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IEEE International Conference on image processing

作者： Tao Zhang Long Xu Jie Yang Pengfei Shi Wenjing Jia Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781479983407

In this paper, we address the problem of human action recognition by representing image sequences as a sparse collection of patch-level spatiotemporal events that are salient in both space and time domain. Our method uses a multi-scale volumetric representation of video and adaptively selects an optimal space-time scale under which the saliency of a patch is most significant. The input image sequences are first partitioned into non-overlapping patches. Then, each patch is represented by a vector of coefficients that can linearly reconstruct the patch from a learned dictionary of basis patches. We propose to measure the spatiotemporal saliency of patches using Shannon's self-information entropy, where a patch's saliency is determined by information variation in the contents of the patch's spatiotemporal neighborhood. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method.

关键词： Sparse coding Spatiotemporal saliency Action recognition Shannon information entropy Sparse coding image sequences Shannon information Patches

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Fast Pedestrian Detection for Mobile Devices

Fast Pedestrian Detection for Mobile Devices

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International IEEE Conference on Intelligent Transportation Systems

作者： Arthur Daniel Costea Andreea Valeria Vesa Sergiu Nedevschi Image Processing and Pattern Recognition Research Center Computer Science Department Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781467365970

In this paper we present a fast and robust solution for pedestrian detection that can run in real time conditions even on mobile devices with limited computational power. An optimization of the channel features based multiscale detection schemes is proposed by using 8 detection models for each half octave scales. The image features have to be computed only once each half octave and there is no need for feature approximation. We use multiscale square features for training the multiresolution pedestrian classifiers. The proposed solution achieves state of art detection results on Caltech pedestrian benchmark at over 100 FPS using a CPU implementation, being the fastest detection approach on the benchmark. The solution is fast enough to perform under real time conditions on mobile platforms, yet preserving its robustness. The full detection process can run at over 20 FPS on a quad-core ARM CPU based smartphone or tablet, being a suitable solution for limited computational power mobile devices or embedded platforms.

关键词： pedestrian detection image features solution image feature Mobile devices Pedestrians Solutions Fasting Robust solution FES gene Smartphone Solution computational power

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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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SALIENT OBJECT DETECTION USING NORMALIZED CUT AND GEODESICS

SALIENT OBJECT DETECTION USING NORMALIZED CUT AND GEODESICS

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IEEE International Conference on image processing

作者： Keren Fu Chen Gong Irene Y. H. Gu Jie Yang Pengfei Shi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Department of Signals and Systems Chalmers University of Technology

ISBN: (纸本)9781479983407

Recently the Normalized cut (Ncut) has been introduced to salient object detection [1, 2]. In this paper we validate that instead of proposing new detection models that leverage the Ncut, the previous geodesic saliency detection model which computes shortest paths on a graph can be adapted to eigenvectors of the Ncut to produce superior performance. Since the Ncut partitions a graph in a normalized energy minimization fashion, resulting eigenvectors contain decent cluster information that can group visual contents. Combining it with the existing geodesic saliency detection is conducive to highlighting salient objects uniformly, yielding to improved detection accuracy. Experiments by comparing with 12 existing methods on four benchmark datasets show the proposed method significantly outperforms the original geodesic saliency model and achieves comparable performance to state-of-the-art methods.

关键词： Salient object detection Normalized cut Geodesic saliency Saliency map Geodesic Object detection Eigenvector Line graph

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GRIB Parallel Design of Civil Aviation Meteorological Data processing System

GRIB Parallel Design of Civil Aviation Meteorological Data P...

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The 14th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2015)(第十四届分布式计算及其应用国际学术研讨会)

作者： Zhengwei Guo Yongwei Gao Yafei Jiang Guang Xue School of Computer and Information Engineering Institute of Image Processing and Pattern Recognition Henan University Kaifeng Henan475001China

With the increasing volume of business the civil aviation data processing system,the data processing efficiency requirements become higher and *** order to improve the speed of data processing,we design a parallel scheme of GRIB according to the GRIB data processing *** achieved the parallel processing of GRIB data reading and decoding process,and carried out the related *** is significantly improve the speed of data loading.

