL Visual Object Class (VOC) Challenge 2012 and MSRC-21 object segmentation data sets. [ABSTRACT FROM AUTHOR]

Recently, region-based 3D video coding has been proposed. However, existing view synthesis distortion estimation (VSDE) methods are performed at the frame level. To guide the rate-distortion optimization process of region-based 3D video coding schemes, this paper proposes the first pixel-level VSDE (PL-VSDE) method. We first give the definition of the pixel-level view synthesis distortion. To estimate it, a backward prediction method is then developed, which starts from the pixels of interest (POIs) in the virtual view and finds their corresponding pixels in the reference view via a coarse-to-fine approach, denoted as coarse-to-fine backward prediction (CFBP) method. Additionally, the CFBP fully considers the details of 3D warping, the rounding operation and the warping competition in view synthesis, leading to improve accuracy of the prediction. Besides, a table-lookup method and a warping property are introduced to speed up the CFBP. After integrating the CFBP into the PL-VSDE, we can estimate the view synthesis distortion at the pixel level. Our method is carried out pixel-by-pixel independently, which is friendly for parallel processing. The experimental results demonstrate that our proposed method has significant advantages in both accuracy and efficiency compared with the state-of-the-art frame-level VSDE methods. [ABSTRACT FROM AUTHOR]

This paper presents an effective video cut-out approach, which can be utilized to segment the moving object in video shots. We first introduce the Octagon-Sensitive-Filtering (OSF) and its illumination invariant feature (IIF), which is computed on each pixel of the image via adding contributions from neighboring pixels. We integrate our IIF into the variational model and obtain the seeds during preprocessing to help address large displacement and illumination changes. An effective seed update method based on tracking-then-refinement based on IIF is presented to compensate for location ambiguities, and the strategy is effective to deal with illumination variances and objects deformation. Furthermore, we apply the IIF-based graph-cut to deal with fuzzy boundaries. Multiple experiments on quantitative challenging datasets have shown the robustness, high-quality video cut-out and efficiency of our approach to acute variances of illumination and complex motion. [ABSTRACT FROM AUTHOR]

Depth images play an important role and are prevalently used in many computer vision and computational imaging tasks. However, due to the limitation of active sensing technology, the captured depth images in practice usually suffer from low resolution and noise, which prevents its further applications. To remedy this problem, in this paper, we first propose an adaptive data fidelity formulation to optimally generate each depth pixel from a mixture probability distribution, characterizing the similarity both in the depth map and the corresponding high-resolution guided color image. The proposed method is able to fit the distribution of the input depth signal as an optimization problem by maximizing the mixture probability. Furthermore, to promote the piecewise property that depth images exhibit, we propose a transferred graph Laplacian model as a regularization term, which is general and able to handle various depth recovery tasks such as super-resolution and denoising well. Specifically, each pixel within the recovered depth image is represented as a vertex in a graph with weights in connected edges representing the similarity between vertices. By minimizing the squared variations of the image signal, the task of depth image recovery can be converted to the problem of graph-based image filtering. Since the proposed graph Laplacian regularization model is able to fully exploit a priori information about the depth image, a much more accurate and robust estimation of the underlying depth can be obtained. Extensive experiment evaluations verify that the proposed method obtains recovered depth with higher quality in terms of both objective and subjective criteria, compared with most of the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Compared to element-wise embedding, vector-wise embedding based on CPV (color pixel vector) shows its superiority in color image steganography. However, when working with an adversarial embedding scheme for introducing adversarial perturbations, its success rate of deceiving a target CNN (convolutional neural network) steganalyzer dramatically drops. In this paper, inspired by the I-FGSM (iterative fast gradient sign method), we present an effective steganography for color images. Specifically, after decomposing an image into several non-overlapped sub-images, we iteratively and gradually increase the possibilities of generating adversarial perturbations for the CPVs in each sub-image by changing their adversarial costs. The costs are incrementally adjusted with a small step so that their maximum relative variation is minimized. Leveraging a new designed cost adjustment criterion, more modification patterns of CPV can participate in producing effective adversarial perturbations. Extensive experiments demonstrate that the proposed method achieves a high success rate in deceiving the target CNN steganalyzer and stably defending against the detection of other non-target steganalytic schemes for color images. [ABSTRACT FROM AUTHOR]

