A Brownian cellular automaton (BCA) is an asynchronous cellular automaton (ACA) in which local configurations representing signals may move forth and back randomly, as if they were undergoing random walks. The random fluctuation offers a natural mechanism to propagate signals in the 2-dimensional cell space, and to cross signals moving in directions perpendicular to each other. As a result, the BCA in (Lee et al., 2016) employs 4 cell states and 17 transition rules to conduct universal computation, both of which are less than other equivalent ACAs in the literature. This paper aims to advance the fluctuation-based scheme one step further, via proposing a new BCA with 4 states and 14 rules that achieves a reduction in the number of transition rules. We show that the BCA is capable of implementing any arbitrary logic circuit, thereby proving its universality in computation. We illustrate this by implementing a circuit that converts a 4-bit number to its equivalent hexadecimal digit.

We present a novel algorithm that exploits joint optimization of representation and classification for robust tracking in which the goal is to minimize the least-squares reconstruction errors and discriminative penalties with regularized constraints. In this formulation, an object is represented by the sparse coefficients of local patches based on an overcomplete dictionary, and a classifier is learned to discriminate the target object from the background. To locate the target object in each frame, we propose a deterministic approach to solve the optimization problem. We show that the proposed algorithm can be considered as a generalization of several tracking methods with effectiveness. To account for appearance change of the target and the background, the classifier is adaptively updated with new tracking results. Compared with the most recent tracking algorithms based on sparse representation, the proposed formulation has more discriminative power due to the use of background information and is much faster due to the use of deterministic optimization. Qualitative and quantitative experiments on a variety of challenging sequences show favorable performance of the proposed algorithm against several state-of-the-art methods.

One-class classification (OCC) builds models using only the samples from one class (the target class) so as to predict whether a new-coming sample belongs to the target class or not. OCC widely exists in many application fields, such as fault detection. As an effective tool for OCC, one-class SVM (OCSVM) with the Gaussian kernel has received much attention recently. However, its kernel parameter selection greatly affects its performance and is still an open problem. This paper proposes a novel method to solve this problem. First, an effective way is presented to measure the distances from the samples to the OCSVM enclosing surfaces. Then based on this measurement, an optimization objective function for the parameter selection is put forward. Extensive experiments are conducted on various data sets, and the results verify the effectiveness of the proposed method.

Gaussian processes (GPs) provide predicted outputs with a full conditional statistical description, which can be used to establish confidence intervals and to set hyperparameters. This characteristic provides GPs with competitive or better performance in various applications. However, the specificity of one-class classification (OCC) makes GPs unable to select suitable hyperparameters in their traditional way. This brief proposes to select hyperparameters for GP OCC using the prediction difference between edge and interior positive training samples. Experiments on 2-D artificial and University of California benchmark data sets verify the effectiveness of this method.

As one of the methods to solve one-class classification problems (OCC), one-class support vector machines (OCSVM) have been applied to fault detection in recent years. Among all the kernels available for OCSVM, the Gaussian kernel is the most commonly used one. The selection of Gaussian kernel parameters influences greatly the performances of classifiers, which remains as an open problem. In this paper two methods are proposed to select Gaussian kernel parameters in OCSVM: according to the first one, the parameters are selected using the information of the farthest and the nearest neighbors of each sample; using the second one, the parameters are determined via detecting the “tightness” of the decision boundaries. The two proposed methods are tested on UCI data sets and Tennessee Eastman Process benchmark data sets. The results show that, the two proposed methods can be used to select suitable parameters for the Gaussian kernel, enabling the resulting OCSVM models to perform well on fault detection.

In this paper, we propose a reweighted low-rank matrix recovery method and demonstrate its application for robust image restoration. In the literature, principal component pursuit solves low-rank matrix recovery problem via a convex program of mixed nuclear norm and l1 norm. Inspired by reweighted l1 minimization for sparsity enhancement, we propose reweighting singular values to enhance low rank of a matrix. An efficient iterative reweighting scheme is proposed for enhancing low rank and sparsity simultaneously and the performance of low-rank matrix recovery is prompted greatly. We demonstrate the utility of the proposed method both on numerical simulations and real images/videos restoration, including single image restoration, hyperspectral image restoration, and background modeling from corrupted observations. All of these experiments give empirical evidence on significant improvements of the proposed algorithm over previous work on low-rank matrix recovery.

