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Constructing a good graph that can capture intrinsic data structures is critical for graph-based semi-supervised learning methods, which are widely applied for hyperspectral image (HSI) classification with small amount of labeled samples. Among the existing graph construction methods, sparse representation (SR)-based methods have shown impressive performance on semi-supervised HSI classification tasks. However, most SR-based algorithms fail to consider the rich spatial information of HSI, which has been shown beneficial for classification tasks. In this paper, we propose a spatial and class structure regularized sparse representation (SCSSR) graph for semi-supervised HSI classification. Specifically, spatial information has been incorporated into SR model via the graph Laplacian regularization, it assumes that the spatial neighbors should have similar representation coefficients, the obtained coefficient matrix thus can reflect the similarity between samples more accurately. Besides, we also incorporate probabilistic class structure, which implies the probabilistic relationship between each sample and each class, into SR model to further improve discriminability of graph. The experimental results on Hyperion and AVIRIS hyperspectral data show that our method outperforms state of the art methods. [ABSTRACT FROM AUTHOR]

The X-ray angiogram image denoising is always one of the most popular research in the field of computer vision. While the methods removed the noise, the useful structure (such as peripheral vascular) had also been smoothed, the fundamental reason is that the denoising methods cannot efficiently distinguish structural areas from flat areas. In this paper, we have proposed a spatially adaptive image denoising (SAID) method which contains two steps: spatially adaptive gradient descent (SAGD) image denoising and dual-domain filter (DDF). The SAGD denoising method contains the following parts: first of all, the wavelet shrinkage method is used to estimate redundant information which is composed of the noise and useful structures; secondly, according to the characteristic of second order matrix, a spatially adaptive gradient factor (SAGF) has been constructed to distinguish the structure from flat areas; finally, the SAGF replaces the original gradient factor and then the SAGD image denoising method is formed. To further improve the quality of the SAGD image, the SAGD image is re-denoised by a modified DDF which is guided with a rotationally invariant non-local filter (RINLF) in spatial domain and gets structural details by wavelet shrinkage in frequency domain. The results of simulation experiments verify that the proposed SAID method can get well quantitative and qualitative results which are even superior to those using the state-of-the-art denoising methods. Even more, the fluctuation of peak signal-to-noise ratio (PSNR) value is very small with a small disturbance of SAGF, which illustrates that our algorithm is more robust than the prior progressive image denoising (PID) method. Moreover, the comparison results of the extensive experiments on clinical X-ray cardiovascular angiogram images further illustrate that our method can yield clearer cardiovascular images which can provide more useful vascular information for clinicians to analyze and diagnose the cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Several dimensionality reduction methods based on the max-min idea have been proposed in recent years and can obtain good classification performance. In this paper, inspired by the idea, we develop max-min linear discriminant analysis (MMLDA), which maximizes the minimum ratio of each two-class scatter measure to the within-class scatter measure. However, the method ignores equal emphasis on the distances between class centers and there may be room to improve the classification performance. We then propose regularized max-min linear discriminant analysis (RMMLDA), which introduces the Shannon entropy and the corresponding distance difference regularization terms based on MMLDA. The changing trends of distances between class centers can be precisely controlled in optimization and the separation between classes can be emphasized approximately equally. As a result, RMMLDA may obtain better classification performance. Experiments on synthetic data sets and three publicly available data sets demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Graph-based semi-supervised learning (SSL), which performs well in hyperspectral image classification with a small amount of labeled samples, has drawn a lot of attention in the past few years. The key step of graph-based SSL is to construct a good graph to represent original data structures. Among the existing graph construction methods, sparse representation (SR) based methods have shown impressive performance on graph-based SSL. However, most SR based methods fail to take into consideration the class structure of data. In SSL, we can obtain a probabilistic class structure, which implies the probabilistic relationship between each sample and each class, of the whole data by utilizing a small amount of labeled samples. Such class structure information can help SR model to yield a more discriminative coefficients, which motivates us to exploit this class structure information in order to learn a discriminative graph. In this paper, we present a discriminative graph construction method called probabilistic class structure regularized sparse representation (PCSSR) approach, by incorporating the class structure information into the SR model, PCSSR can learn a discriminative graph from the data. A class structure regularization is developed to make use of the probabilistic class structure, and therefore to improve the discriminability of the graph. We formulate our problem as a constrained sparsity minimization problem and solve it by the alternating direction method with adaptive penalty (ADMAP). The experimental results on Hyperion and AVIRIS hyperspectral data show that our method outperforms state of the art. [ABSTRACT FROM AUTHOR]

