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作者： Zhou, Tao Zhang, Junhao Xie, Kai Yang, Jie He, Xiangjian Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9781479957514

It is a challenging task to develop an effective and robust visual tracking method due to factors such as pose variation, illumination change, occlusion, and motion blur. In this paper, a novel tracking algorithm based on weighted subspace reconstruction error is proposed. We first compute the discriminative weights by sparse construction error with template dictionary consisted of positive and negative samples, and then confidence map for candidates is computed through subspace reconstruction error. Finally, the location of the target object is estimated by maximizing the decision map which is combined discriminative weights and subspace reconstruction error. Furthermore, we use the new evaluation criterion to verify the robustness of the current tracking result, which can reduce the accumulated error effectively. Experimental results on some challenging video sequences show that the proposed algorithm performs favorably against seven state-of-the-art methods in terms of accuracy and robustness. © 2014 IEEE.

关键词： Errors

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Online learning of multi-feature weights for robust object tracking

Online learning of multi-feature weights for robust object t...

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IEEE International Conference on image processing

作者： Tao Zhou Harish Bhaskar Kai Xie Jie Yang Xiangjian He Pengfei Shi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Dept. of Elec. & Comp. Engg. Khalifa Univ. of Science Technology and Research Abu Dhabi U.A.E Faculty of Engineering and Information Technology University of Technology Sydney Australia

Sparse Representation based Classification (SRC) and its potential in object tracking have been explored in recent years. However, the trade-off between the discriminative ability of the overly emphasized sparse representation and the lack of insight on correlation of visual information has raised questions over the general applicability of such methods in object tracking. In addition, the need for the optimization of a series of l 1 -regularized least square norm, increases the computational complexity thereby limiting their usage in real-time applications. In this paper, a novel approach to robust object tracking is proposed. First, the variations in the appearance of the tracked target is modelled using PCA basis vectors, and further, a l 2 -regularized least square method is used to solve the proposed representation model. In order to improve the robustness of feature representation in object tracking applications, weights are associated with multiple trackers; each formulated using a different feature, and adapted via an online learning scheme. Finally, a decision fusion criterion is imposed to generate an optimized output through the weighted combination of different tracking results. Experiments on challenging video sequences have demonstrated the superior accuracy and robustness of the proposed method in comparison to thirteen other state-of-the-art baselines.

关键词： Target tracking Robustness Object tracking Lighting Clutter Computed tomography Visualization

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Part-Based Tracking with Appearance Learning and Structural Constrains

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8834卷 594-601页

作者： Xiang, Wei Zhou, Yue Institute of Image Processing and Pattern Recognition Jiao Tong University Shanghai China

Adaptive tracking-by-detection methods are widely used in computer vision for tracking objects. Despite these methods achieve promising results, deformable targets and partial occlusions continue to represent key problem in visual tracking. In this paper, we propose a part-based visual tracking method. First, we take advantage of the existing online learning appearance model to learning the appearance of each part. Second, we propose a novel part initialization method and an affine invariant structural constrain between these parts. Third, a tracking model based on the appearance of each part and the spatial relationship between the parts is proposed. We make use of an optimization algorithm to find the best parts during tracking, update the appearance model and the structural constraints between parts simultaneously. In this paper we show our method has many advantages over the pure appearance learning based tracking model. Our method can effective solve the partial occlusion problem, and relieve the drift problems. What’s more, our method achieves great result while tracking the target of which geometric appearance changes drastically over time. © Springer International Publishing Switzerland 2014.

关键词： Target tracking

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Video-based tracking and quantified assessment of spontaneous limb movements in neonates

Video-based tracking and quantified assessment of spontaneou...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Long Xu Irene Yu-Hua Gu Anders Flisberg Magnus Thordstein Inst. of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Dept. of Signals and Systems Chalmers University of Technology Gotheburg Sweden Dept. of Pediatrics Sahlgrenska University Hospital Gothenburg Sweden Dept. of Clinical Neurophysiology Sahlgrenska University Hospital Gothenburg Sweden

ISBN: (纸本)9781467383264

Central nervous system dysfunction in infants may be manifested through inconsistent, rigid and abnormal limb movements. Detection and quantification of these movements in infants from videos are hence desirable for providing useful information to clinicians. This could lead to computer-aided diagnosis of dysfunctions where early treatment may improve infant development. In this paper, we propose a scheme for detecting and quantifying qualitative aspects of limb movement through multiple tracking and state space motion modeling on videos. The main novelties of the paper include: (a) An enhanced detection method for effectively detection small weak marker points from video; (b) Bayesian estimation and nearest neighbor searching for selecting new observation in individual tracker and for tracking marker trajectories on limbs; (c) A criterion for anomaly detection based on the frequency and duration of abrupt changes in limb movement, using window averaged prominent residual powers. The proposed method has been tested on videos of neonates, results show that the proposed method is promising for tracking and quantifying the movement of neonate limbs for helping medical diagnostics.

关键词： Pediatrics Videos Trajectory Tracking Estimation Feature extraction Clutter

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A Robust Method for High-Accuracy Microbe Identification Based on image processing

A Robust Method for High-Accuracy Microbe Identification Bas...

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2014 IEEE International Conference on Progress in Informatics and Computing

作者： Mingqi Cao Yuncai Liu Shanghai JiaoTong University Institute of Image Processing and Pattern Recognition

We present a novel fast method based on computer vision toidentify microbe. The proposed method is simple but absolutely effective. It combines approximate parallel light source and industrial camera, toautomatically accomplish the bacteria identification and monitor the growing states of bacteria during the progress of a drug sensitive test. Based on this method, the color information and turbidity information, which reflect the primary information of drug sensitive tests, can be obtained fast, while processing efficiency can be as high as hundreds of milliseconds per frame. The performance of our method is significantly accurate and robust.