Identifying causal structures from observations is fundamental in many applications. Probabilistic graphical models provide a unifying framework for capturing complex causal dependencies among random variables. Recently, a linear non-Gaussian acyclic model (LiNGAM) and some smart algorithms (ICA-LiNGAM, DirectLiNGAM) have been proposed, which outperform previous graphical models and learning methods in identifying variable orders. We propose new solutions (TMFLiNGAM, SchurLiNGAM and RCLiNGAM) to the LiNGAM learning task from the perspective of matrix identification. TMFLiNGAM and SchurLiNGAM recover orders more directly, and RCLiNGAM can improve the accuracy of previous algorithms on uniform and sparse structures. The perspective also facilitates the learning of sparse models where the performance of all independent component analysis-based algorithms can be improved by reconstructing variable orders from the inverse of separation matrices. Experimental results under various settings provide average evaluations over the learning methods, and verify the effectiveness of our perspective and algorithms.

Shape from defocus (SFD) is one of the most popular techniques in monocular 3D vision. While most SFD approaches require two or more images of the same scene captured at a fixed view point, this paper presents an efficient approach to estimate absolute depth from a single defocused image. Instead of directly measuring defocus level of each pixel, we propose to design a sequence of aperture-shape filters to segment a defocused image by defocus level. A boundary-weighted belief propagation algorithm is employed to obtain a smooth depth map. We also give an estimation of depth error. Extensive experiments show that our approach outperforms the state-of-the-art single-image SFD approaches both in precision of the estimated absolute depth and running time.

We study the marginal-MAP problem on graphical models, and present a novel approximation method based on direct approximation of the sum operation. A primary difficulty of marginal-MAP problems lies in the non-commutativity of the sum and max operations, so that even in highly structured models, marginalization may produce a densely connected graph over the variables to be maximized, resulting in an intractable potential function with exponential size. We propose a chain decomposition approach for summing over the marginalized variables, in which we produce a structured approximation to the MAP component of the problem consisting of only pairwise potentials. We show that this approach is equivalent to the maximization of a specific variational free energy, and it provides an upper bound of the optimal probability. Finally, experimental results demonstrate that our method performs favorably compared to previous methods.

We consider the problem of super-resolution from unregistered aliased images with unknown spatial scaling factors and shifts. Due to the limitation of pixel size in the image sensor, the sampling rate for each image is lower than the Nyquist rate of the scene. Thus, we have aliasing in captured images, which makes it hard to register the low-resolution images and then generate a high-resolution image. To work out this problem, we formulate it as a multichannel sampling and reconstruction problem with unknown parameters, spatial scaling factors and shifts. We can estimate the unknown parameters and then reconstruct the high-resolution image by solving a nonlinear least square problem using the variable projection method. Experiments with synthesized 1-D signals and 2-D images show the effectiveness of the proposed algorithm.

We propose an online algorithm based on local sparse representation for robust object tracking. Local image patches of a target object are represented by their sparse codes with an over-complete dictionary constructed online, and a classifier is learned to discriminate the target from the background. To alleviate the visual drift problem often encountered in object tracking, a two-stage algorithm is proposed to exploit both the ground truth information of the first frame and observations obtained online. Different from recent discriminative tracking methods that use a pool of features or a set of boosted classifiers, the proposed algorithm learns sparse codes and a linear classifier directly from raw image patches. In contrast to recent sparse representation based tracking methods which encode holistic object appearance within a generative framework, the proposed algorithm employs a discrimination formulation which facilitates the tracking task in complex environments. Experiments on challenging sequences with evaluation of the state-of-the-art methods show effectiveness of the proposed algorithm.

Principal component analysis (PCA) is fundamental in many pattern recognition applications. Much research has been performed to minimize the reconstruction error in L1-norm based reconstruction error minimization (L1-PCA-REM) since conventional L2-norm based PCA (L2-PCA) is sensitive to outliers. Recently, the variance maximization formulation of PCA with L1-norm (L1-PCA-VM) has been proposed, where new greedy and nongreedy solutions are developed. Armed with the gradient ascent perspective for optimization, we show that the L1-PCA-VM formulation is problematic in learning principal components and that only a greedy solution can achieve robustness motivation, which are verified by experiments on synthetic and real-world datasets.

This paper studies the problem of simultaneously aligning a batch of linearly correlated images despite gross corruption (such as occlusion). Our method seeks an optimal set of image domain transformations such that the matrix of transformed images can be decomposed as the sum of a sparse matrix of errors and a low-rank matrix of recovered aligned images. We reduce this extremely challenging optimization problem to a sequence of convex programs that minimize the sum of l1-norm and nuclear norm of the two component matrices, which can be efficiently solved by scalable convex optimization techniques. We verify the efficacy of the proposed robust alignment algorithm with extensive experiments on both controlled and uncontrolled real data, demonstrating higher accuracy and efficiency than existing methods over a wide range of realistic misalignments and corruptions.

