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A computational framework for real-time detection and recognition of large number of classes

A computational framework for real-time detection and recogn...

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33rd Applied Imagery pattern recognition Workshop

作者： Liu, Xiuwen Department of Computer Science Florida State University Tallahassee FL 32306 United States

ISBN: (纸本)0769522505

Inspired by recent advances in real-time vision for certain applications, we propose a framework for developing and implementing systems that are capable of detecting and recognizing a large number of objects in real time on a top desktop workstation with field programmable gate array (FPGA) devices. To avoid explicit segmentation, detection and recognition is performed by scanning through local windows of input scenes at multiple scales. This is achieved by using a new feature family (named as topological local spectral histogram (ToLoSH) features, consisting of histograms of local regions of filtered images) and a lookup table decision tree (i.e. a decision tree where each node is implemented as lookup tables) as the classifier to reduce the average time per local window while achieving high accuracy. We show through analysis and empirical studies that ToLoSH features are effective to discriminate a large number of object classes and can be computed using only three instructions. Given the choice of the ToLoSH feature family and lookup table decision tree classifiers, the problem of real-time scene interpretation becomes a joint optimization problem of learning an optimal classifier and associated optimal ToLoSH features. To show the feasibility of the proposed framework, we have constructed a decision lookup table tree for a dataset consisting of textures, faces, and objects. We argue that the proposed framework may reconcile some of the fundamental issues in visual recognition modeling. © 2005 ieee.

关键词： Object recognition

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Gene Selection via a Spectral Approach

Gene Selection via a Spectral Approach

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： L. Wolf A. Shashua S. Mukherjee The McGovern Institute for brain research M.I.T. Cambridge MA USA School of Engineering School of Engineering Hebrew University Jerusalem Israel Institute of Statistics and Decision Sciences Duke University Durham NC USA

Array technologies have made it possible to record simultaneously the expression pattern of thousands of genes. A fundamental problem in the analysis of gene expression data is the identification of highly relevant genes that either discriminate between phenotypic labels or are important with respect to the cellular process studied in the experiment. Examples include: cell cycle or heat shock in yeast experiments, chemical or genetic perturbations of mammalian cell lines, and genes involved in class discovery for human tumors. We focus on the task of unsupervised gene selection. Selecting a small subset of genes is particularly challenging as the data sets involved are typically characterized by a small sample size and a very large feature space. We propose a model independent approach which scores candidate gene selections using spectral properties of the candidate af?nity matrix. The algorithm is simple to implement, yet contains a number of remarkable properties which guarantee consistent sparse selections. We applied our algorithm on five different data sets. The first consists of time course data from four well studied Hematopoietic cell lines (HL-60, Jurkat, NB4, and U937). The other four data sets include three well studied treatment outcomes (large cell lymphoma, childhood medulloblastomas, breast tumors) and one unpublished data set (lymph status). We compared our approach both with other unsupervised methods (SOM,PCA,GS) and with supervised methods (SNR,RMB,RFE). The results show that our approach considerably outperforms all the other unsupervised approaches in our study, is competitive with supervised methods and in some cases even outperforms supervised approaches.

关键词： Gene expression Neoplasms Statistics computer science Electric shock Fungi Chemicals Genetics Humans Sparse matrices

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Face recognition based on generalized canonical correlation analysis

Face recognition based on generalized canonical correlation ...

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International conference on Intelligent Computing, ICIC 2005

作者： Sun, Quan-Sen Heng, Pheng-Ann Jin, Zhong Xia, De-Shen School of Science Jinan University Jinan 250022 China Department of Computer Science Nanjing University of Science and Technology Nanjing 210094 China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Hong Kong Centre de Visió per Computador Universitat Autònoma de Barcelona Spain

We have proposed a new feature extraction method and a new feature fusion strategy based on generalized canonical correlation analysis (GCCA). The proposed method and strategy have been applied to facial feature extraction and recognition. Compared with the face feature extracted by canonical correlation analysis (CCA), as in a process of GCCA, it contains the class information of the training samples, thus, aiming for pattern classification it would improve the classification capability. Experimental results on ORL and Yale face image database have shown that the classification results based on GCCA method are superior to those based on CCA method. Moreover, those two methods are both better than the classical Eigenfaces or Fishierfaces method. In addition, the newly proposed feature fusion strategy is not only helpful for improving the recognition rate, but also useful for enriching the existing combination feature extraction methods. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Face recognition

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Estimation of critical parameters in concrete production using multispectral vision technology

Estimation of critical parameters in concrete production usi...