Edge-preserving image filtering is an essential task in computational photography and imaging. In this paper, we propose a simple yet effective global edge-preserving filter based on soft clustering, and we propose a novel soft clustering algorithm based on a restricted Gaussian mixture model. Given specified parameters, the soft clustering process is firstly performed on the image to derive the partition matrix, from which the affinity matrix is then constructed for filtering. The filtering output is calculated as the weighted average of the pixels in the local window, so the proposed filter could suppress the intensity shift artifacts that impede most global filters. Besides, the weights in the proposed filter are derived by clustering, which properly separates dissimilar pixels, so the proposed filter could handle the halo artifacts that haunt many local filters. Moreover, our filter provides flexible control over the amount of smoothing that is deficient in the deep learning-based filters. Besides the efficacy in smoothing, the proposed filter naturally has low computational complexity. Qualitative and quantitative results suggest that the proposed filter benefits various applications, including edge-preserving smoothing, image enhancing, flash/non-flash fusion, HDR tone mapping, and dehazing. [ABSTRACT FROM AUTHOR]

This paper presents a robust foreground detection method capable of adapting to different motion speeds in scenes. A key contribution of this paper is the background estimation using a proposed novel algorithm, neighbor-based intensity correction (NIC), that identifies and modifies the motion pixels from the difference of the background and the current frame. Concretely, the first frame is considered as an initial background that is updated with the pixel intensity from each new frame based on the examination of neighborhood pixels. These pixels are formed into windows generated from the background and the current frame to identify whether a pixel belongs to the background or the current frame. The intensity modification procedure is based on the comparison of the standard deviation values calculated from two pixel windows. The robustness of the current background is further measured using pixel steadiness as an additional condition for the updating process. Finally, the foreground is detected by the background subtraction scheme with an optimal threshold calculated by the Otsu method. This method is benchmarked on several well-known data sets in the object detection and tracking domain, such as CAVIAR 2004, AVSS 2007, PETS 2009, PETS 2014, and CDNET 2014. We also compare the accuracy of the proposed method with other state-of-the-art methods via standard quantitative metrics under different parameter configurations. In the experiments, NIC approach outperforms several advanced methods on depressing the detected foreground confusions due to light artifact, illumination change, and camera jitter in dynamic scenes. [ABSTRACT FROM PUBLISHER]

This paper explores the problem of recovering the discriminative representation of a hyperspectral remote sensing image (HRSI), which suffers from spectral variations, to boost its classification accuracy. To tackle this challenge, we propose a new method, namely local-global balanced low-rank approximation (GLB-LRA), which can increase the similarity between pixels belonging to an identical category while promoting the discriminability between pixels of different categories. Specifically, by taking advantage of the particular structural spatial information of HRSIs, we exploit the low-rankness of an HRSI robustly in both spatial and spectral domains from the perspective of local and global balance. We mathematically formulate GLB-LRA as an explicit optimization problem and propose an iterative algorithm to solve it efficiently. Experimental results over three commonly-used benchmark datasets demonstrate the significant superiority of our method over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Depth estimation from a camera is an important task for 3D perception. Recently, without using the labeled ground truth of depth map, a self-supervised deep learning network can use relative pose to synthesize the target image from the reference image, and the photometric error between synthesized reference image and real one is used as self-supervisory signal. In this paper, we propose a novel self-supervised depth estimation network, which takes advantage of the quadtree constraint to optimize the depth estimation network. Based on the quadtree constraint, the photometric loss and depth loss of quadtree are proposed. In order to solve the problem that multiple depth values in repeated structures and uniform texture regions can cause relatively low photometric loss, we use quadtree-based photometric loss, which calculates the averaged photometric loss in quadtree blocks instead of the pixel-wise loss. For the problem of imbalanced depth distribution, we use quadtree depth loss, which constrains the depth inconsistency within quadtree blocks. The depth estimation network is composed of deep fusion module and cross-layer feature fusion module, which can better extract the feature information of RGB image and sparse keypoints depths, and makes full use of the detail information of the shallow feature map and the semantic information of the deep feature map to enrich the feature information extraction. Experimental results demonstrate that our method outperforms the state-of-the-art approaches of depth estimation. [ABSTRACT FROM AUTHOR]