We propose an object tracking algorithm that learns a set of appearance models for adaptive discriminative object representation. In this paper, object tracking is posed as a binary classification problem in which the correlation of object appearance and class labels from foreground and background is modeled by partial least squares (PLS) analysis, for generating a low-dimensional discriminative feature subspace. As object appearance is temporally correlated and likely to repeat over time, we learn and adapt multiple appearance models with PLS analysis for robust tracking. The proposed algorithm exploits both the ground truth appearance information of the target labeled in the first frame and the image observations obtained online, thereby alleviating the tracking drift problem caused by model update. Experiments on numerous challenging sequences and comparisons to state-of-the-art methods demonstrate favorable performance of the proposed tracking algorithm.

Visual prior from generic real-world images can be learned and transferred for representing objects in a scene. Motivated by this, we propose an algorithm that transfers visual prior learned offline for online object tracking. From a collection of real-world images, we learn an overcomplete dictionary to represent visual prior. The prior knowledge of objects is generic, and the training image set does not necessarily contain any observation of the target object. During the tracking process, the learned visual prior is transferred to construct an object representation by sparse coding and multiscale max pooling. With this representation, a linear classifier is learned online to distinguish the target from the background and to account for the target and background appearance variations over time. Tracking is then carried out within a Bayesian inference framework, in which the learned classifier is used to construct the observation model and a particle filter is used to estimate the tracking result sequentially. Experiments on a variety of challenging sequences with comparisons to several state-of-the-art methods demonstrate that more robust object tracking can be achieved by transferring visual prior.

Three-dimensional motion estimation from multiview video sequences is of vital importance to achieve high-quality dynamic scene reconstruction. In this paper, we propose a new 3-D motion estimation method based on matrix completion. Taking a reconstructed 3-D mesh as the underlying scene representation, this method automatically estimates motions of 3-D objects. A "separating + merging" framework is introduced to multiview 3-D motion estimation. In the separating step, initial motions are first estimated for each view with a neighboring view. Then, in the merging step, the motions obtained by each view are merged together and optimized by low-rank matrix completion method. The most accurate motion estimation for each vertex in the recovered matrix is further selected by three spatiotemporal criteria. Experimental results on data sets with synthetic motions and real motions show that our method can reliably estimate 3-D motions.

Learning Markov boundaries from data without having to learn a Bayesian network first can be viewed as a feature subset selection problem and has received much attention due to its significance in the wide applications of AI techniques. Popular constraint based methods suffer from high computational complexity and are usually unstable in spaces of high dimensionality. We propose a new perspective from matroid theory towards the discovery of Markov boundaries of random variable in the domain, and develop a learning algorithm which guarantees to recover the true Markov boundaries by a greedy learning algorithm. Then we use the precision matrix of the original distribution as a measure of independence to make our algorithm feasible in large scale problems, which is essentially an approximation of the probabilistic relations with Gaussians and can find possible variables in Markov boundaries with low computational complexity. Experimental results on standard Bayesian networks show that our analysis and approximation can efficiently and accurately identify Markov boundaries in complex networks from data.

We propose a new markerless shape and motion capture approach from multiview video sequences. The shape recovery method consists of two steps: separating and merging. In the separating step, the depth map represented with a point cloud for each view is generated by solving a proposed variational model, which is regularized by four constraints to ensure the accuracy and completeness of the reconstruction. Then, in the merging step, the point clouds of all the views are merged together and reconstructed into a 3-D mesh using a marching cubes method with silhouette constraints. Experiments show that the geometric details are faithfully preserved in each estimated depth map. The 3-D meshes reconstructed from the estimated depth maps are watertight and present rich geometric details, even for non-convex objects. Taking the reconstructed 3-D mesh as the underlying scene representation, a volumetric deformation method with a new positional-constraint computation scheme is proposed to automatically capture motions of the 3-D object. Our method can capture non-rigid motions even for loosely dressed humans without the aid of markers.

This paper proposes a robust tracking algorithm by third-order tensor representation and adaptive appearance modeling. In this method, the target in each video frame is represented by a third-order tensor. This representation preserves the spatial correlation inside the target region and can integrate multiple appearance cues for target description. Based on this representation, a multilinear subspace is learned online to model the target appearance variations during tracking. Compared to other methods, our approach can detect local spatial structure in the target tensor space and fuse information from different feature spaces. Therefore, the learned appearance model is more discriminative when there are significant appearance variations of the target or when the background gets cluttered. Applying the multilinear algebra, our appearance model can efficiently be learned and updated online, without causing high-dimensional data-learning problems. Then, tracking is implemented in the Bayesian inference framework, where a likelihood model is defined to measure the similarity between a test sample and the learned appearance model, and a particle filter is used to recursively estimate the target state over time. Theoretic analysis and experiments compared with other state-of-the-art methods demonstrate the effectiveness of the proposed approach.