关键词： Civil Aviation Meteorological Data processing System GRIB Multithreading Parallel design

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Learning to transfer privileged ranking attribute for object classification

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Journal of Information and Computational Science 2015年第1期12卷 367-380页

作者： Wang, Shengzheng Yang, Jie Merchant Marine College Shanghai Maritime University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

Learning Using Privileged Information (LUPI) provides an effective framework to solve the learning problem under situation of the asymmetric distribution of information between training and test time. It has been successfully applied in the category recognition, e.g., protein classification, hand-writing recognition, animal categorization, etc. However, in the existing methods, various semantic attributes, with the help of experts, were only simply translated into the feature vectors and considered as the privileged data, which restricts the LUPI to the simple applications since it is difficult to guarantee that the privileged data is similarly informative about the problem at hand as the original data. Therefore, this paper presents a novel approach based on an attribute-ranking learning algorithm to construct the example-oriented privileged data. The main idea is to provide an effective means to transfer the mid-level semantic attributes to the original training data. Namely, we first obtain a real-valued rank per attribute for each example indicating the relative strength of the attribute presence in all examples, and then the resulting attribute ranking results are used to generate the privileged data. The experimental results show that the proposed approach provides a promising means to apply the privileged ranking attributes, and further demonstrate significant improvements in classification accuracy on three typical databases: PubFig, OSR and AwA.

关键词： Object recognition

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Visual Tracking via Nonnegative Regularization Multiple Locality Coding

Visual Tracking via Nonnegative Regularization Multiple Loca...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Fanghui Liu Tao Zhou Jie Yang Irene Y. H. Gu Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Dept. of Signals and Systems Chalmers University of Technology

ISBN: (纸本)9781467397124

This paper presents a novel object tracking method based on approximated Locality-constrained Linear Coding (LLC). Rather than using a non-negativity constraint on encoding coefficients to guarantee these elements nonnegative, in this paper, the non-negativity constraint is substituted for a conventional l2 norm regularization term in approximated LLC to obtain the similar nonnegative effect. And we provide a detailed and adequate explanation in theoretical analysis to clarify the rationality of this replacement. Instead of specifying fixed K nearest neighbors to construct the local dictionary, a series of different dictionaries with pre-defined numbers of nearest neighbors are selected. Weights of these various dictionaries are also learned from approximated LLC in the similar framework. In order to alleviate tracking drifts, we propose a simple and efficient occlusion detection method. The occlusion detection criterion mainly depends on whether negative templates are selected to represent the severe occluded target. Both qualitative and quantitative evaluations on several challenging sequences show that the proposed tracking algorithm achieves favorable performance compared with other state-of-the-art methods.

关键词： Dictionaries Encoding Target tracking Visualization Bismuth image reconstruction image coding

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Based on Space Geometry Method for Actuator Fault Detection and Isolation with Disturbances and Noises 33

Based on Space Geometry Method for Actuator Fault Detection ...

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第三十三届中国控制会议

作者： HOU Yandong JIN Yong CHENG Qianshuai Institute of Image Processing and Pattern Recognition Henan University

ISBN: (纸本)9781479947249

This paper investigates the application of space geometry method for actuator fault detection and isolation(FDI) with disturbance inputs and measurement noises. To achieve the input signals, disturbances and noises are decoupled from residuals. Then the residual generator is designed and the relevant parameters are solved for it. When there are residuals exceed the threshold value, we can detect the faults. It can obtain single and multiple actuators FDI because of the method which we proposed satisfies the one-to-one corresponding relationship between faults and residuals. Simulation results demonstrate effectiveness of our proposed algorithm.

关键词： Actuator fault,Residual generator,Space geometry method,FDI.

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Blind source separation with evolution based KICA

Blind source separation with evolution based KICA

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Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast)

作者： Peng Wu Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9781479979684

Kernel independent component analysis (KICA) has been widely used in the field of blind source separation. The selection of kernel function and its parameters plays an important role in KICA algorithm performance. An optimal kernel model should be rich enough to well map the given samples. However, users usually use a singular kernel based model in their experiments, which leads to a suboptimal kernel model. In order to solve this problem, we propose the evolution based multiple kernel independent component analysis (EMKICA), in which a convex combination of multiple base kernels is used instead of single kernel of KICA. The combination weights are learned by particle swarm optimization algorithm. Firstly, we elaborate the basic theory of KICA and concept of EMKICA, also the combination form of the composition kernel used in EMKICA. Secondly, we describe the presentation of the individuals in the particle swarm optimization algorithm, the settings of the evaluation function and general algorithm. Finally, we evaluate the separation ability of EMKICA on three different data sets including one-dimensional mixed signals, composite images and images with reflection. The experimental results verify the effectiveness of EMKICA.

关键词： Kernel Independent component analysis Algorithm design and analysis Multimedia communication Correlation Optimization Asia

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A novel multi-sensor multiple model particle filter with correlated noises for maneuvering target tracking

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High Technology Letters 2014年第4期20卷 355-362页

作者：胡振涛 Fu Chunling Institute of Image Processing and Pattern Recognition Henan University School of Physics and Electronics Henan University

Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is *** with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new *** the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle *** addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement *** the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-line way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor ***,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

关键词： multi-sensor information fusion weight optimization correlated noises maneuvering target tracking

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