It is essential to extract dynamic information about a patient's heart from a medical X-ray angiography image sequence for a quantitative medical diagnosis. As the motions included in the angiography sequences are the mixture of various motion signals, such as the body's integral translation, respiratory motion, cardiac impulse, and tremor, automatic separation of these signals challenges the effectiveness of the image information processing method. This paper has proposed an optimal time-frequency domains iteration separation algorithm for multi-motion parameters (TFISA-MMP) to obtain the physiological parameters including the translation. The main procedures of the TFISA-MMP algorithm include three parts. First, the algorithm automatically extracted a set of relatively stable branch points from the coronary artery angiography image, and then automatically tracked these branch points in the sequence image to obtain their motion curves that changed with the time. Second, with the guidance of the multi-motion parameter model, the initial values of each component were estimated based on Discrete Fourier Transformation (DFT). Moreover, the initial values of each motion component were optimized using the global mean square minimum error between the estimated reconstructed signal and original signal and the local mean square minimum error in the frequency domain for each frequency component. Finally, these motion components were estimated by minimizing the residual signal between the original signal and the reconstructed signal via the loop iteration to obtain the estimated optimal motion components, such as two-dimensional (2D) heartbeat, tremor, respiratory motion, and translational motion. Both visible human coronary model simulation experiments and the clinical experiments of single-arm X-ray angiography images of many individuals verified the correctness, validity, and clinical applicability of the separation method. [ABSTRACT FROM AUTHOR]

The goal of this work is to present a computational model for contour detection, based on the surround suppression mechanisms of the primary visual cortex, in which the strength of surround suppression can adaptively vary with contrast—the surround modulation tends to be clearly suppressive at high contrast and less suppressive at low contrast, which may help to achieve the tradeoff between reducing cluttered edges and retaining object structures with weak responses, thus improving performance of contour detection under different circumstances; on the other hand, for similar texels can engender stronger suppression effects, more than orientation feature, texton is introduced to homogeneity measurement, which can well character multiple properties of texture regions, thereby yielding better contour detection results than other suppression models. The study can provide useful suggestions for contour detection algorithm in computer vision, but may also contribute to understanding contrast influence and feature representation in non-classical receptive field inhibition. [ABSTRACT FROM AUTHOR]

We present a method for object detection combining the effectiveness of a set of mid-level parts. These parts are learned weak-supervised from object bounding box annotations. The approach based part models can handle the detection of objects across changes in viewpoint, intraclass variability and object deformation. The objects are localized by the detected parts with learned information of location and scale. We evaluate the detection method on the standard PASCAL VOC 2007 dataset. Our system is competitive with the state of art in localizing the object. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simple yet effective local color image descriptor, completed local similarity pattern (CLSP), for face recognition. We represent the color image as the co-occurrence of its image pixel color quantization information and the local color image textural information. Specifically, CLSP consists of two complementary components: color label and local similarity pattern. Expressed by soft color label for every image pixel, we adopt the k-means clustering method and the soft-assignment coding method to summarize and encode the image pixel color information globally ignoring local neighboring pixels. While based on the similarity of color information between central pixel and its neighbors, local similarity pattern (LSP) is used to encode the local spatial textural feature of the color image ignoring their color value. Therefore, LSP has the merits of robustness and compactness of coding based feature extraction method. The joint distribution (2-D histogram), which unifies the color label and LSP, is used for a given image representation. Moreover, seven different color spaces are selected and fused to compensate each other for color image feature extraction. Experimental results on GeorgiaTech, AR, NUST-RWFR and LFW face database show that CLSP outperforms state-of-the-art color image feature extraction methods. [ABSTRACT FROM AUTHOR]