This paper studies the problem of simultaneously aligning a batch of linearly correlated images despite gross corruption (such as occlusion). Our method seeks an optimal set of image domain transformations such that the matrix of transformed images can be decomposed as the sum of a sparse matrix of errors and a low-rank matrix of recovered aligned images. We reduce this extremely challenging optimization problem to a sequence of convex programs that minimize the sum of ℓ1-norm and nuclear norm of the two component matrices, which can be efficiently solved by scalable convex optimization techniques with guaranteed fast convergence. We verify the efficacy of the proposed robust alignment algorithm with extensive experiments with both controlled and uncontrolled real data, demonstrating higher accuracy and efficiency than existing methods over a wide range of realistic misalignments and corruptions.

This paper proposes a new color calibration method for multi-camera systems with a novel omnidirectional color checker. The designed cylindrical color checker contains a periodic array of color patches, and is visible for all the cameras without manual adjustment. For color calibration, accurate global correspondences are first generated by local descriptors and area-based correlation methods. Then, the multi-camera color calibration problem is formulated as an overdetermined linear system, in which the dynamic range shaping is incorporated to ensure the high contrasts of captured images. The cameras are calibrated with the parameters obtained by solving the linear system. According to experimental results on a real multi-camera system, the proposed method shows high performance in achieving inter-camera color consistency and high dynamic range.

This paper proposes a robust visual tracking approach based on saliency selection. In this method, salient patches and their spatial context inside the object region are exploited for object representation and appearance modeling. Tracking is then implemented by a hybrid stochastic and deterministic mechanism, which needs a small number of samples for particle filtering and escapes local minimum in conventional deterministic tracking. As time progresses, the selected salient patches and their spatial context are updated online to adapt the appearance model to both object and environmental changes. We carry out experiments on several challenging sequences and compare our method with the state-of-the-art algorithm to show its improvement in terms of tracking performance.

As an integral part of real-time vision system, there are two most important requirements for feature matching mechanisms: high computational efficiency for meeting the real-time demands, and high correct matching rate for ensuring the convergence and consistency of state estimation. Both of these are addressed and solved as an integrated whole by the efficient minimum-error active matching scheme proposed in this paper. Image processing is performed in a dynamically guided fashion by checking only parts of the image where positive matches are most probable. For achieving the global consensus matchings, rigorous analysis on how to minimize the matching errors in active matching by choosing an optimal search order is made. After that, practical feature matching algorithms are given, which have naturally absorbed the ideas of nearest neighbor (NN) and joint compatibility branch and bound (JCBB) approaches. Both statistical simulations and real-world experimental results have verified the proposed methods can perform better than the state-of-the-art algorithms, i.e. being able to obtain the best global consensus matchings with much lower computational cost.

When contourlet coefficients are directly coded, the benefits from directional representation are discounted by the redundancy of the transform. In this paper, we propose a contourlet-based image coding scheme under the sparse representation framework, in which contourlet coefficients are sparsified by the iterative thresholding method before compression. Dependency analysis is performed to reveal correlations among sparsified contourlet coefficients. Considering the characteristics of coefficient correlations, the obtained coefficients are coded with an intra-subband coder. Experimental results show that the coding performances of the contourlet-based schemes are significantly boosted via sparse representations.

In the matching tasks of tracking and geometrical vision, there are usually priors available on the absolute and/or relative image locations of features of interest. In this paper, we use these priors dynamically to guide a feature by feature matching search that can achieve global matching with much fewer image processing operations and lower overall computational cost. First, the concept of dynamic sequential search (DSS) is presented. Then, the problem of determining an optimal search order for DSS is investigated, when the probabilistic distribution of the features can be described by a multivariate Gaussian model. Based on the general formulas for sequentially updating the predicted positions of the features as well as their innovation covariance, the theoretic lower bound for the sum of the areas of the features search-regions is derived, and the necessary and sufficient condition for the optimal search order to approach this lower bound is presented. After that, an algorithm for dynamically determining a suboptimal search order is presented, with a computational complexity of O(n3), which is two magnitudes lower than those of the state-of-the-art algorithms. The effectiveness of the proposed method is validated by both statistical simulation and real-world experiments with a monocular visual SLAM (simultaneous localization and mapping) system. The results verify that the performance of the proposed method is better than the state-of-the-art algorithms, with both fewer image processing operations and lower overall computational cost.

This paper presents a robust multiview stereo (MVS) algorithm for free-viewpoint video. Our MVS scheme is totally point-cloud-based and consists of three stages: point cloud extraction, merging, and meshing. To guarantee reconstruction accuracy, point clouds are first extracted according to a stereo matching metric which is robust to noise, occlusion, and lack of texture. Visual hull information, frontier points, and implicit points are then detected and fused with point fidelity information in the merging and meshing steps. All aspects of our method are designed to counteract potential challenges in MVS data sets for accurate and complete model reconstruction. Experimental results demonstrate that our technique produces the most competitive performance among current algorithms under sparse viewpoint setups according to both static and motion MVS data sets.