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14th Scandinavian conference on Image Analysis, SCIA 2005

作者： Hansen, Michael E. Ersbøll, Bjarne K. Carstensen, Jens M. Nielsen, Allan A. Building 321 Technical University of Denmark Richard Peternsens Plads Denmark Videometer A/S Lyngsø Allé 3 DK-2970 Hørsholm Denmark

We analyze multispectral reflectance images of concrete aggregate material, and design computational measures of the important and critical parameters used in concrete production. The features extracted from the images are exploited as explanatory variables in regression models and used to predict aggregate type, water content, and size distribution. We analyze and validate the methods on five representative aggregate types, commonly used in concrete production. Using cross validation, the generated models proves to have a high performance in predicting all of the critical parameters. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Parameter estimation

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Image pattern recognition with separable Trade-off correlation filters

Image pattern recognition with separable Trade-off correlati...

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7th International conference on Advanced Concepts for Intelligent vision Systems, ACIVS 2005

作者： Martín, César San Vargas, Asticio Campos, Juan Torres, Sergio Department of Electrical Engineering University of La Frontera Casilla 54-D Temuco Chile Department of Science Physics University of La Frontera Casilla 54-D Temuco Chile Department of Physics University of Barcelona 08193 Bellaterra Barcelona Spain Department of Electrical Engineering University of Concepción Casilla 160-C Concepción Chile

ISBN: (纸本)354029032X

In this paper, a method to design separable trade-off correlation filters for optical pattern recognition is developed. The proposed method not only is able to include the information about de desirable peak correlation value but also is able to minimize both the average correlation energy and the effect of additive noise on the correlation output. These optimization criteria are achieved by employing multiple training objects. The main advantage of the method is based on using multiple information for improving the optical pattern recognition work on images with various objects. The separable Trade-off filter is experimentally tested by using both digital and optical pattern recognition. © Springer-Verlag Berlin Heidelberg 2005.

关键词： pattern recognition

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A two-step area based method for automatic Tight segmentation of zona pellucida in HMC images of human embryos 1

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5th International conference on Scale Space and PDE Methods in computer vision, Scale-Space 2005

作者： Karlsson, Adam Overgaard, Niels Chr. Heyden, Anders Applied Mathematics Group School of Technology and Society Malmö University Sweden

ISBN: (数字)9783540320128

ISBN: (纸本)9783540255475

An important prognostic parameter for assessing the success of an in vitro fertilization treatment is the variation in thickness of the zona pellucida. Zona pellucida, the envelope of the human embryo, is usually visualized using Huffman modulation contrast microscopy (HMC). This paper considers automatic segmentation of zona pellucida in HMC images of human embryos. There are two subproblems: (a) the embryo should be separated from the background and (b) the zona should be separated from the rest of the embryo, (a) is solved using a robust formulation of a classical area based method and (b) is solved using a probabilistic method. Both solutions are set in a variational framework using a novel image model for the zona. This variational framework is adapted to handle images in which large artefacts are covered with masks. Since the zona has a simple topology we focus on parametric models and a representation by trigonometric sums is considered. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Image segmentation

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Phase contrast MRI segmentation using velocity and intensity

Phase contrast MRI segmentation using velocity and intensity

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5th International conference on Scale Space and PDE Methods in computer vision, Scale-Space 2005

作者： Persson, Markus Solem, Jan Erik Markenroth, Karin Svensson, Jonas Heyden, Anders Applied Mathematics Group School of Technology and Society Malmö University Sweden Philips Medical Systems MR Department Lund University Hospital Sweden Department of Radiation Physics Malmö University Hospital Sweden

This paper presents a method for three-dimensional (3D) segmentation of blood vessels, i.e. determining the surface of the vessel wall, using a combination of velocity data and magnitude images obtained using phase contrast MRI. In addition to standard MRI images, phase contrast MRI gives velocity information for blood and tissue in the human body. The proposed method uses a variational formulation of the segmentation problem which combines different cues;velocity and magnitude. The segmentation is performed using the level set method. Experiments on phantom data and clinical data support the proposed method. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Image segmentation

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Quaternion correlation filters for face recognition in wavelet domain

Quaternion correlation filters for face recognition in wavel...