High-resolution fingerprint recognition has been a hot topic for many years. Compared with a traditional fingerprint image, a high-resolution fingerprint image can provide more features, such as pores and ridge contours. Introducing these features into fingerprint comparison and recognition can improve the recognition accuracy and reduce the risk of identification errors. This paper proposes a novel method for comparing pores on high-resolution fingerprint images. The method can be divided into two steps. In the first step, fingerprints are aligned using the pixel-category-distance-based data-driven descending algorithm. Traditionally, fingerprints are aligned based on feature points, such as minutiae and singular points. Such alignment methods are not suitable when dealing with partial fingerprints because small overlapping areas often do not contain enough features to guarantee a correct alignment. In this research, the ridges and valleys on fingerprints are used in combination with the orientation field for alignment. The proposed algorithm performs well when aligning both partial and full fingerprints. The common areas between the two images can be estimated based on the alignment result. In the second step, pores lying in the common areas are selected for comparison. To improve the comparison accuracy, pores are compared using local features and spatial relations. A graph comparison algorithm is designed in this step. The experimental results show that the proposed method is more accurate than other state-of-the-art pore comparison algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method of reversible data hiding in encrypted images (RDH-EI). We first provide an RDH-EI approach using single-level embedding, in which three parties are involved, including an image owner, a data hider, and a recipient. The image owner encrypts an original image into a ciphertext image. After dividing the original image into blocks, the owner pseudo-randomly permutes all blocks by a permutation key. With an encryption key, the image owner further encrypts the contents of all blocks using a stream cipher algorithm, during which pixels inside each block share the same stream bytes. Once the encrypted image is uploaded onto the server, a data hider embeds additional messages into the ciphertext. The data hider divides the encrypted image into blocks and selects peak pixels from each block using an embedding key. With the peak pixels, the data hider embeds an additional message using histogram shifting inside each block. On the receiver side, a recipient extracts the hidden message using the embedding key, and losslessly recover the original image with the permutation key and the encryption key. Based on the single-level algorithm, we further construct a multi-level approach. The embedding process is iteratively used to generate the marked encrypted images. Compared with state-of-the-art works, the proposed method achieves a better embedding efficiency and an error-free recovery. [ABSTRACT FROM AUTHOR]

To ensure the confidentiality of color images during their storage or transmission on insecure networks, a number of encryption methods based on fractional transforms have been proposed and widely investigated. However, most of their outputs are complex values that are inconvenient for record and transmission. Also, those methods always deal with a whole color component with the same fractional-order and ignore the textural features that are contained in different image parts. In this paper, we first define a reality-preserving multiple-parameter fractional Hartley transform (RPMPFRHT), whose output is real value, and then a novel color image encryption method is proposed that divides the RGB components into different blocks and uses a constructed pixel scrambling operator to mix and hide the original color information. The outputs are transformed to different RPMPFRHT domains and further scrambled by a non-adjacent coupled map lattices system. Numerical simulations are performed to demonstrate that the proposed encryption algorithm is feasible, secure, sensitive to keys, and robust to potential attacks. [ABSTRACT FROM AUTHOR]