Depth-map merging approaches have become more and more popular in multi-view stereo (MVS) because of their flexibility and superior performance. The quality of depth map used for merging is vital for accurate 3D reconstruction. While traditional depth map estimation has been performed in a discrete manner, we suggest the use of a continuous counterpart. In this paper, we first integrate silhouette information and epipolar constraint into the variational method for continuous depth map estimation. Then, several depth candidates are generated based on a multiple starting scales (MSS) framework. From these candidates, refined depth maps for each view are synthesized according to path-based NCC (normalized cross correlation) metric. Finally, the multiview depth maps are merged to produce 3D models. Our algorithm excels at detail capture and produces one of the most accurate results among the current algorithms for sparse MVS datasets according to the Middlebury benchmark. Additionally, our approach shows its outstanding robustness and accuracy in free-viewpoint video scenario.

This paper proposes a multi-component extended strong tracking filter (MESTer) for global localization. It is the first time strong tracking filter (STF) is introduced into robotics domain and is fundamentally extended to be suitable for fusing observations with arbitrary time-varying dimensionality, based on equivalent space transformation and extended orthogonality principle. The resulted extended strong tracking filter (ESTF) is then combined with a probabilistic multi-component evolving mechanism and finally forms the MESTer localization method. Real robot experiments and comparisons with existing methods show that MESTer has high convergence speed, computational efficiency and definite robustness to sensor noises, kidnapped robot problem, system nonlinearities, and symmetric environments.

We address the problem of measuring the distance between two subspaces, each of which is spanned by an image set. In the existing methods, only the orthonormal basis is used to represent the subspace. However, the images are usually distributed in a limited area, rather than the whole subspace. Therefore, the characteristics of the distribution should also be considered. In this letter, a weighted subspace distance (WSD) is proposed, in which the principal component values of the data set are adopted to calculate the weights. Experimental results on object recognition and retrieval with image sets demonstrate the effectiveness of our proposal.

We investigate image and video denoising using adaptive dual-tree discrete wavelet packets (ADDWP), which is extended from the dual-tree discrete wavelet transform (DDWT). With ADDWP, DDWT subbands are further decomposed into wavelet packets with anisotropic decomposition, so that the resulting wavelets have elongated support regions and more orientations than DDWT wavelets. To determine the decomposition structure, we develop a greedy basis selection algorithm for ADDWP, which has significantly lower computational complexity than a previously developed optimal basis selection algorithm, with only slight performance loss. For denoising the ADDWP coefficients, a statistical model is used to exploit the dependency between the real and imaginary parts of the coefficients. The proposed denoising scheme gives better performance than several state-of-the-art DDWT-based schemes for images with rich directional features. Moreover, our scheme shows promising results without using motion estimation in video denoising. The visual quality of images and videos denoised by the proposed scheme is also superior.

The multimodal and hierarchical structure characteristics of a system make process modeling quite difficult. In this paper, we present a hierarchical control model (HCM) for hierarchically multimodal processing. From multiple streams, a control layer extracts the inherent group process that denotes the evolution of the system and controls the evolution of every modality. HCMs model the influences of the group on modalities and represent the hierarchical structure of the system by a multilayer network. To estimate the state order of the model, we also present a new information criterion that corrects the preference of traditional criteria for more complex models and proves the rationality of HCMs. Comparisons with other models on multiagent activity recognition show that HCMs are reliable and efficient.

In this paper, we extend the 2-D dual-tree complex wavelet transform (DTCWT) to an adaptive anisotropic dual-tree complex wavelet packets (ADTCWP). The DTCWT subbands are iteratively decomposed into anisotropic complex wavelet packets, generating anisotropic wavelets and increasing the number of wavelets orientations without introducing extra redundancy. Then a basis selection procedure is applied so that the selected complex wavelet packets are well adapted to image characteristics. The effectiveness of ADTCWP is examined in image denoising with a bivariate statistical model. The ADTCWP-based denoising scheme shows better denoising results than several DTCWT-based methods for image with rich directional features. The denoised images recovered by the proposed scheme are visually more appealing.

In this paper, we propose a novel face recognition method based on anisotropic dual-tree complex wavelet packets(ADT-CWP). 2-D dual-tree complex wavelet transform(DT-CWT) provides a geometrically oriented decomposition for image representation as well as shift invariance. By applying anisotropic wavelet packet decomposition on DT-CWT further, ADT-CWP can be used to extract facial features better, which turns out to benefit for face recognition. With adaptively assigning different weights to different wavelet subbands, consistent best performances can be obtained based on different face databases which are under different conditions, such as varying illuminations and expressions, compared to PCA and other face recognition methods, especially Gabor-based method. Furthermore, in addition to the consistent and promising classification performances, our proposed ADT-CWP-based method has a really low computational complexity.