关键词： microbe identification drug sensitive test localization color feature turbidity feature

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Gait period detection based on layered coding of depth information

Gait period detection based on layered coding of depth infor...

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International Conference on Machine Tool Technology and Mechatronics Engineering, ICMTTME 2014

作者： Zhang, Hao Lin Ben, Xian Ye Zhang, Peng Liu, Tian Jiao Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China School of Information Science and Engineering Shandong University Jinan China

ISBN: (纸本)9783038352464

Gait period detection, serving as a preprocessor for gait recognition, is commonly studied in the recent past. In this paper, we proposed a novel gait period detection method for depth gait video stream. The method introduces the concept of layered coding for depth images which decreases computational complexity. Furthermore, the extreme value of the sum of layered codes for gait sequence is utilized to judge the period endpoint, which is in accord with the naked-eye observation. In addition, gait recognition experiments on the TUM GAID database are conducted with the description of gait features of one single detected period by the proposed scheme using tensor representation. The high recognition accuracy verifies the effectiveness of the proposed depth gait period detection method. © (2014) Trans Tech Publications, Switzerland.

关键词： Codes (symbols)

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Combining local binary patterns for scene recognition

Journal of Software

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Journal of Software 2014年第1期9卷 203-210页

作者： Song, Minguang Guo, Ping Image Processing Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

Recently, spatial principal component analysis of census transform histograms (PACT) was proposed to recognize instance and categories of places or scenes in an image. An improved representation called Local Difference Binary pattern (LDBP) also was proposed and performed better than that of PACT. LDBP is based on the comparisons between center pixel and its neighboring pixels, but the relationship among neighbor pixels is not considered. In this paper, we propose to combine Local Neighbor Binary pattern (LNBP) with LDBP to construct a spatial representation for scene recognition, because that LNBP can provide complementary information regarding neighboring pixels for LDBP. Experiments on widely used datasets demonstrate that the performance of image recognition is further improved with proposed method. © 2014 ACADEMY PUBLISHER.

关键词： Pixels

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Accurate Computation of Fingerprint Intrinsic images with PDE-Based Regularization

Accurate Computation of Fingerprint Intrinsic Images with PD...

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7th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2012

作者： Li, Mingyan Chen, Xiaoguang College of Sciences Beijing Forestry University 100083 Beijing China Institute of Image Processing and Pattern Recognition College of Sciences North China University of Technology 100083 Beijing China

The intrinsic images of fingerprint, such as orientation field and frequency map, represent the particular and basic characteristics of fingerprint ridge/valley patterns, and play a key role in feature extraction and matching of the fingerprint recognition system. In this paper, a novel algorithm is presented for accurate computation of fingerprint intrinsic images with PDE-based regularization technique. First, the coarse orientation field is estimated using general gradient-based method, and the frequency map is estimated by Fourier spectrum analysis of the projected curve of local window. Then, to measure the reliability of the intrinsic images, the quality map is computed based on the gray-scale intensity and local structural information. Finally, accurate orientation field and frequency map are reconstructed with PDE-based regularization of nonlinear diffusion filtering which is controlled by the quality-based diffusivity. Experimental results illustrated the efficiency of the proposed approach. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Spectrum analysis

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Joint probabilistic constrained robust beamforming and antenna selection

Joint probabilistic constrained robust beamforming and anten...

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IEEE International Conference on Signal processing, Communications and Computing (ICSPCC)

作者： Jinwang Yue Yong Jin Zhentao Hu Xianxing Liu Institute of Image Processing & Pattern Recognition Henan University Kaifeng China

We propose a novel method for joint probabilistic constrained robust beamforming and antenna selection used in cognitive radio (CR) networks. Assuming complex Gaussian distributed channel state information (CSI) errors, the Bernstein-type inequalities are used to transform the no closed-form probabilistic constrained into the deterministic forms. Moreover, l1-norm is introduced as the closest convex approximation of ℓ0-norm. So, the original NP-hard optimal problem can be relaxed to as a tractable convex optimization problem. A computationally efficient and near-optimal solution is obtained by a iteratively re-weighted algorithm. Simulations show that the proposed algorithm meet prescribed service levels at a relatively small excess transmission power in a number of transmitter reduction scenarios.

关键词： Array signal processing Interference Probabilistic logic Antennas Signal to noise ratio Robustness Vectors

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SALIENCY DETECTION VIA FOREGROUND RENDERING AND BACKGROUND EXCLUSION

SALIENCY DETECTION VIA FOREGROUND RENDERING AND BACKGROUND E...

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IEEE International Conference on image processing

作者： Yijun Li Keren Fu Lei Zhou Yu Qiao Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University

ISBN: (纸本)9781479957521

In this paper, a novel approach for image visual saliency detection is proposed from both the salient object (foreground) and the background perspective. To better highlight the salient object, we start from what is a salient object and adopt priors including contrast prior and center prior to measure the dissimilarity between different image elements. To better suppress the background, we focus on what is the background and measure the pixel-wise saliency by the minimum seam cost where the seam is an optimal 8-connected path from the pixel to some boundary pixel. The final saliency map is obtained by the combination of two measure systems which leads to the goal of both highlighting the salient object and suppressing the background. Both qualitative and quantitative experiments conducted on a benchmark dataset show that our approach outperforms seven state-of-the-art methods.

关键词： Saliency detection SODM map BDM map Saliency map

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