This paper presents a two-level hierarchical multi-resolution image fusion method based on attention, which focus on combining multi-sensor images from visible and infrared spectrums into an image while maintaining the salient information necessary for human visual. Attention-based one-level fusion employs adaptive weighting fusion rules to obtain the initial fused image which may being the boundary of saliency regions, then two-level fusion based on nonsubsampled contourlet transform (NSCT) is used to hide possible imprints of the initial fused image. Firstly, for the given visible and infrared images from the same scene, salient regions are detected by visual attention model. Secondly, salient sizes are acquired by calculating maximum entropy. Thirdly, one-level fusion is achieved by the adoptive weighing fusion rules. Various weight values are adopted according to the visual saliency order from high to low. The initial fused image contains not only the salient information of visible image, but also the salient thermal target information of infrared image. Finally, two-level fusion based on NSCT is employed to fuse the visible source image, the infrared source image and the initial fused image, and the final fused image is obtained. Two different data sets from TNO, Human Factors are employed and the experimental results show that the proposed approach is efficient and also performed better than some existing methods. [ABSTRACT FROM AUTHOR]

Abstract: The double-random phase-encoding (DRPE) technique is a typical optical image encryption technique, which can also be used for image hiding. Usually, the secret image is encrypted with the DRPE technique and the encoded image is hidden into the host image via superimposition to obtain the stego-image. The attack technique on the DRPE-based image hiding method was proposed in this paper. Firstly, a randomly selected superimposition coefficient was used to approximate the original superimposition coefficient to extract the hidden encoded images from the stego-images approximately. Then, the chosen-plaintext attack technique on the DRPE-based optical image encryption technique was applied to recover the random phase masks used in the DRPE technique. The theoretical analysis indicated that, without considering the computational error, the recovered secret image via the proposed attack technique is identical to the original one. Even considering the computational error, it is identical to the secret image recovered with the original DRPE-based image hiding method, which demonstrates that the attack on the DRPE-based image hiding method is successfully achieved. The numerical simulation results demonstrated the correctness of the theoretical analysis. [Copyright &y& Elsevier]

Abstract: In this paper, we present a novel method to detect license plates in video sequences automatically. The framework mainly integrates the cascade detectors method and the Tracking Learning Detection (TLD) algorithm. The cascade detectors are used to detect license plates, and the TLD algorithm is adopted to track the license plate regions. The license plates in the first frame image are detected by the cascade detectors to build the original tracking list, the tracking results and the detection results in following frames will be compared, and the newly appearing license plate information will be added to the tracking list. Meanwhile, the tracking results existing in the current tracking list would be replaced by the corresponding detection results with higher degree of confidence. We demonstrate the effectiveness of our algorithm for license plate detection task on a road intersection dataset, and the experimental evaluation shows the detection performance has been greatly improved by synthetically using the detection and tracking strategy. [Copyright &y& Elsevier]

Abstract: Artificial vision systems are powerful tools for the automatic guiding of fruit harvesting robots, a novel method based on chromatic aberration map and luminance map is developed to identify citrus fruits with highlight within a tree canopy. Twenty images of citrus-grove scene under direct sunlight are taken, the color properties of target objects are analyzed. First, parts of citrus fruits from background are segmented from background by thresholding the CAM (chromatic aberration map), then the highlight region of citrus fruits could be detected correctly from the tree canopy by thresholding the LM (luminance map), at last the citrus fruits can be detected integrally by fusing the segmented results of CAM and LM. The results showed that the fruits under direct sunlight can be segmented wholly using this algorithm, the detection accuracy by fusion method is up to 86.81%, and the false alarm rate of fusion method is 2.25%. [Copyright &y& Elsevier]