In this paper, we explore the application of 2-D dual-tree discrete wavelet transform (DDWT), which is a directional and redundant transform, for image coding. Three methods for sparsifying DDWT coefficients, i.e., matching pursuit, basis pursuit, and noise shaping, are compared. We found that noise shaping achieves the best nonlinear approximation efficiency with the lowest computational complexity. The interscale, intersubband, and intrasubband dependency among the DDWT coefficients are analyzed. Three subband coding methods, i.e., SPIHT, EBCOT, and TCE, are evaluated for coding DDWT coefficients. Experimental results show that TCE has the best performance. In spite of the redundancy of the transform, our DDWT _ TCE scheme outperforms JPEG2000 up to 0.70 dB at low bit rates and is comparable to JPEG2000 at high bit rates. The DDWT _TCE scheme also outperforms two other image coders that are based on directional filter banks. To further improve coding efficiency, we extend the DDWT to an anisotropic dual-tree discrete wavelet packets (ADDWP), which incorporates adaptive and anisotropic decomposition into DDWT. The ADDWP subbands are coded with TCE coder. Experimental results show that ADDWP _ TCE provides up to 1.47 dB improvement over the DDWT _TCE scheme, outperforming JPEG2000 up to 2.00 dB. Reconstructed images of our coding schemes are visually more appealing compared with DWT-based coding schemes thanks to the directionality of wavelets.

Content-based image retrieval (CBIR) has been an active research topic in the last decade. As one of the promising approaches, graph-based semi-supervised learning has attracted many researchers. However, while the related work mainly focused on global visual features, little attention has been paid to region-based image retrieval (RBIR). In this paper, a framework based on multilabel neighborhood propagation is proposed for RBIR, which can be characterized by three key properties: (1) For graph construction, in order to determine the edge weights robustly and automatically, mixture distribution is introduced into the Earth mover's distance (EMD) and a linear programming framework is involved. (2) Multiple low-level labels for each image can be obtained based on a generative model, and the correlations among different labels are explored when the labels are propagated simultaneously on the weighted graph. (3) By introducing multilayer semantic representation (MSR) and support vector machine (SVM) into the long-term learning, more exact weighted graph for label propagation and more meaningful high-level labels to describe the images can be calculated. Experimental results, including comparisons with the state-of-the-art retrieval systems, demonstrate the effectiveness of our proposal.

Label propagation and manifold ranking have been successfully adopted in content-based image retrieval (CBIR) in recent years. However, while the global low-level features are widely utilized in current systems, region-based features have received little attention. In this paper, a novel transductive framework based on correlated probabilistic label propagation is proposed for region-based images retrieval (RBIR), which can be characterized by three key properties: (1) unified feature matching (UFM) is chosen to measure the similarity between two segmented images. (2) To represent the segmented images in a uniform feature space, a generative model is adopted and the probabilistic labels of each image can be obtained. (3) In the retrieval process, multiple probabilistic labels of training samples are propagated simultaneously on the weighted graph, and the correlation among different labels are explored. Experimental results on 10000 images show that our algorithm can greatly improve the retrieval performance of the RBIR system.

This paper investigates the use of the anisotropic 3-D dual-tree discrete wavelet transform (DDWT) for video coding. The 3-D DDWT is an attractive video representation because it isolates image patterns with different spatial orientations and motion directions and speeds in separate subbands. Our previous codecs using the 3-D isotropic DDWT provides better performance than the 3-D SPIHT codec on the 3-D DWT. In this paper, we explore the use of anisotropic DDWT (ADDWT) for video coding. The ADDWT extends the superiority of the normal DDWT with more directional subbands without adding to the redundancy. The proposed codec applies SPIHT to each of the ADDWT trees. This codec provides significantly better performance than the 3-D SPIHT codec using the standard DWT and the DDWT both objectively and subjectively. None of these video codecs requires motion compensation.

This article presents an adaptive dynamic clustered particle filtering method for mobile robot global localization. The posterior distribution of robot pose in global localization is usually multimodal due to the symmetry of the environment and ambiguous detected features. Moreover, the multimodal distribution of the posterior varies as the robot moves and observations are obtained. Considering these characteristics, we use a set of clusters of particles to represent the posterior. These clusters are dynamically evolved corresponding to the varying posterior by merging the overlapping clusters and splitting the diffuse clusters or those whose particles gather to some sub-clusters inside. Further, in order to improve computational efficiency without sacrificing estimation accuracy, a mechanism for adapting the sample size of clusters is proposed. The theoretical lower bound of the number of particles needed to limit the estimation error is derived, based on the central limit theorem in multidimensional space and the statistic theory of ImportanceSampling (IS). Simulation results show the effectiveness of the proposed method, which is sufficient to achieve robust tracking of robot’s real pose and meanwhile significantly enhance the computational efficiency.

