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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Qiang Wu Huaifeng Zhang Wenjing Jia Xiangjian He Jie Yang Tom Hintz Computer Vision Research Group University of Technology Broadway NSW Australia Institute of Image Process. & Pattern Recognition Shanghai Jiaotong University China

Car plate detection is a key component in automatic license plate recognition system. This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features. The statistical features are useful for simplifying the process on cascade classifier. The cascaded tree-style detector design will further reduce the false alarm and the false dismissal while retaining a high detection ratio. The experimental results obtained by the proposed algorithm exhibit the encouraging performance.

关键词： Object detection Licenses Detectors computer vision pattern recognition Lighting Interference Australia Image recognition Robustness

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Tracking of human motion based on adaptive foreground segmentation and particle filter

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Journal of Test and Measurement Technology 2005年第3期19卷 320-326页

作者： Li, Wei Zhang, Yun-Hua Wang, Ya-Ming Research Center for Computer Vision and Pattern Recognition Zhejiang University of Sciences Hangzhou 310018 China

An approach to the tracking of trajectories of 3-D human motion from image sequence based on adaptive foreground segmentation and particle filter is proposed in this paper. First, the Gaussian model for image pixel is presented. Based on this, the adaptive segmentation of human body is finished using the information of difference image and the prior distribution of pixel density. Then, the tracking model under perspective imaging for body plane is established. Due to the fact that image function is nonlinear and the distribution of the noise in images is unknown, the particle filter based tacking is used. Finally, the 3-D trajectory of body plane is obtained. Experimental results show that 3-D trajectory of body plane can be effectively tracked, and the tracking results of particle filter is better than that of extended Kalman filter for this human motion tracking problem.

关键词： Image segmentation

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Motion constraint patterns

Motion constraint patterns

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IEEE Workshop on Qualitative vision

作者： C. Fermuller Department for Pattern Recognition and Image Processing Institute for Automation Technical University of of Vienna Vienna Austria Computer Vision Laboratory Center for Automation Research University of Maryland College Park MD USA

The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, derivatives of the image flow) to 3-D motion and structure. Since it has proved very difficult to achieve accurate input (local image motion), a lot of effort has been devoted to the development of robust techniques. A new approach to the problem of egomotion estimation is taken, based on constraints of a global nature. It is proved that local normal flow measurements form global patterns in the image plane. The position of these patterns is related to the three dimensional motion parameters. By locating some of these patterns, which depend only on subsets of the motion parameters, through a simple search technique, the 3-D motion parameters can be found. The proposed algorithmic procedure is very robust, since it is not affected by small perturbations in the normal flow measurements. As a matter of fact, since only the sign of the normal flow measurement is employed, the direction of translation and the axis of rotation can be estimated with up to 100% error in the image measurements.< >

关键词： Motion estimation computer vision Automation Image motion analysis Fluid flow measurement Motion measurement Rotation measurement Laboratories Educational institutions Geometrical optics

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Convex hull based approach for multi-oriented character recognition from graphical documents

Convex hull based approach for multi-oriented character reco...

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International Conference on pattern recognition

作者： Partha Pratim Roy Umapada Pal Josep Llados Fumitaka Kimura Computer Vision Center Universitat Autònoma De Barcelona Bellaterra Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Mie Japan

ISBN: (纸本)9781424421749

In this paper, we present a scheme towards recognition of English character in multi-scale and multi-oriented environments. Graphical document such as map consists of text lines which appear in different orientation. Sometimes, characters in a single word may follow a curvilinear way to annotate the graphical curve lines. For recognition of such multi-scale and multi-oriented characters a Support Vector Machine (SVM) based scheme is presented in this paper. The feature used here is invariant to character orientation. Circular ring and convex hull have been used along with angular information of the contour pixels of the character to make the feature rotation invariant. We tested our proposed scheme on two different datasets. Combining circular and convex hull feature we have obtained 96.73% and 99.56% accuracy in these two datasets.

关键词： Character recognition pattern recognition Support vector machines Frequency Text recognition Histograms Clocks Rivers Optical character recognition software Testing

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Combination of product graph and random walk kernel for symbol spotting in graphical documents

Combination of product graph and random walk kernel for symb...

