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3rd Canadian Conference on Computer and Robot Vision, CRV 2006

作者： Bais, Abdul Sablatnig, Robert Gu, Jason Institute of Computer Technology Vienna University of Technology Vienna Austria Pattern Recognition and Image Processing Group Institute of Computer Aided Automation Vienna University of Technology Vienna Austria Robotics Research Group Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)0769525423

In this paper we discuss landmark based absolute localization of tiny autonomous mobile robots in a known environment. Landmark features are naturally occurring as it is not allowed to modify the environment with special navigational aids. These features are sparse in our application domain and are frequently occluded by other robots. This makes simultaneous acquisition of two or more landmarks difficult. Therefore, we propose a system that requires a single landmark feature. The algorithm is based on range measurement of a single landmark from two arbitrary points whose displacement can be measured using dead-reckoning sensors. Range estimation is done with a stereo vision system. Simulation results show that the robot can localize itself if it can estimates range of the same landmark from two different position and if the displacement between the two position is known. © 2006 IEEE.

关键词： Mobile robots

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Robot Position Identification by Actively Localizing Sound Beacons

Robot Position Identification by Actively Localizing Sound B...

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IEEE Instrumentation and Measurement Technology Conference

作者： Jie Huang Satoshi Ishikawa Michiko Ebana Huakang Li Qunfei Zhao School of computer science and engineering University of Aizu Japan Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

In this paper, we proposed a robot self position identification method by active sound localization. This method can be used for autonomous security robots working in room environments. A system using a AIBO robot equipped with two microphones and wireless network is constructed and is used for position identification experiments. Arrival time differences to the microphones of robot are used as localization cues. To overcome the ambiguity of front-back confusion, a three-head position measurement method was proposed. The robot position can be identified by the intersection of circles restricted by the azimuth differences to different speaker pairs. By localizing three or four speakers as sound beacons positioned on known locations, the robot can identify its self position with an average error of about 7 cm in a 2.5 times 3.0 m 2 working space

关键词： Microphones Loudspeakers Humans Head Azimuth Orbital robotics Mobile robots Humanoid robots image processing Robot sensing systems

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3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis

3D and Infrared Face Reconstruction from RGB data using Cano...

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International Conference on pattern recognition

作者： M. Reiter R. Donner G. Langs H. Bischof Pattern Recognition and Image Processing Group Vienna University of Technology Austria Graz University of Technology Institute for Computer Graphics and Vision Austria

In this paper, we apply a multiple regression method based on canonical correlation analysis (CCA) to face data modelling. CCA is a factor analysis method which exploits the correlation between two high dimensional signals. We first use CCA to perform 3D face reconstruction and in a separate application we predict near-infrared (NIR) face texture. In both cases, the input data are color (RGB) face images. Experiments show, that due to the correlation between input and output signal, only a small number of canonical factors are needed to describe the functional relation of RGB images to the respective output (NIR images and 3D depth maps) with reasonable accuracy

关键词： Shape Face detection Lighting image reconstruction image analysis image databases pattern analysis Rendering (computer graphics) image color analysis pattern recognition

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Recent Progress in Computerization of TCM

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通讯和计算机（中英文版） 2006年第7期3卷 78-81页

作者： Zhi Liu Jingqi Yan Qunlin Tang Qingli Li Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Shenzhen Graduate School of Harbin Institute of Technology Shenzhen 518055 China

The principles of Traditional Chinese Medical (TCM) diagnosis are based on the information obtained from four diagnostic processes, which are inspection, listening and smelling, inquiry and palpation. These diagnosis methods mainly rely on the experiences of the doctors. With the development of information technology, computerization of the diagnosis method is urgent. However, there are so many challenges in this field. This paper will provide detailed introduction of these contents. Through it, the state of arts of the computerization of TCM and the existing challenge of this field can be gotten.

关键词：终端设备至计算机多路转接器生物工程计算机化计算机技术

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An Efficient Method for Face Localization and recognition in Color images

An Efficient Method for Face Localization and Recognition in...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Farshid Hajati Karim Faez Saeed Khoshfetrat Pakazad Pattern Recognition and Image Processing Laboratory EE Department Amirkabir University of Technology슠 Tehran Iran Electrical Engineering Department Amirkabir University of Technology슠 Tehran Iran

This paper introduces an efficient method for face localization and recognition in color images. The proposed method uses the location of eyes for computation and extraction of a face's bounding ellipse. In this way, parameters of a face's ellipse (center, orientation, major and minor axis), is computed by the location of eyes in a face image. In the next step, we apply Pseudo Zernike Moments (PZM), Zernike Moments (ZM) and Principal Component Analysis (PCA) for feature extraction. For classification of these feature vectors a new structure of RBF neural networks with a novel distance function is introduced and a new method for determination of RBF unit parameters is proposed. Finally, we compare the efficiency of the proposed system for three types of feature vectors (PZM, ZM and PCA). Results emphasize the high accuracy and efficiency of the PZM features proportion to other features (ZM and PCA) for use in the proposed recognition system.

关键词： Face recognition image recognition Color Face detection Skin Eyes Principal component analysis Humans Feature extraction Neural networks

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A DSP/FPGA - Based Parallel Architecture for Real-time image processing

A DSP/FPGA - Based Parallel Architecture for Real-time Image...

