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检索条件"机构=Institute of Image Processing and Pattern recognition"

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Robust 3D modeling from silhouette cues

Robust 3D modeling from silhouette cues

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Enliang Zheng Qiang Chen Xiaochao Yang Yuncai Liu Institute of Image Processing and Pattern Recognition Shanghai JiaoTong University Shanghai China

We consider the problem of 3D modeling under the environments where colors of the foreground objects are similar to the background, which poses a difficult problem of foreground and background classification. A purely image-based algorithm is adopted in this paper, with no prior information about the foreground objects. We classify foreground and background by fusing the information at the pixel and region levels to obtain the similarity probability map, followed by a Bayesian sensor fusion framework to infer the space occupancy grid. The estimation of the occupancy allows incremental updating once a new observation is available, and the contribution of each observation can be adjusted according to its reliability. Finally, three parameters in the algorithm are analyzed in detail and experiments show the effectiveness of this method.

关键词： Robustness Bayesian methods image reconstruction Cameras Probability Pixel image processing pattern recognition Color Sensor fusion

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International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, IJCBS

作者： Huilin Xiong Panfei Shi Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

In this paper, we present a scheme of similarity measure learning based on kernel optimization. Employing a data-dependent kernel model, the proposed scheme optimizes the spatial distribution of the training data in the feature space, aiming to maximize the class separability of the data in the feature space. The learned similarity measure, derived from the optimized kernel, exhibits a favorable feature to the task of pattern classification, that the spatial resolution of the embedding space is expanded around the boundary areas, and shrunk around the homogeneous areas. Experiments demonstrate that using the learned similarity measure can substantially improve the performances of the K-nearest-neighbor classifier.

关键词： Kernel Euclidean distance pattern recognition Area measurement Performance evaluation Extraterrestrial measurements Support vector machines Hilbert space Training data Spatial resolution

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Deformable Surface 3D Tracking Using Linear Programming

Deformable Surface 3D Tracking Using Linear Programming

引用

International Workshop on Computer Science and Engineering (WCSE)

作者： Chenhao Wang Shuhan Shen Yuncai Liu Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

A method for 3D shape reconstruction of deformable surfaces from consecutive frames was presented. In our method, the model of the surface is represented by a triangulated mesh. The constraints for the model, including keypoint correspondences and disallowing large changes of edge orientation between consecutive frames, are formulated as Linear Programming (LP) constraints. Therefore the deformable surface 3D tracking method turns into an LP problem that can be effectively solved. The robustness and efficiency of our approach are validated on synthetic and real data.

关键词： Linear programming Surface reconstruction Shape image reconstruction Cameras Deformable models Computer science image processing pattern recognition Robustness

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A Unified Framework for Kernelization: The Empirical Kernel Feature Space

A Unified Framework for Kernelization: The Empirical Kernel ...

引用

Chinese Conference on pattern recognition (CCPR)

作者： Huilin Xiong Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

In this paper, we propose to kernelize linear learning machines, e.g., PCA and LDA, in the empirical kernel feature space, a finite-dimensional embedding space, in which the distances of the data in the kernel feature space are preserved. The empirical kernel feature space provides a unified framework for the kernelization of all kinds of linear machines: performing a linear machine in the finite-dimensional empirical feature space, its nonlinear kernel machine is then established in the original input data space. This method is different from the conventional kernel-trick based kernelization, and more importantly, the final nonlinear kernel machines, called empirical kernel machines, are shown to be more efficient in many real-world applications, such as face recognition and facial expression recognition, than the kernel-trick based kernel machines.

关键词： Kernel Face recognition pattern analysis pattern recognition Machine learning Linear discriminant analysis Training data Principal component analysis image processing Support vector machines

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Receptive Field of Visual Cortex Works Follow Multi-Resolution Statistics?

Receptive Field of Visual Cortex Works Follow Multi-Resoluti...

引用

Chinese Conference on pattern recognition (CCPR)

作者： Nanhong Tan Pengfei Shi Institute of Image Processing & Pattern Recognition Jiao Tong University Shanghai China

How does man's vision system work? In some cross research fields like neurobiology, psychology and robotics researchers have been work hard to answer the question for long time. Now on visual cortex neuroscience has accumulate much experimental data and some theories like information redundancy reduction, sparse coding have given their interpretation of experiments, but understanding information processing as a whole , especially to make a representation of image with basic conceptions of receptive field and direction column, is still a difficult task. In our work, together with consideration of psychology and sparse coding a multi-resolution statistics scheme is given, signal grads statistics is carried out according to resolution level, strength and direction in space respectively. By comparing the distribution of nerve cell on visual cortex with one of neural network which works follow multi-resolution statistics, the similarity of both tell the arithmetic meanings of receptive field and direction column. With modern neuroscience experimental means the validation of the point of view in this article may be done in principle.

