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Face recognition Using Most Discriminative Local and Global Features

Face Recognition Using Most Discriminative Local and Global ...

引用

International conference on pattern recognition

作者： Yong Gao Yangsheng Wang Xuetao Feng Xiaoxu Zhou Institute of Automation Chinese Academy and Sciences China

ISBN: (纸本)0769525210

Numerous studies in psychophysics and neurophysiological literatures have shown that both local and global features are important for representing and recognizing face. In this paper, a face recognition method, using local and global multi-resolution discriminative information, is proposed. First, face is represented by multi-scale and multi-orientation Gabor features. Then AdaBoost is employed to learn local feature classifier, and LDA (linear discriminant analysis) is used to extract global discriminative information. Finally, their recognition results are fused. We evaluate both score and rank based combination schemes on FERET and XM2VTS face databases. Experimental results demonstrate that almost all combination methods improve recognition rates and the best fusion method achieves 99% rank-1 recognition rate on FERET fb probe set

关键词： Face recognition Gabor filters Face detection Linear discriminant analysis Psychology Spatial databases Probes Robustness computer vision pattern recognition

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Motion Segmentation by Multibody Trifocal Tensor Using Line Correspondence

Motion Segmentation by Multibody Trifocal Tensor Using Line ...

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International conference on pattern recognition

作者： Jing Zhang Fanhuai Shi Yuncai Liu Image Processing and Pattern Recognition Institute Shanghai Jiaotong University Shanghai China Image Processing and Pattern Recognition Institute School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai P. R. China

ISBN: (纸本)0769525210

We present a geometric approach to multibody motion segmentation from line correspondences. Given three perspective views containing multiple moving objects, we demonstrate that after applying a polynomial embedding to the line correspondences, they became related by the so-called multibody trilinear constrain and its associated multibody trifocal tensor. We show how to obtain the individual trifocal tensor from the second order derivatives of the multibody trilinear constraint. Given trifocal tensors, we obtain the clustering of the motions and correspondences. Experimental results on synthetic and real dynamic scenes are presented

关键词： Motion segmentation computer vision Tensile stress Layout Cameras Motion estimation pattern recognition Image processing Materials science and technology Polynomials

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Informative Shape Representations for Human Action recognition

Informative Shape Representations for Human Action Recogniti...

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International conference on pattern recognition

作者： Liang Wang D. Suter ARC Centre for Perceptive and Intelligent Machines in Complex Environments Monash University Clayton VIC Australia

ISBN: (纸本)0769525210

Shape and kinematics are two important cues in human movement analysis. Due to real difficulties in extracting kinematics from videos accurately, this paper proposes to address the problem of human action recognition by spatiotemporal shape analysis. Without explicit feature tracking and complex probabilistic modeling of human movements, we directly convert an associated sequence of human silhouettes derived from videos into two types of computationally efficient representations, i.e., average motion energy and mean motion shape, to characterize actions. Supervised pattern classification techniques using various distance measures are used for recognition. The encouraging experimental results are obtained on a recent dataset including 10 different actions from 9 subjects

关键词： Shape Humans Videos Tracking Kinematics Spatiotemporal phenomena pattern recognition Optical computing Image motion analysis computer vision

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Two-Dimensional Matrix Principal Component Analysis Useful for Character recognition

Two-Dimensional Matrix Principal Component Analysis Useful f...

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International conference on Information and Automation for Sustainability (ICIAfS)

作者： V N Manjunath Aradhya G Hemantha Kumar S Noushath Department of Studies in Computer Science University of Mysore Mysore India

Low dimensional feature representation with enhanced discriminatory power is of paramount importance to any recognition systems. Principal component analysis (PCA) is a classical feature extraction and data representation technique widely used in the area of pattern recognition and computer vision. In this paper, two-dimensional Principal Component Analysis (2D-PCA) is presented for character image representation. 2D-PCA is based on 2D image matrices rather than 1D vectors so that image matrix does not need to be transform into a vector prior to feature extraction as done in PCA. Experimental results on character database (Printed and Handwritten) showed a good recognition rate compared to other existing methods.

关键词： Principal component analysis Character recognition Optical character recognition software Feature extraction Handwriting recognition Support vector machines Support vector machine classification Optical sensors Natural languages Covariance matrix

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Space-Time Moment Invariants and recognition of Non-Rigid Motions from Arbitrary Viewpoints

Space-Time Moment Invariants and Recognition of Non-Rigid Mo...

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International conference on pattern recognition

作者： T. Yamada Jun Sato Department of Computer Science and Engineering Nagoya Institute of Technology Nagoya Japan

The geometric invariants are very useful for recognizing objects in the scene from arbitrary viewpoints. Recently, it has been shown that some geometric invariants can be defined in the space with time domain, and they can be applied for recognizing dynamic motions from arbitrary viewpoints. However, the existing invariants on motions are computed from multiple points in the sequence of images, and thus accurate tracking of image features is required. As a result, if tracking of image features fails, these invariants cannot be computed. In this paper, we propose invariants on motions, which are defined by using image moments. We show that by using the proposed moment invariants on motions, we can recognize non-rigid motions from arbitrary viewpoints without tracking image features. We also show that they can reflect entire changes in object shapes and motions in image sequences

关键词： Tracking Image recognition Shape Layout Space technology Image sequences Cameras Gravity computer science computer vision