Abstract: An adaptive enhancement algorithm is proposed in the paper for low illumination color image which is low brightness and low contrast. The algorithm is realized through three steps: the global brightness adaptive adjustment, locally adaptive contrast enhancement and color restore. Experiments show that the brightness and local contrast of low illumination color image can be effectively enhanced. The details, especially in the dark region of enhanced image are more prominent and the enhanced image is more vivid than some other methods for low illumination color image enhancement. [Copyright &y& Elsevier]

Highlights: [•] The multi-center initialization method can get rid of the problems that the FCM algorithm is sensitive to the initial prototypes, and it can handle non-traditional curved clusters. [•] The similarity matrix generated by the Floyd algorithm is notably block symmetric, which ensure the effective extraction of spectral features. [•] The problem of clustering samples is transformed to a problem of merging subclusters, the computational load is low, and has strong robustness. [ABSTRACT FROM AUTHOR]

Abstract: In the object recognition process of the remote sensing image, as the object data is influenced by the imaging scale, intensity and the shape of the object, the distribution of the object data is very different with that of known training data which leads to the low reliability of the object recognition. Aimed at the problem, an object recognition method based on transfer learning framework for the remote sensing image is proposed in this paper. The feature vectors of the object data are extracted by an improved LBP firstly, and then the transfer learning is used to find the common parameters among the feature spaces of the object data with the different distributions. The transfer learning method can transfer knowledge from the old object data to the new object data and improve the performance of the object recognition. According to the experiments in the satellite remote sensing images, it shows that the accuracy of object recognition has been greatly improved by our proposed method compared with the other classical methods. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a license plate detection method based on the gradient information and a cascade detection framework. The proposed method consists of three main modules: image preprocessing, license plate detection and license plate confirmation. Considering the diversities of Chinese license plate, we first get the gradient images to unify plate forms through image preprocessing. The license plate will be detected roughly by using the cascade AdaBoost classifier in the next step. Finally, several heuristic judgment strategies and a voting-based method are used to verify the candidate license plates. The effectiveness of our method is validated with an experimental study in license plate detection. [Copyright &y& Elsevier]

Abstract: Recently the sparse representation based classification (SRC) is successfully used to automatically recognize facial expression, well-known for its ability to solve occlusion and corruption problems. The results of those methods which using different features conjunction with SRC framework show state of the art performance on clean or noised facial expression images. Therefore, the role of feature extraction for SRC framework will greatly affect the success of facial expression recognition (FER). In this paper, we select a new feature which called LBP map. This feature is generated using local binary pattern (LBP) operator. It is not only robust to gray-scale variation, but also extracts sufficient texture information for SRC to deal with FER problem. Then we proposed a new method using the LBP map conjunction with the SRC framework. Firstly, we compared our method with state of the art published work. Then experiments on the Cohn–Kanade database show that the LBP map+SRC can reach the highest accuracy with the lowest time-consuming on clean face images than those methods which use different features such as raw image, Downsample image, Eigenfaces, Laplacianfaces and Gabor conjunction with SRC. We also experiment the LBP map+SRC to recognize face image with partial occluded and corrupted, the result shows that this method is more robust to occlusion and corruption than existing methods based on SRC framework. [Copyright &y& Elsevier]

Abstract: We propose a new method to estimate image bias field by an intuitive assumption that the overall trend of image surface is associated with bias field and the true bias field can evolve from image surface. To constrain the evolution, an energy function is designed based on two diffusion constraints. One is that the bias-free image has concise representation with image gradients. The other constraint is that model fit on the bias field which is more relaxed than previous methods. The new algorithm enables adaptive specifications of the estimated bias field's smoothness. The experimental results show our new technique can be applied to intensity inhomogeneity correction of medical image effectively. [Copyright &y& Elsevier]