Some state-of-the-art binary image steganographic methods aim to generate stego images with good visual quality, while others focus more on the statistical security of the anti-steganalysis. This paper proposes a binary steganographic scheme that improves both of them by selecting more appropriate flipped pixels. First, a fused distortion measurement is developed that combines the advantages of flipping distortion measurement (FDM) and two data-carrying pixel location methods, including the edge adaptive grid method (EAG) and the “Connectivity Preserving” criterion (CPc). The FDM measures the distortion score by statistical features and achieves high-statistical security, while the EAG and CPc select pixels by analyzing the local texture structures based on visual quality. Then, to eliminate the interference brought by adjacent flipped pixels, a flipping position optimization strategy is proposed to find better positions for flipping pixels to further improve the steganographic performance. Experimental results have demonstrated that the proposed steganographic scheme can achieve stronger statistical security with better visual quality without degrading the embedding capacity. [ABSTRACT FROM AUTHOR]

Compared with conventional color images, light field images (LFIs) contain richer scene information, which allows a wide range of interesting applications. However, such additional information is obtained at the cost of generating substantially more data, which poses challenges to both data storage and transmission. In this paper, we propose a new hybrid framework for effective compression of LFIs. The proposed framework takes the particular characteristics of LFIs into account so that the inter- and intra-view correlations of LFIs can be more efficiently exploited to produce better compression performance. Specifically, the proposed scheme partitions sub-aperture images (SAIs) of an LFI into two groups, namely, key SAIs and non-key SAIs. Bi-level view compensation is proposed to exploit the inter-view correlation: first, based on the group of selected key SAIs, learning-based angular super-resolution is performed to compensate non-key SAIs in pixel-wise, during which heterogeneous inter-view correlation between the non-key SAIs is efficiently removed; second, the two groups of SAIs are respectively reorganized as pseudo-sequences, and block-wise motion compensation is carried out with a standard video encoder, during which the homogeneous inter-view correlation is subsequently exploited. The video encoder also helps to remove the intra-view correlation of the SAIs and finally generates the encoded bitstream. Moreover, the bits allocated to each group are optimally determined via model-based rate distortion optimization. Extensive experimental evaluations and comparisons demonstrate the advantage of the proposed framework over existing methods in terms of rate-distortion performance. [ABSTRACT FROM AUTHOR]

With the increasingly extensive applications of the new emerging video coding standard H.264, it inspires an urgent need to transcode the widely available MPEG-2 compressed video to H.264 format. In this paper, we investigate the issues on transcoding MPEG-2 into H.264 in transform domain with consideration of drift error due to the mismatch of motion compensation, and propose a transform domain solution to transcode MPEG-2 into H.264. We first analyze two major kinds of drifting error resulting from the mismatch of motion compensation: interpolation error and quantization error. The former is caused by the difference between the interpolation filters adopted in these two standards, thus very unique to the transform domain transcoding from MPEG-2 to H.264. Furthermore, it is identified as the dominant factor of the video quality degradation, especially in the case of small quantization parameter at high bit-rate, by extensive experimental results. As a major contribution of this paper, the close form of interpolation error is derived from transform domain. We then proposed the transcoding scheme based on quantization error drifting compensation and Interpolation error drifting compensation. Experimental results show that the proposed transform domain transcoding scheme achieves very promising performance in terms of low computational complexity and high transcoded video quality. Most importantly, its peak signal-to-noise ratio is very close to the cascaded transcoding architecture with time-consuming decoding and recoding process in pixel domain. [ABSTRACT FROM AUTHOR]

The 3D semantic map plays an increasingly important role in a wide variety of applications, especially for many kinds of task-driven robots. In this paper, we present a semantic mapping methodology for 3D semantic map obtaining from RGB-D scans. In contrast to existing methods that use 3D annotated information as supervisory, we focus on accurate 2D frame labeling and combine labels in 3D space using semantic fusion mechanism. For scene parsing, a two-stream network with a novel discriminatory mask loss is proposed to explore sufficient extraction and fusion of RGB and depth information achieving steadily semantic segmentation. The discriminatory mask guides the cross-entropy loss function and interprets the influence of different pixels on back-propagation, which reduces the harmful effects of the depth noise or the fallible annotation at the edges of objects. After the correspondences between frames are provided, these semantic frames are fused in unified 3D coordinates using the novel label-oriented voxelgrid filter. It can ensure the intra-frame spatial continuity and the inter-frame spatiotemporal consistency through introducing the label-oriented statistical principle into labeled point clouds. In order to avoid the unfavorable interference between uncorrelated frames, we further propose an adaptive grouping algorithm by applying the view frustum filter to group frames with sufficient overlap as a segment. To this end, we demonstrate the effectiveness of the proposed method on the 2D/3D semantic label benchmark of ScanNetv2 and Cityscapes datasets. [ABSTRACT FROM AUTHOR]