关键词： Statistics Statistical distributions Psychology Neuroscience Machine vision Robots image coding Information processing Signal resolution Biological neural networks

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High-Speed Video Transfer and Real-Time Infrared Spots Detection Based on FPGA

High-Speed Video Transfer and Real-Time Infrared Spots Detec...

引用

International Workshop on Computer Science and Engineering (WCSE)

作者： Maoxiang Huang Chenhao Wang Yuncai Liu Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

This paper presents a high-speed video transfer scheme and a real-time infrared spots detection algorithm designed for field programmable gate array (FPGA) implementation. Rather than IEEE 1394a, two IEEE 1394b interfaces are alternatively used to ensure high-resolution image transfer in real time. In order to execute fast infrared spots detection, a parallel algorithm that processes four pixels per clock cycle is proposed. It detects infrared spots in a single pass over a frame and its implementation is only composed of combinatorial logic and registers. Furthermore, the execution time of the algorithm is independent of image content. A prototype system is implemented in an FPGA device. It is capable of transferring 1024 × 768 images smoothly at 60 fps and detecting infrared sports in a 1024 × 768 image within 1.966ms, demonstrating its superiority over the existing multi-pass algorithms and some other one-pass algorithms. Details of software and hardware architecture are discussed in this paper.

关键词： Infrared detectors Field programmable gate arrays Detection algorithms Algorithm design and analysis Parallel algorithms Clocks Logic devices Software prototyping Prototypes Infrared imaging

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Gender Classification of Facial images Based on Multiple Facial Regions

Gender Classification of Facial Images Based on Multiple Fac...

引用

Computer Science and Information Engineering, CSIE, WRI World Congress on FLAGGED

作者： Li Lu Ziyi Xu Pengfei Shi Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

In this paper, we describe an experimental investigation to evaluate the significance of different facial regions of a person in the task of gender classification. For this purpose we use a support vector machine (SVM) classifier on face images for gender classification. We perform experiments using different facial regions of varying resolution so that the significance of facial regions in this application can be assessed. According to the results obtained, the upper region of the face proved to be the most significant for the task of gender classification. Moreover, the changes in the resolution of the facial region images do not produce significant changes in the result. Based on the significance of different facial regions, we propose a gender classification method based on fusion of multiple facial regions and show that this method is able to compensate for facial expressions and lead to better overall performance.

关键词： Support vector machines Support vector machine classification Face detection Humans Face recognition Computer vision Principal component analysis Classification tree analysis Nose Computer science

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recognition of People Reoccurrences Using Bag-Of-Features Representation and Support Vector Machine

Recognition of People Reoccurrences Using Bag-Of-Features Re...

引用

Chinese Conference on pattern recognition (CCPR)

作者： Kun Liu Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

In multi-camera surveillance systems, it is important to track the same person across multiple cameras. It is also desirable to recognize the individuals who have been previously observed in a single-camera system. The method that represents a object image using a bag of visual words has been commonly used in image retrieval applications. For recognizing people, it can outperform the methods mainly based on global appearance like color histogram, and fit better to low-quality images compared to biometric features such as face and gait. In this paper we study the details in feature extraction, vocabulary building and classifier learning of the bag-of-features approach for classifying tracks of different individuals. Based on this approach, we design a online system applying incremental support vector machine learning with a decision scheme to distinguish reoccurrences from new targets. We get promising results from the evaluation with more than 100 tracks of 50 different people.

关键词： Support vector machines Target tracking Surveillance Cameras image retrieval Face recognition image recognition Histograms Biometrics Feature extraction

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Using silhouette for pose estimation of object with surface of revolution

Using silhouette for pose estimation of object with surface ...

引用

IEEE International Conference on image processing

作者： Ming Zhang Yinqiang Zheng Yuncai Liu Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9781424456536;9781424456543

A novel approach of pose estimation is proposed for the object with surface of revolution(SOR). The silhouette of the object is the only information necessary for this method and no cross section circle (latitude circle) is needed. In this article, we explain the property of tangent circle and use it to establish constraint between two images of object with different poses. Such constraint can help to solve the pose of object in both images. We test our method with a simulation experiment and use it to estimate the pose for both rigid body and articulated object.

关键词： image reconstruction Cameras Surface reconstruction image processing pattern recognition Testing Computational modeling Computer vision Application software Data mining

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Fault tolerant interval fusion for moving vehicle classification in wireless sensor networks

Fault tolerant interval fusion for moving vehicle classifica...

引用

IET International Communication Conference on Wireless Mobile and Computing (CCWMC 2009)

作者： C. T. Liu H. Huo T. Fang D. R. Li Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

Faults of sensor data will always present in sensor networks because of unreliable communication links, measurement interference and harsh environment. Developing fusion algorithms that can tolerate faults is necessary for reliable sensor network applications. In this paper, we study the fault tolerant fusion for moving vehicle classification based on Marzullo's interval fusion algorithm. The unreliable sensor data are represented using interval estimations. To reduce communication cost, quantized interval representation is adopted. Simulation results demonstrate the validity of the interval fusion algorithm. By using quantized representation, the communication cost is reduced.

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