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2005 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Xie, Chunyan Savvides, Marios Vijaya Kumar, B.V.K. Carnegie Mellon University Department of Electrical and Computer Engineering 5000 Forbes Avenue Pittsburgh PA 15213 United States

ISBN: (纸本)0780388747

In this paper, a new frequency domain face recognition method using wavelet decomposition and quaternion correlation filters is proposed. The wavelet decomposition of the face image leads to a wavelet subband representation, which contains four subband images corresponding to four orthogonal channels. These four subbands can be encoded into a 2-D quaternion number array. The quaternion correlation filter method is developed for performing pattern recognition on multichannel 2-D signal jointly. The proposed method has been shown to achieve significant improvement in face recognition results compared to traditional advanced correlation filter method for handling illumination variations of face images. We experimented with the CMU PIE database consisting of 65 people with 21 illumination variations per person, showing that our method can achieve close to 100% recognition accuracy using just a single training image of a person under neutral frontal lighting and testing on all other un-seen harsh illuminations. © 2005 ieee.

关键词： Image analysis

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Image parsing: Unifying segmentation, detection, and recognition

Image parsing: Unifying segmentation, detection, and recogni...

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9th ieee International conference on computer vision

作者： Tu, ZW Chen, XG Yuille, AL Zhu, SC Univ Calif Los Angeles Dept Stat Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Psychol Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA

ISBN: (纸本)0769519504

In this paper we present a Bayesian framework for parsing images into their constituent visual patterns. The parsing algorithm optimizes the posterior probability and outputs a scene representation as a "parsing graph", in a spirit similar to parsing sentences in speech and natural language. The algorithm constructs the parsing graph and re-configures it dynamically using a set of moves, which are mostly reversible Markov chain jumps. This computational framework integrates two popular inference approaches-generative (top-down) methods and discriminative (bottom-up) methods. The former formulates the posterior probability in terms of generative models for images defined by likelihood functions and priors. The latter computes discriminative probabilities based on a sequence (cascade) of bottom-up tests/filters. In our Markov chain algorithm design, the posterior probability, defined by the generative models, is the invariant (target) probability for the Markov chain, and the discriminative probabilities are used to construct proposal probabilities to drive the Markov chain. Intuitively, the bottom-up discriminative probabilities activate top-down generative models. In this paper, we focus on two types of visual patterns-generic visual patterns, such as texture and shading, and object patterns including human faces and text. These types of patterns compete and cooperate to explain the image and so image parsing unifies image segmentation, object detection, and recognition of we use generic visual patterns only then image parsing will correspond to image segmentation (Tu and Zhu, 2002. ieee Trans. PAM1, 24(5):657-673). We illustrate our algorithm on natural images of complex city scenes and show examples where image segmentation can be improved by allowing object specific knowledge to disambiguate low-level segmentation cues, and conversely where object detection can be improved by using generic visual patterns to explain away shadows and occlusions.

关键词： image parsing image segmentation object detection object recognition data driven Markov Chain Monte Carlo AdaBoost

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Team pattern recognition: Sharing cognitive chunks under time pressure

Team pattern recognition: Sharing cognitive chunks under tim...

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38th Annual Hawaii International conference on System Sciences, HICSS 2005

作者： Hayne, Stephen C. Smith, C.A.P. Vijayasarathy, Leo

This study extends the theory of recognition Primed Decision-Making by applying it to groups. Furthermore, we explore the application of Template Theory to collaboration. An experiment was conducted in which teams made resource allocation decisions while physically dispersed and supported with a shared virtual work surface (What You See Is What I See - WYSIWIS) both with and without time-pressure. The task required teams to recognize patterns and collaborate to allocate their resources appropriately. The experiment explores the use of a cognitively aligned tool (memory chunks) designed to minimize the cognitive effort required to for teams to recognize and share recognized patterns. Dependent measures included outcome quality, resource allocation time, and resource allocation ordering. All teams received significant financial rewards in direct proportion to their outcome quality and decision speed. Teams supported with the pattern-sharing tool had high outcome quality even under time pressure.

关键词： pattern recognition

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