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World Congress on Intelligent Control and Automation (WCICA)

作者： Luxin Yan Tianxu Zhang Sheng Zhong Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China Key Laboratory for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

A DSP/FPGA-based parallel architecture oriented to real-time image processing applications is presented. The architecture is structured with high performance DSPs interconnected by FPGA. Within FPGA a FIFO interconnection network and the specific data communication protocol are implemented, which interconnect 3 DSPs (TMS320C6414) effectively. The measured performances in the prototype with the proposed parallel architecture, including inter-DSP data communication performance and system computing capacity, show high data transfer bandwidth (up to 400 Mbytes/s) with low latency as well as high image processing performance, which achieve a good balance for parallel image processing

关键词： Digital signal processing Field programmable gate arrays Parallel architectures image processing Data communication Computer architecture Multiprocessor interconnection networks Protocols Performance evaluation Prototypes

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Dualband Infrared images Target Detection Algorithm Based on Multiscale Morphology

Dualband Infrared Images Target Detection Algorithm Based on...

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2006 8th International Conference on Signal processing

作者： Yuqiu Sun Jinwen Tian Jian Liu School of Information and Mathematics Yangtze University State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial IntelligenceHuazhong University of Science and Technology

According to the features of mid-wave and long-wave infrared images,they are decomposed into morphology pyramid respectively based on the new multiscale mathematical morphology filters proposed in the *** features such as local maximum gray level and average gradient strength of every image are extracted at each level of morphology *** dualband infrared images based on fusion rule put forward in the paper,and then reconstruct original image and detect target using contrast threshold *** experiment results show that dualband infrared images target detection algorithm based on multiscale morphology algorithm is better than use mid-wave or long-wave infrared images detect targets alone.

关键词： image Dualband Infrared images Target Detection Algorithm Based on Multiscale Morphology Target

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Robust Audio Watermark Decoding by Supervised Learning

Robust Audio Watermark Decoding by Supervised Learning

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

作者： S. Kirbiz B. Gunsel Multimedia Signal Processing and Pattern Recognition Laboratory Department of Electronics and Communications Eng. Istanbul Technical University Istanbul Turkey

Most of the watermark (WM) decoding schemes use correlation-based methods because of their simplicity. In these methods, the WM signal embedded through a secret key is assumed as uncorrelated with the host signal. This is a hard restriction that can never be achieved and correlation between the received signal and the secret key becomes greater than zero even though the received signal is un-watermarked. Mostly a decision threshold specified semi-automatically is used at the decoding site. Since the audio watermarking is a nonlinear process that guarantees the inaudibility, there is no analytic way of determining an optimal threshold value that makes the WM decoding problem harder. This paper introduces a learning scheme followed by a nonlinear classification thus eliminates the threshold specification problem. The decoding process is modelled as a three-class classification problem and support vector machines (SVMs) are used in the learning of the embedded data. The decoding and detection performances of the developed system are greater than 98% and 95%, respectively. When the watermark-to-signal-ratio (WSR) is higher than -30 dB, system false alarm ratios remain less than 2%. It is shown that the introduced WM decoding method is robust to additive noise and most of add/remove and filter attacks of Stirmark

关键词： Watermarking Decoding Supervised learning Support vector machines Support vector machine classification Machine learning Meteorological radar Noise robustness Additive noise Filters

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TONGUE SHAPE DETECTION BASED ON B-SPLINE

TONGUE SHAPE DETECTION BASED ON B-SPLINE

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： LIU ZHI JING-QI YAN TAO ZHOU QUN-LIN TANG Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 2000 the graduate school of Harbin Institute of Technology Shenzhen 5180055 China

Human tongue is the only organ of human, which can be laid out. It takes an important role in Traditional Chinese Medicine (TCM) because of its abundant biomedical information. With the progress of modernization of TCM,computerization of tongue diagnosis attracts the researchers'interesting. Thus, how to get an accurate tongue region becomes a key of automatic tongue diagnosis. However, tongue images segmentation is difficult for some physiological properties: tongue is non-rigid and has a high degree of variability in size, shape, color and texture. This paper presents a novel tongue detection method based on *** this method to tongue image, we can get the satisfying results with the respect to robust and veracity.

关键词： Tongue shape Detection B-Spline

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Fast Support Vector Machine Classification using linear SVMs

Fast Support Vector Machine Classification using linear SVMs

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International Conference on pattern recognition

作者： K.Z. Arreola J. Fehr H. Burkhardt LITIS INSA Rouen Saint Etienne du Rouvray France Chair of Pattern Recognition and Image Processing University of Freiburg Freiburg im Breisgau Germany

We propose a classification method based on a decision tree whose nodes consist of linear support vector machines (SVMs). Each node defines a decision hyperplane that classifies part of the feature space. For large classification problems (with many support vectors (SVs)) it has the advantage that the classification time does not depend on the number of SVs. Here, the classification of a new sample can be calculated by the dot product with the orthogonal vector of each hyperplane. The number of nodes in the tree has shown to be much smaller than the number of SVs in a non-linear SVM, thus, a significant speedup in classification time can be achieved. For non-linear separable problems, the trivial solution (zero vector) of a linear SVM is analyzed and a new formulation of the optimization problem is given to avoid it

关键词： Support vector machine classification Support vector machines Classification tree analysis Decision trees pattern recognition image processing Kernel Quadratic programming Principal component analysis

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