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International Conference on pattern recognition

作者： Anjan Dutta Jaume Gibert Josep Lladós Horst Bunke Umapada Pal Computer Vision Center Universitat Autònoma de Barcelona Barcelona Spain Institute of Computer Science and Applied Mathematics Universitat Bern Bern Switzerland Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781467322164

This paper explores the utilization of product graph for spotting symbols on graphical documents. Product graph is intended to find the candidate subgraphs or components in the input graph containing the paths similar to the query graph. The acute angle between two edges and their length ratio are considered as the node labels. In a second step, each of the candidate subgraphs in the input graph is assigned with a distance measure computed by a random walk kernel. Actually it is the minimum of the distances of the component to all the components of the model graph. This distance measure is then used to eliminate dissimilar components. The remaining neighboring components are grouped and the grouped zone is considered as a retrieval zone of a symbol similar to the queried one. The entire method works online, i.e., it doesn't need any preprocessing step. The present paper reports the initial results of the method, which are very encouraging.

关键词： Kernel Computational modeling pattern recognition Databases Labeling Equations Performance evaluation

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Skeleton based action recognition with convolutional neural network 3

Skeleton based action recognition with convolutional neural ...

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3rd IAPR Asian Conference on pattern recognition, ACPR 2015

作者： Du, Yong Fu, Yun Wang, Liang Center for Research on Intelligent Perception and Computing CRIPAC China Center for Excellence in Brain Science and Intelligence Technology CEBSIT China Nat'l Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences China College of Engineering College of Computer and Information Science Northeastern University United States

ISBN: (纸本)9781479961009

Temporal dynamics of postures over time is crucial for sequence-based action recognition. Human actions can be represented by the corresponding motions of articulated skeleton. Most of the existing approaches for skeleton based action recognition model the spatial-temporal evolution of actions based on hand-crafted features. As a kind of hierarchically adaptive filter banks, Convolutional Neural Network (CNN) performs well in representation learning. In this paper, we propose an end-to-end hierarchical architecture for skeleton based action recognition with CNN. Firstly, we represent a skeleton sequence as a matrix by concatenating the joint coordinates in each instant and arranging those vector representations in a chronological order. Then the matrix is quantified into an image and normalized to handle the variable-length problem. The final image is fed into a CNN model for feature extraction and recognition. For the specific structure of such images, the simple max-pooling plays an important role on spatial feature selection as well as temporal frequency adjustment, which can obtain more discriminative joint information for different actions and meanwhile address the variable-frequency problem. Experimental results demonstrate that our method achieves the state-of-art performance with high computational efficiency, especially surpassing the existing result by more than 15 percentage on the challenging ChaLearn gesture recognition dataset. © 2015 IEEE.

关键词： Neural networks

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Deep Audio-visual Learning:A Survey

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International Journal of Automation and computing 2021年第3期18卷 351-376页

作者： Hao Zhu Man-Di Luo Rui Wang Ai-Hua Zheng Ran He Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and TechnologyAnhui UniversityHefei 230601China Center for Research on Intelligent Perception and Computing(CRIPAC)and National Laboratory of Pattern Recognition(NLPR) Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of the Chinese Academy of SciencesBeijing 100049China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China

Audio-visual learning,aimed at exploiting the relationship between audio and visual modalities,has drawn considerable attention since deep learning started to be used *** tend to leverage these two modalities to improve the performance of previously considered single-modality tasks or address new challenging *** this paper,we provide a comprehensive survey of recent audio-visual learning *** divide the current audio-visual learning tasks into four different subfields:audiovisual separation and localization,audio-visual correspondence learning,audio-visual generation,and audio-visual representation ***-of-the-art methods,as well as the remaining challenges of each subfield,are further ***,we summarize the commonly used datasets and challenges.

关键词： Deep audio-visual learning audio-visual separation and localization correspondence learning generative models representation learning

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Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation

Med-DANet V2: A Flexible Dynamic Architecture for Efficient ...