Abstract: In this paper, we propose an Instance Transfer Boosting (ITB) framework for object tracking. The proposed tracking framework tries to transfer prior knowledge from the first frame and frame t-2 regarded as source instance to frame t-1 approximately as target instance. Those instances build the online training classifier used in tracking-by-detection for frame t. The novel method presents the tracking task in the current frame from the knowledge transferred by the first frame and the previous two frames, resulting in a more robust tracker for distinguishing the object from the background. Experimental results on several public video sequences demonstrated promising performance of the proposed tracking framework in both tracking accuracy and stability. [Copyright &y& Elsevier]

Abstract: Automatic image segmentation is always a fundamental but challenging problem in computer vision. The simplest approach to image segmentation may be clustering feature vectors of pixels at first, then labeling each pixel with its corresponding cluster. This requires that the clustering on feature space must be robust. However, most of popular clustering algorithms could not obtain a robust clustering result yet, if the clusters in feature space have a complex distribution. Generally, for most of clustering-based segmentation methods, it still needs more constraints of positional relations between pixels in image lattice to be utilized during the procedure of clustering. Our works in this paper address the problem of image segmentation under the paradigm of pure clustering-then-labeling. A robust clustering algorithm which could maintain good coherence of data in feature space is proposed and utilized to do clustering on the L∗a∗b∗ color feature space of pixels. Image segmentation is straightforwardly obtained by setting each pixel with its corresponding cluster. Further, based on the theory of Minimum Description Length, an effective approach to automatic parameter selection for our segmentation method is also proposed. We test our segmentation method on Berkeley segmentation database, and the experimental results show that our method compares favorably against some state-of-the-art segmentation methods. [Copyright &y& Elsevier]

Abstract: The paper presents a versatile nonlinear diffusion method to visually enhance the angiogram images for improving the clinical diagnosis. Traditional nonlinear diffusion has been shown very effective in edge-preserved smoothing of images. However, the existing nonlinear diffusion models suffer several drawbacks: sensitivity to the choice of the conductance parameter, limited range of edge enhancement, and the sensitivity to the selection of evolution time. The new anisotropic diffusion we proposed is based on facet model which can solve the issues mentioned above adaptively according to the image content. This method uses facet model for fitting the image to reduce noise, and uses the sum square of eigenvalues of Hessian as the standard of the conductance parameter selection synchronously. The capability of dealing with noise and conductance parameter can also change adaptively in the whole diffusion process. Moreover, our method is not sensitive to the choice of evolution time. Experimental results show that our new method is more effective than the original anisotropic diffusion. [Copyright &y& Elsevier]

Abstract: The responses of neurons in the primary visual cortex (V1) to stimulus inside the receptive field (RF) can be markedly modulated by stimuli outside the classical receptive field. The modulation, relying on contextual configurations, yields excitatory and inhibitory activities. The V1 neurons compose a functional network by lateral interactions and accomplish specific visual tasks in a dynamic and flexible fashion. Well-organized structures and conspicuous image locations are more salient and thus can pop out perceptually from the background. The excitatory and inhibitory activities give different visual physiological interpretations to the two kinds of saliencies. A model of contour extraction, inspired by visual cortical mechanisms of perceptual grouping, is presented. We unify the dual processes of spatial facilitation and surround inhibition to extract salient contours from complex scenes, and in this way coherent spatial configurations and region boundaries could stand out from their surround. The proposed method can selectively retain object contours, and meanwhile can dramatically reduce non-meaningful elements resulting from a texture background. This work gives a clear understanding for the roles of the inhibition and facilitation in grouping, and provides a biologically motivated computational strategy for contour extraction in computer vision. [Copyright &y& Elsevier]