In this paper, we investigate image compression using 2D dual-tree discrete wavelet transform (DDWT), which is an overcomplete transform with direction-selective basis functions. To further sparsify DDWT coefficients, an iterative projection-based noise shaping method is employed. We analyze the statistics of DDWT coefficients as well as the inter-scale, inter-subband, and intra-subband dependency among the DDWT coefficients. We further evaluate the application of SPIHT and EBCOT for coding DDWT coefficients. Experimental results show that SPIHT is more effective than EBCOT for DDWT, and the DDWT-SPIHT coder outperforms JPEG2000 at low bit rates and is comparable to JPEG2000 at high bit rates.

Hidden Markov models (HMMs) have been widely used in various fields, including image categorization and retrieval. Most of the existing methods train HMMs by low-level features of image blocks; however, the blockbased features can not reflect high-level semantic concepts well. This paper proposes a new method to train HMMs by region-based features, which can be obtained after image segmentation. Our work can be characterized by two key properties: (1) Region-based HMM is adopted to achieve better categorization performance, for the region-based features accord with the human perception better. (2) Multi-layer semantic representation (MSR) is introduced to couple with region-based HMM in a long-term relevance feedback framework for image retrieval. The experimental results demonstrate the effectiveness of our proposal in both aspects of categorization and retrieval.

Discrete wavelet transform is an effective tool to generate scalable stream, but it cannot efficiently represent edges which are not aligned in horizontal or vertical directions, while natural images often contain rich edges and textures of this kind. Hence, recently, intensive research has been focused particularly on the directional wavelets which can effectively represent directional attributes of images. Specifically, there are two categories of directional wavelets: redundant wavelets (RW) and adaptive directional wavelets (ADW). One representative redundant wavelet is the dual-tree discrete wavelet transform (DDWT), while adaptive directional wavelets can be further categorized into two types: with or without side information. In this paper, we briefly introduce directional wavelets and compare their directional bases and image compression performances.

Efficient exploitation of the temporal and inter-view correlation is critical to multi-view video coding (MVC), and the key to it relies on the design of prediction chain structure according to the various pattern of correlations. In this paper, we propose a novel prediction structure model to design optimal MVC coding schemes along with tradeoff analysis in depth between compression efficiency and prediction structure complexity for certain standard functionalities. Focusing on the representation of the entire set of possible chain structures rather than certain typical ones, the proposed model can given efficient MVC schemes that adaptively vary with the requirements of structure complexity and video source characteristics (the number of views, the degrees of temporal and interview correlations). To handle large scale problem in model optimization, we deploy a hybrid genetic algorithm which yields satisfactory results shown in the simulations.

Human action recognition is one of the most important problems in video content analysis and computer vision. In this letter, we propose a novel framework of human interaction recognition through motion decomposition. Interactions contain not only motions corresponding to each person but also motion details on different scales. Hence, we decompose an interaction into multiple interacting stochastic processes in the above two aspects. Under the framework, we present a Coupled Hierarchical Durational-State Dynamic Bayesian Network (CHDS-DBN) to model interactions by modeling the multiple stochastic processes. The effectiveness of the approach is demonstrated by experiments of two-person interaction recognition.

Block-matching algorithm (BMA) is widely adopted in video coding. However, many blocks contain both background pixels and moving-object pixels, which often differ significantly in true physical motions. Using BMA for these blocks may lead to large prediction error and thus decreased coding efficiency. More accurate prediction can be achieved by object-based coding, which segments a video frame into different objects and codes these objects separately. However, additional bits are required for encoding the shape of the objects, and the computation complexity of object segmentation is often very high. In this paper, a composite-block model and a joint-prediction algorithm are proposed. This algorithm can get accurate prediction without shape coding, and its computation complexity is very close to that of the BMA. The experimental result shows that the new H.264 coder enhanced by our algorithm outperforms original H.264 coder by approximately 0.8~1.5 dB

To bridge the gap between high level semantic concepts and low level visual features in content-based image retrieval (CBIR), online feature selection is really required. An effective similarity-based online feature selection algorithm in region-based image retrieval (RBIR) systems was proposed by W. Jiang etc., but some parts of the algorithm need to be improved. In this paper, the above algorithm is modified in two aspects: (1) Adaptive mixture models based on mutual information theory are adopted to determine the codebook size. (2) A new method is proposed, which can select not only feature axes parallel to the original ones, but also combined feature axes. Experimental results on 10000 images show that the proposed method can improve the retrieval performance, and save the computational time

The ability to interactively and seamlessly roam in the scenario while watching a video through IP network is an exciting visual experience. In this work, we implemented a 3D TV system with real-time data acquisition, compression, Internet transmission, light field rendering, and free-viewpoint control of dynamic scenes. Our system consists of an 8times8 light field camera array, 16 producer PCs, a streaming server system and several clients. Multiple video streams are coded in a real time manner that each client can freely selects the streams for novel view rendering. Also, our system minimize the per-user transmission bit rate while maintaining multi-view simul-switching ability for each user. We believe that this is the first real-time Internet streaming system that can simultaneously guarantee real time free-view point control, data storage and support arbitrary number of users. The average transmission bit rate for end user is lower than 2 Mbps which is suitable for the broadband IP network