Motion-compensated frame interpolation (MCFI) usually finds the most matched blocks by minimizing pixel intensity discrepancies between neighboring frames along the motion trajectory. However, the quality of interpolated frames is susceptible to inaccurate motion vectors possibly for regions with complex texture patterns, irregularly shaped objects, repeated patterns, motion blurring or aliasing, and so on. It is believed that pixel intensity across adjacent frames varies gradually and smoothly, and therefore can be modeled mathematically by a continuous and differentiable function. Thus, the pixel intensity within one frame can be expressed as either a forward polynomial approximation (FWPA) or a backward polynomial approximation (BWPA) by the Taylor expansion in this paper. The discrepancy between the FWPA and BWPA is employed to find the best motion vector. In addition, a motion-aligned partial derivative is proposed to calculate the Taylor expansion along the motion trajectory. The proposed method is applicable to any existing MCFI schemes and achieve superior performance by consuming relatively more buffer memories and computational resources. Extensive experimentation with comparison with previous techniques validates our method in terms of both objective and subjective criteria. [ABSTRACT FROM PUBLISHER]

Background completion for consumer videos captured by free-moving cameras is a challenging problem. In this paper, we present a new approach to complete the holes left by removing objects with motion-guided pixels assignment optimization. We first estimate the motion field in the holes by applying a two-step motion propagation method. Then, using estimated motion field as guidance, the missing parts of the video are completed by performing pixels assignment optimization based on the Markov random field, which optimally assigns available pixels from other neighboring video frames to the missing regions. Finally, we present an illumination-adjusting approach to eliminate the illumination inconsistency in the completed holes. We validate our method on a variety of videos captured by free-moving cameras. Compared with previous methods, our method works better to keep the completed background spatiotemporally coherent, to complete video background with much depth discontinuity and to make the illumination consistent in the completed region. [ABSTRACT FROM PUBLISHER]

Salient object detection aims at detecting the most visually distinct objects and producing the corresponding masks. As the cost of pixel-level annotations is high, image tags are usually used as weak supervisions. However, an image tag can only be used to annotate one class of objects. In this paper, we introduce saliency subitizing as the weak supervision since it is class-agnostic. This allows the supervision to be aligned with the property of saliency detection, where the salient objects of an image could be from more than one class. To this end, we propose a model with two modules, Saliency Subitizing Module (SSM) and Saliency Updating Module (SUM). While SSM learns to generate the initial saliency masks using the subitizing information, without the need for any unsupervised methods or some random seeds, SUM helps iteratively refine the generated saliency masks. We conduct extensive experiments on five benchmark datasets. The experimental results show that our method outperforms other weakly-supervised methods and even performs comparable to some fully-supervised methods. [ABSTRACT FROM AUTHOR]

In video surveillance, the captured face images are usually of low resolution (LR). Thus, a framework based on singular value decomposition (SVD) for performing both face hallucination and recognition simultaneously is proposed in this paper. Conventionally, LR face recognition is carried out by super-resolving the LR input face first, and then performing face recognition to identify the input face. By considering face hallucination and recognition simultaneously, the accuracy of both the hallucination and the recognition can be improved. In this paper, singular values are first proved to be effective for representing face images, and the singular values of a face image at different resolutions have approximately a linear relation. In our algorithm, each face image is represented using SVD. For each LR input face, the corresponding LR and high-resolution (HR) face-image pairs can then be selected from the face gallery. Based on these selected LR–HR pairs, the mapping functions for interpolating the two matrices in the SVD representation for the reconstruction of HR face images can be learned more accurately. Therefore, the final estimation of the high-frequency details of the HR face images will become more reliable and effective. The experimental results demonstrate that our proposed framework can achieve promising results for both face hallucination and recognition. [ABSTRACT FROM AUTHOR]