Vessel segmentation is the base of three dimensional reconstruction on digital subtraction angiography (DSA) images. In this paper we propose two simple but efficient methods of vessel segmentation for DSA images. The original DSA image is divided into several appropriate subimages according to a prior knowledge of the diameter of vessels. We introduce the vessels existence measure to determine whether each subimage contains vessels and then choose an optimal threshold, respectively, for every subimage previously determined to contain vessels. Finally, an overall binarization of the original image is achieved by combining the thresholded subimages. Experiments are implemented on cerebral and hepatic DSA images. The results demonstrate that our proposed methods yield better binary results than global thresholding methods and some other local thresholding methods do. [Copyright &y& Elsevier]

A contour detection model, inspired by the behavior of the primary visual cortex, is presented. The response of a central stimulus in the receptive field is affected by the presence of surrounding stimuli – for some stimulus conditions, the response is suppressed and for other conditions the response is enhanced. The visual mechanisms of contextual influences are utilized to extract “coherent” configurations. This is mainly due to the following two reasons: (1) on the one hand, a smooth contour can yield collinear excitation, which highlights smooth contours from irregularly textured surround; (2) on the other hand, similar orientation textures receive iso-orientation surround suppression and region boundary is subjected to the less inhibition, which makes boundary more salient for perceptual pop-out. Accordingly, smooth contours progressively stand out from their surround and at the same time textures are gradually suppressed by their surround through dynamic fine-tuning of contextual information. The proposed method which distinguishes between contours and texture edges is more effective for contour-based object recognition tasks. Initial experiments show that the model can be successfully applied to contour detection. Especially, when object contours are lumped together with unwantedly cluttered surround, the advantage of our approach is more prominent. This study provides a biological scheme for contour detection in computer vision. [Copyright &y& Elsevier]

Video synopsis is an intelligent condensation approach to solve fast video browsing and retrieval for surveillance cameras. However, collision caused by unsatisfied tube rearrangement in traditional methods brings uncomfortable visual effect to users and how to mitigate the collision still remains an attracting topic. Unlike conventional methods that deal with tube rearrangement by minimizing a global energy function, we propose a novel approach by formulating it as a graph coloring problem. In our approach, all the tubes are firstly mapped into the spatial domain for analyzing their potential collision relationship. The input tube set is then represented by a graph structure, where each node stands for a tube and the edge between two nodes represents the potential collision relationship. To mitigate the collision artifacts, our method finds the mapping of tubes from original video to synopsis video by L ( q )- coloring the graph, which separates tubes from their collision points. The parameter q is left tunable to make a compromise between collision artifacts and synopsis length, which can better meet users’ demand of freely adjusting the compactness of synopsis video. The shifted objects are finally composited with the background image to obtain the high-quality video synopsis. Extensive experimental results show that the proposed method can generate more compact video synopsis with less collision artifacts than the existing methods. [ABSTRACT FROM AUTHOR]

Few-shot classification (FSC) is a highly challenging task, as only a small number of labeled samples are available when identifying new categories. Distance metric learning-based methods have emerged as a prominent approach to FSC, which typically use a distance function to measure the difference between query and support samples for identifying the class membership of the query sample. However, these methods simply treat each channel difference between query and support features equally when computing the class scores. Since different channels in the learned feature seek for different patterns, these distance metrics fail to consider that different channels are of different importance to FSC, and thus cannot accurately measure the similarity between samples. To address this issue, we propose a Q uery- C entric D istance M odulator (QCDM) to generate query-related weights for each channel difference adaptively. Specifically, since the distribution of difference between a query sample and all support samples in a particular channel can reflect the importance of this channel to the classification of the query sample, we take this difference vector as input and generate a query-specific channel weight through a meta-network. QCDM can guide FSC models to focus on discriminative channel differences and achieve better generalization. QCDM is a plug-and-play module that can be seamlessly integrated with existing distance metric learning-based FSC methods. Extensive experimental results indicate that our method can effectively improve the performance of distance metric learning-based FSC methods. The source code is available in https://github.com/Wu-Wenxiao/QCDM. • A plug-and-play query-centric distance modulator for distance metric learning-based FSC methods. • Weight generation through data inconsistency within each channel from different classes. • Decoupled from the process of feature extraction and integrated with any given backbone. [ABSTRACT FROM AUTHOR]