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IEEE Workshop on Applications of computer vision (WACV)

作者： Haoran Shen Yifu Zhang Wenxuan Wang Chen Chen Jing Liu Shanshan Song Jiangyun Li School of Automation and Electrical Engineering University of Science and Technology Beijing Center for Research in Computer Vision University of Central Florida National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences

Recent works have shown that the computational efficiency of 3D medical image (e.g. CT and MRI) segmentation can be impressively improved by dynamic inference based on slice-wise complexity. As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i.e. Med-DANet [44]) has achieved a favorable accuracy and efficiency trade-off by dynamically selecting a suitable 2D candidate model from the pre-defined model bank for different slices. However, the issues of incomplete data analysis, high training costs, and the two-stage pipeline in Med-DANet require further improvement. To this end, this paper further explores a unified formulation of the dynamic inference framework from the perspective of both the data itself and the model structure. For each slice of the input volume, our proposed method dynamically selects an important foreground region for segmentation based on the policy generated by our Decision Network and Crop Position Network. Besides, we propose to insert a stage-wise quantization selector to the employed segmentation model (e.g. U-Net) for dynamic architecture adapting. Extensive experiments on BraTS 2019 and 2020 show that our method achieves comparable or better performance than previous state-of-the-art methods with much less model complexity. Compared with previous methods Med-DANet and TransBTS with dynamic and static architecture respectively, our framework improves the model efficiency by up to nearly 4.1 and 17.3 times with comparable segmentation results on BraTS 2019. Code will be available at https://***/Rubics-Xuan/Med-DANet.

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Edgy salient local binary patterns in inter-plane relationship for image retrieval in Diabetic Retinopathy

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Procedia computer Science 2017年 115卷 440-447页

作者： Gajanan M. Galshetwar Laxman M. Waghmare Anil B. Gonde Subrahmanyam Murala Center of Excellence in Signal and Image Processing (COESIP) Department of ECE SGGSIET Nanded Maharashtra 431606 India Computer Vision and Pattern Recognition Laboratory Department of Electrical Engineering IIT Ropar Rupnagar 140001 India

In this paper, a novel approach for content based image retrieval (CBIR) in diabetic retinopathy (DR) is proposed. The concept of salient point selection and inter-plane relationship technique is used. Salient points are selected from edgy image and later using inter-planer relationship, Local Binary patterns (LBPs) are calculated using the salient point as a center pixel. Our approach enhanced the results as we used color features in combination with LBP features. Experimentation is carried out on MESSIDOR database of 1200 retinal images, proposed approach has average precision of 57.82% as compared to the earlier approach whose average precision is 53.70%.

关键词： Content-Based image retrieval (CBIR) Diabetic Retinopathy (DR) Edgy Salient points Local Binary patterns (LBPs)

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Robust object tracking in crowd dynamic scenes using explicit stereo depth

Robust object tracking in crowd dynamic scenes using explici...

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11th Asian Conference on computer vision, ACCV 2012

作者： Li, Chi Lu, Le Hager, Gregory D. Tang, Jianyu Wang, Hanzi Cognitive Science Department Xiamen University Xiamen China Imaging and Computer Vision Siemens Corporate Research Princeton NJ United States Computer Science Department Johns Hopkins University Baltimore MD United States Center for Pattern Analysis and Machine Intelligence Xiamen University China

ISBN: (纸本)9783642374302

In this paper, we exploit robust depth information with simple color-shape appearance model on single object tracking in crowd dynamic scenes. Since binocular video streams are captured from a moving camera rig, background subtraction cannot provide a reliable enhancement of region of interest. Our main contribution is a novel tracking strategy to employ explicit stereo depth to track and segment object in crowd dynamic scenes with occlusion handling. Appearance cues including color and shape play a secondary role to further extract the foreground acquired by previous depth-based segmentation. The proposed depth-driven tracking approach can largely alleviate the drifting issue, especially when the object frequently interacts with similar background in long sequence tracking. The problems caused by rapid object appearance change can also be avoided due to the stability of the depth cue. Furthermore, we propose a new, yet simple and effective depth-based scheme to cope with complete occlusion in tracking. From experiments on a large collection of challenging outdoor and indoor sequences, our algorithm demonstrates accurate and reliable tracking performance which outperforms other state-of-the-art competing algorithms. © 2013 Springer-Verlag.

关键词： Image segmentation

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