Common action recognition methods describe an action sequence along with its time axis, i.e., first extracting features from the $x y$ plane, and then modeling the dynamic changes along with the time axis. Other than the ordinary $x y$ plane-based representation, other views, e.g., $x t$ slice-based representation, may be more efficient to distinguish different actions. In this paper, we investigate different slicing views of the spatiotemporal volume to organize action sequences and propose an efficient slice representation for human action recognition. First, a minimum average entropy principle is proposed to select the optimal slicing angle for each action sequence adaptively. This allows the foreground pixels to be distributed in the fewest slices so as to reduce more uncertainty caused by the information dispersed in different slices. Then, the obtained slice sequence is transformed into a pair of 1-D signals to describe the distribution of foreground pixels along the time axis. Finally, the mel frequency cepstrum coefficient features are calculated to describe the spectrum characteristics of the 1-D signals over time. Thus, a 3-D spatiotemporal action volume is efficiently transformed into a low-dimensional spectrum features. Extensive experiments on the 2-D human action data sets (the UIUC and the WEIZMANN) as well as the Microsoft Research (MSR) Action3-D depth data set demonstrate the effectiveness of the slice-based representation, where the recognition performance can reach to the state-of-the-art level with high efficiency. [ABSTRACT FROM PUBLISHER]

This paper presents a novel multidirectional weighted interpolation algorithm for color filter array interpolation. Our proposed method has two contributions to demosaicking. First, different from conventional interpolation methods based on two directions or four directions, the proposed method exploits to greater degree correlations among neighboring pixels along eight directions to improve the interpolation performance. Second, we propose an efficient postprocessing method to reduce interpolation artifacts based on the color difference planes. Compared with conventional state-of-the-art demosaicking algorithms, our experimental results show the proposed algorithm provides superior performance in both objective and subjective image quality. Furthermore, this implementation has moderate computational complexity. [ABSTRACT FROM PUBLISHER]

Visible light cameras commonly used for surveillance applications usually have a limited field of view (FoV). To acquire a broader FoV, distributed aperture systems (DASs) combine views from multiple cameras. Although some commercial or proprietary systems already exist, the open literature in this field reports solely the performance and/or hardware architecture of the respective systems, and omits the required details for a reimplementation. In this paper we present a low-cost, personal-computer-based 360° DAS, with a full description of the hardware architecture and the software implementation details. In particular, we describe in detail two problems, the offline estimation of the orientations of the cameras of the proposed DAS and the online synthesis of a virtual view in real time. For the first problem, we propose an area-based bundle adjuster by combining the forward additive Lucas–Kanade algorithm with a bundle adjustment strategy. The color virtual view of up to $2048\times 1024$ pixels is synthesized online at 39 frames/s. A large majority of the workload of the online phase is implemented on the graphic processing unit. The techniques and methods proposed in this paper are generic and independent on the arrangement of cameras. [ABSTRACT FROM PUBLISHER]

This paper presents a new image retargeting method based on seam searching and pixel fusion. First, the importance map of the original image, which is represented by the saliency of every pixel, is generated. Second, all the pixels in the original image are divided into $W$ (width of the original image) groups with the help of seam searching. Then, it performs inter-row coherence filtering on importance map to maintain the spatial coherence, and directly utilizes the filtered importance map to generate the scaling map. Finally, after pixel fusion with the scaling map, the image can be retargeted effectively. Experimental results show that our method is able to provide visually pleasing resized images. [ABSTRACT FROM AUTHOR]