Few-shot action recognition is a challenging but practical problem aiming to learn a model that can be easily adapted to identify new action categories with only a few labeled samples. However, existing attempts still suffer from two drawbacks: (i) learning individual features without considering the entire task may result in limited representation capability, and (ii) existing alignment strategies are sensitive to noises and misaligned instances. To handle the two limitations, we propose a novel Hybrid Relation guided temporal Set Matching (HyRSM++) approach for few-shot action recognition. The core idea of HyRSM++ is to integrate all videos within the task to learn discriminative representations and involve a robust matching technique. To be specific, HyRSM++ consists of two key components, a hybrid relation module and a temporal set matching metric. Given the basic representations from the feature extractor, the hybrid relation module is introduced to fully exploit associated relations within and cross videos in an episodic task and thus can learn task-specific embeddings. Subsequently, in the temporal set matching metric, we carry out the distance measure between query and support videos from a set matching perspective and design a bidirectional Mean Hausdorff Metric to improve the resilience to misaligned instances. Furthermore, we extend the proposed HyRSM++ to deal with the more challenging semi-supervised few-shot action recognition and unsupervised few-shot action recognition tasks. Experimental results on multiple benchmarks demonstrate that our method consistently outperforms existing methods and achieves state-of-the-art performance under various few-shot settings. The source code is available at https://github.com/alibaba-mmai-research/HyRSMPlusPlus. • A new temporal coherence regularization on videos is proposed. • Capturing the intra- and inter-relations inside the episodic task. • Reformulating the query-support metric as a set matching problem. [ABSTRACT FROM AUTHOR]

Abstract: A double-random phase-encoding technique based information hiding scheme was analyzed and improved. By encoding the hidden image with the double-random phase-encoding technique, and embedding the encoded image into the enlarged host image to generate the composed image, the original scheme can recover the hidden image from the composed image directly (i.e., without using the original host image) with satisfying quality. For the proposed improved scheme, 3 values (other than 2 values as in the original scheme) are embedded into each 2×2 block within the enlarged host image, resulting in increasing the data hiding capacity around 50%, while not influence the qualities of the composed image and the reconstructed hidden image seriously. In addition, the security of the original scheme is enhanced markedly. Theoretical analysis and experimental results demonstrate the effectiveness of the improved scheme. [Copyright &y& Elsevier]

Image matching aims to find a similar area of the small image in the large image, which is one of the key steps in image fusion and vision-based navigation; however, most matching methods perform poorly when the images to be matched are blurred. Traditional approaches for blurred image matching usually follow a two-stage framework - first resorting to image deblurring and then performing image matching with the recovered image. However, the matching accuracy of these methods often suffers greatly from the deficiency of image deblurring. Recently, a joint image deblurring and matching method that utilizes the sparse representation prior to exploit the correlation between deblurring and matching was proposed to address this problem and found to obtain a higher matching accuracy. Yet, that technique is not efficient when the image is seriously blurred, and the method's time complexity is excessive. In this paper, we propose a joint image deblurring and matching approach with a feature-based sparse representation prior. Our approach utilizes two-directional two-dimensional (2 D)2PCA to extract feature vectors from images and obtains a sparse representation prior in a robust feature space rather than the original pixel space, thus mitigating the influence of image blur. Moreover, the reduction in the feature dimension can also increase the computational efficiency. Extensive experiments show that our approach significantly outperforms state-of-the-art approaches in terms of both accuracy and speed. [ABSTRACT FROM AUTHOR]