With the rapid development of hardware equipments, it is now economically and technically feasible to build a video surveillance system. This paper presents the system architecture of VISS, a video intelligent surveillance system deployed in parking lots. In VISS we adopt robust moving object detecting and tracking algorithm, and we present a novel activity recognition framework based on layer hidden semi-Markov model (LHSMM) which is used for modeling activities. The experimental results on real-time video shows the system is effective and robust in complex activity recognition

This paper proposes a design method of decentralized robust controllers for robots with joint backlash, flexibility and damping characteristics. For each joint subsystem, a robust tracking controller is designed in two steps: first, a nominal controller is designed for the nominal plant to get desired tracking performance, then a robust compensator is added to restrain the influence of the perturbation, that is the difference of the real plant from the nominal plant. The controller designed by the proposed method is a linear time-invariant one. It is shown that by applying the controller with a sufficiently wide frequency bandwidth robust output tracking property can be achieved in the contact phase while a new contact of the motor with the load in the correct direction is ensured in the backlash phase. An important feature of the method is that the controller parameters can be tuned on-line easily

This paper presents an affine-in variant image retrieval approach based on wavelet-based detector, which uses the space-tree property of the transform coefficients to estimate the interest points. Meanwhile, in order to retrieve images compressed by wavelet algorithm such as JPEG2000, the detector only uses the partial bit-planes of the wavelet coefficients to detect the interest points. To provide affine-invariant image matching, annular color histogram, annular texture histogram and spatial cohesion based on interest points are presented to describe image features. A series of experiments based on an image database consisting of 1000 images are performed to confirm the effectiveness of our method

A novel data compression scheme of light field is presented. Different from the prior codecs, we perform image predicting and data coding in a set of subbands not directly in original images. In our approach, the original images are decomposed into subbands using wavelet packet transform, and the corresponding wavelet packet bases are divided into two parts: the predictable bases and the unpredictable bases by some criterions. In coding, a propagating algorithm and a rhombuses structure is used, and the subbands corresponding to the basis in predictable and unpredictable bases are added in sequence by relative energy until the reconstructing images meet the pre-established reconstruction quality. Experiments for two standard light fields verify the efficiency of our approach.

This work presents a new kind of reference frame, i.e., the "background frame", to improve the accuracy of motion compensation in video compression. When an object moves in the scene of a video sequence, a certain part of the background is overlapped at first. After several frames, it begins to reappear. In the frames the background reappears, the blocks contained in the background part may be poorly predicted by neighboring blocks which have been overlapped in the previous frames. To solve this problem, the "background frame" is constructed, based on blocks that have kept unchanged in a certain number of continuous frames. The background frame is free from the influence of moving objects. So, when it is used as a reference jointly with the traditional previous frame, better prediction would be obtained. The experimental results show that the background frame based motion compensation (BFMC) algorithm can obtain increased coding efficiency compared to the JVT/AVC/H.264 standard with one reference frame or two reference frames.

A disparity-compensated compression scheme for light fields is presented. In contrast to prior disparity-compensated based codecs we perform disparity estimating and image predicting in subbands not directly in the original images. In our approach, firstly the original images are decomposed into subbands using the wavelet packet transform, and then the corresponding wavelet packet bases are divided into predictable bases and unpredictable bases by some criteria. In coding, the hierarchical structure proposed by Magnor (1999) is used, and the subbands corresponding to the predictable and unpredictable basis are added sequentially by relative energy until the reconstructed images meet the pre-established reconstruction quality. Except for high compression ratios our approach also provides scalability to some extent. Experiments for two standard light fields verify the efficiency of our approach

The research in the above paper (X. Jiang and W. Xu, "Comments on 'Stability of fuzzy control systems with bounded uncertain delays'", IEEE Trans. Fuzzy Syst., vol. 12, pp. 285-286, Apr. 2004) was carried out while the first author was with the Department of Automation, Tsinghua University, Beijing 100084, China. He is now with the Faculty of Informatics and Communication, Central Queensland University, Rockhampton QLD 47012, Australia.

In these comments, some misleading statements in the above paper have been pointed out and relation results are given.

This paper presents a new spatially adaptive wavelet denoising method. Based on a doubly stochastic process model of wavelet coefficients, the method gives a new threshold, which varies spatially according to the variances of the coefficients, using the minimum description length (MDL) principle. The new threshold is not only easier to analyze since it is in a closed form, but also provides more facility for future compression than several other methods, almost without deteriorating mean square error risk.