This paper presents a new technique for hyperspectral image (HSI) classification by using superpixel guided deep-sparse-representation learning. The proposed technique constructs a hierarchical architecture by exploiting the sparse coding to learn the HSI representation. Specifically, a multiple-layer architecture using different superpixel maps is designed, where each superpixel map is generated by downsampling the superpixels gradually along with enlarged spatial regions for labeled samples. In each layer, sparse representation of pixels within every spatial region is computed to construct a histogram via the sum-pooling with $l_{1}$ normalization. Finally, the representations (features) learned from the multiple-layer network are aggregated and trained by a support vector machine classifier. The proposed technique has been evaluated over three public HSI data sets, including the Indian Pines image set, the Salinas image set, and the University of Pavia image set. Experiments show superior performance compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel reversible data hiding scheme for encrypted images by using homomorphic and probabilistic properties of Paillier cryptosystem. In the proposed method, groups of adjacent pixels are randomly selected, and reversibly embedded into the rest of the image to make room for data embedding. In each group, there are a reference pixel and a few host pixels. Least significant bits (LSBs) of the reference pixels are reset before encryption and the encrypted host pixels are replaced with the encrypted reference pixel in the same group to form mirroring ciphertext groups (MCGs). In such a way, the modification on MCGs for data embedding will not cause any pixel oversaturation in plaintext domain and the embedded data can be directly extracted from the encrypted domain. In an MCG, the reference ciphertext pixel is kept unchanged as a reference while data hider embeds the encrypted additional data into the LSBs of the host ciphertext pixels by employing homomorphic multiplication. On the receiver side, the hidden ciphertext data can be retrieved by employing a modular multiplicative inverse operation between the marked host ciphertext pixels and their corresponding reference ciphertext pixels, respectively. After that, the hidden data are extracted promptly by looking for a one-to-one mapping table from ciphertext to plaintext. Data extraction and image restoration can be accomplished without any error after decryption. Compared with the existing works, the proposed scheme has lower computation complexity, higher security performance, and better embedding performance. The experiments on the standard image files also certify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Many computer-vision and machine-learning applications in robotics, mobile, wearable devices, and automotive domains are constrained by their real-time performance requirements. This paper reports a dual-feature-based object recognition coprocessor that exploits both histogram of oriented gradient (HOG) and Haar-like descriptors with a cell-based parallel sliding-window recognition mechanism. The feature extraction circuitry for HOG and Haar-like descriptors is implemented by a pixel-based pipelined architecture, which synchronizes to the pixel frequency from the image sensor. After extracting each cell feature vector, a cell-based sliding window scheme enables parallelized recognition for all windows, which contain this cell. The nearest neighbor search classifier is, respectively, applied to the HOG and Haar-like feature space. The complementary aspects of the two feature domains enable a hardware-friendly implementation of the binary classification for pedestrian detection with improved accuracy. A proof-of-concept prototype chip fabricated in a 65-nm SOI CMOS, having thin gate oxide and buried oxide layers (SOTB CMOS), with 3.22-mm2 core area achieves an energy efficiency of 1.52 nJ/pixel and a processing speed of 30 fps for $1024\times 1616$ -pixel image frames at 200-MHz recognition working frequency and 1-V supply voltage. Furthermore, multiple chips can implement image scaling, since the designed chip has image-size flexibility attributable to the pixel-based architecture. [ABSTRACT FROM AUTHOR]

This paper investigates how to perform efficient and robust foreground detection in challenging scenarios by leveraging multiple source data. We propose a novel approach, called collaborative low-rank decomposition (CLoD), for grayscale-thermal foreground detection. Given two data matrices by accumulating sequential frames from the grayscale and the thermal videos, CLoD detects the foreground objects as sparse noises against the backgrounds with collaborative low rank structure, and also incorporates modality weights to achieve adaptive fusion of different source data. For the optimization, CLoD seeks a sub-optimal solution by making the background matrix rank explicitly determined. In particular, the background matrix with the fixed rank can be decomposed into two sub-matrices of low rank, and then, we iteratively optimize them and the modality weights with closed-form solutions. For improving the efficiency, we design a block-based accelerated algorithm to speed up CLoD while employing the edge-preserving algorithm to keep the accuracy. Extensive experiments on the recently public benchmark grayscale-thermal foreground detection suggest that our approach achieves comparable performance in terms of both accuracy and efficiency against other state-of-the-art methods. [ABSTRACT FROM AUTHOR]