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Evolutionary Learning of Primitive-Based Visual Concepts

Evolutionary Learning of Primitive-Based Visual Concepts

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Congress on Evolutionary Computation

作者： K. Krawiec Institute of Computing Science Poznań University of Technology Poznan Poland

The paper presents a novel method of evolutionary learning dedicated to acquisition of visual concepts. The learning process takes place in a population of genetic programming-based learners that process attributed visual primitives derived from raw raster images. The approach uses an original evaluation scheme: evolving individuals-learners are rewarded for being able to sketch the input visual stimulus. recognition proceeds here as an attempt of restoring essential features of the input image. The approach is general by being based mostly on universal vision knowledge; only very limited amount of a priori knowledge about the particular application or target concept to be learned is required. We explain the method in detail and verify it experimentally on acquisition of simple visual concepts (triangle and section) from examples.

关键词： Image recognition pattern recognition Humans Genetic programming Image restoration Machine vision computer vision Design methodology Image processing Image analysis

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Data Fusion and Confidence Measure in Image Feature Detection

Data Fusion and Confidence Measure in Image Feature Detectio...

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

作者： Qiuyu Zhu Sujuan Huang Shanghai University China

The data fusion approaches for uncorrelated and correlated data are carried out in image feature detection, which integrate information of the same feature based on multiple methods using a statistical approach. Validity of the scheme and properties of the fused data are discussed. A confidence measure is defined, and applied to evaluate credibility of the result. The technique can be used to reduce adverse effects of individual feature detection errors, and improve the rate of pattern recognition. Examples of facial canthus detection and the corresponding confidence levels are presented.

关键词： computer vision pattern recognition Image edge detection Face detection Data mining Image color analysis Feature extraction Shape Image analysis Detection algorithms

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Adaptive Discriminant Projection for Content-based Image Retrieval

Adaptive Discriminant Projection for Content-based Image Ret...

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International conference on pattern recognition

作者： Jie Yu Qi Tian Department of Computer Science University of Texas San Antonio USA

Content-based image retrieval (CBIR) is a computer vision application that aims at automatically retrieving images based on their visual content. Linear discriminant analysis and its variants have been widely used in CBIR applications because of their effectiveness in finding a projection that maps the original high-dimensional space to a low-dimensional one and preserves the most discriminant features. Those techniques assume images from certain class(es) are all visually similar and try to cluster them in the projected space. In this paper we show that the human high-level concept of semantic similarity between images may not arise only from the low-level visual similarity and consequently that assumption is inappropriate in many cases. We propose an adaptive discriminant projection (ADP) framework which could model different data distributions based on the clustering of different classes. To learn the best model fitting the real scenario, boosted adaptive discriminant projection is further proposed. Extensive experiments are designed to evaluate our methods and compare them to the state-of-the-art techniques on benchmark data set and real image retrieval applications. The results show the superior performance of our proposed methods

关键词： Image retrieval Content based retrieval Linear discriminant analysis Application software Information retrieval computer science computer vision Humans Principal component analysis Scattering

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Embodied Proactive Human Interface "PICO-2"

Embodied Proactive Human Interface "PICO-2"

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International conference on pattern recognition

作者： R. Kurazume H. Omasa S. Uchida R. Taniguchi T. Hasegawa Kyushu University Fukuoka Japan

We are conducting research on "embodied proactive human interface". The aim of this research is to develop a new human-friendly active interface based on two key technologies, an estimation mechanism of human intention for supporting natural communication named "proactive interface", and a tangible device using robot technology This paper introduces the humanoid-type two-legged robot named "PICO-2 ", which was developed as a tangible telecommunication device for the proactive human interface. In order to achieve the embodied telecommunication with PICO-2, we propose new tracking technique of human gestures using a monocular video camera mounted on PICO-2, and natural gesture reproduction by PICO-2 which absorbs the difference of body structure between the user and the robot

关键词： Humans Humanoid robots computer interfaces Robot sensing systems computer aided instruction Robot vision systems Cameras computer displays Planing Motion estimation

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Shock Graph for Shape recognition

Shock Graph for Shape Recognition

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Student conference on Research and Development (SCOReD)

作者： Nooritawati Md Tahir Aini Hussain Salina Abdul Samad Hafizah Husain Mohd Marzuki Mustafa Dept. of Electrical Electronic and Systems Universiti Kebangsaan Malaysia Bangi Selangor DE MALAYSIA

Shock graphs have emerged as a powerful generic 2-D shape representation. In this paper, we propose a framework to address the problem of generic 2-D shape recognition. The aim is to use shock graphs as shape representation for recognition where features of objects are detected for recognition and classification. We propose to represent the medial axis characteristic points as an attribute to model the shape. The information about the object shape and its topology is totally embedded in them and this allows the potential to compare different shapes by their shock graph. The techniques developed are skeletonization, followed by thinning and finally detection of end points and junction points that are present in every region of the shock graph for possible modelling of shape recognition. Experimental results demonstrate the correctness in detecting its characteristic points for recognition. It will be shown that this framework is effective in shape recognition. The algorithms were tested with wide varieties of 2D shapes. Research on this promising approach is ongoing, focusing both on new applications and on techniques that will further enhance its performance in a variety of computer vision and pattern recognition domains.

关键词： Electric shock Shape Skeleton Topology Image analysis Application software computer vision Object detection Research and development Systems engineering and theory

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