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Iris recognition based on adjustable scale wavelet transform

Iris recognition based on adjustable scale wavelet transform

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2005 27th Annual International conference of the Engineering in Medicine and Biology society, ieee-EMBS 2005

作者： Huang, Huifang Hu, Guangshu Department of Biomedical Engineering School of Medicine Tsinghua University Beijing China Department of Biomedical Engineering School of Computer and Information Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)0780387406

This paper represents a new method for iris recognition using the bandpass characteristic of wavelets and wavelet transform principles for detecting singularities. The iris image texture is divided into eight bands, and each band is processed to generate a 1-D intensity signal. The appropriate scale wavelet is used to filter each signal for quantization to 256 byte iris feature codes. Then the Hamming distance is adopted to match two iris codes. In particular, the appropriate scale for each signal is searched for by minimizing the Hamming distance. The results show that the performance of this method is better than some algorithms. Meanwhile, it is translation, rotation, and scale invariant, so it is promising to be used in a personal identification system. © 2005 ieee.

关键词： pattern recognition

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Machine learning for clinical diagnosis from functional magnetic resonance imaging

Machine learning for clinical diagnosis from functional magn...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Lei Zhang D. Samaras D. Tomasi N. Volkow R. Goldstein Department of Computer Science SUNY at Stony Brook Stony Brook NY USA Department of Medical Image Brockhaven National Laboratory Upton NY USA

Functional magnetic resonance imaging (fMRI) has enabled scientists to look into the active human brain. FMRI provides a sequence of 3D brain images with intensities representing brain activations. Standard techniques for fMRI analysis traditionally focused on finding the area of most significant brain activation for different sensations or activities. In this paper, we explore a new application of machine learning methods to a more challenging problem: classifying subjects into groups based on the observed 3D brain images when the subjects are performing the same task. Here we address the separation of drug-addicted subjects from healthy non-drug-using controls. In this paper, we explore a number of classification approaches. We introduce a novel algorithm that integrates side information into the use of boosting. Our algorithm clearly outperformed well-established classifiers as documented in extensive experimental results. This is the first time that machine learning techniques based on 3D brain images are applied to a clinical diagnosis that currently is only performed through patient self-report. Our tools can therefore provide information not addressed by traditional analysis methods and substantially improve diagnosis.

关键词： Machine learning Clinical diagnosis Magnetic resonance imaging Brain Machine learning algorithms Humans Magnetic analysis Learning systems Boosting Information analysis

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Robust centerline extraction framework using level sets

Robust centerline extraction framework using level sets

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conference on computer vision and pattern recognition (cvpr)

作者： M.S. Hassouna A.A. Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY USA

In this paper, we present a novel framework for computing centerlines for both 2D and 3D shape analysis. The framework works as follows: an object centerline point is selected automatically as the point of global maximum Euclidean distance from the boundary, and is considered a point source (Ps) that transmits a wave front that evolves over time and traverses the object domain. The front propagates at each object point with a speed that is proportional to its Euclidean distance from the boundary. The motion of the front is governed by a nonlinear partial differential equation whose solution is computed efficiently using level set methods. Initially, the P/sub S/ transmits a moderate speed wave to explore the object domain and extract its topological information such as merging and extreme points. Then, it transmits a new front that is much faster at centerline points than non central ones. As a consequence, centerlines intersect the propagating fronts at those points of maximum positive curvature. Centerlines are computed by tracking them, starting from each topological point until the Ps is reached, by solving an ordinary differential equation using an efficient numerical scheme. The proposed method is computationally inexpensive, handles efficiently objects with complex topology, and computes centerlines that are centered, connected, one point thick, and less sensitive to boundary noise. In addition, the extracted paths form a tree graph without additional cost. We have extensively validated the robustness of the proposed method both quantitatively and qualitatively against several 2D and 3D shapes.

关键词： Robustness Level set Shape Euclidean distance Partial differential equations Data mining Merging Differential equations Topology Noise shaping

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Real-time, adaptive color-based robot vision

Real-time, adaptive color-based robot vision

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ieee IRS/RSJ International conference on Intelligent Robots and Systems, IROS 2005

作者： Browning, Brett Veloso, Manuela School of Computer Science Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA 15213 United States

ISBN: (纸本)0780389123

With the wide availability, high information content, and suitability for human environments of low-cost color cameras, machine vision is an appealing sensor for many robot platforms. For researchers interested in autonomous robot teams operating in highly dynamic environments performing complex tasks, such as robot soccer, fast color-based object recognition is very desirable. Indeed, there are a number of existing algorithms that have been developed within the community to achieve this goal. Many of these algorithms, however, do not adapt for variation in lighting intensity, thereby limiting their use to statically and uniformly lit indoor environments. In this paper, we present a new technique for color object recognition that can adapt to changes in illumination but remains computationally efficient. We present empirical results demonstrating the performance of our technique for both indoor and outdoor environments on a robot platform performing tasks drawn from the robot soccer domain. Additionally, we compare the computational speed of our new approach against CMvision, a fast open-source color segmentation library. Our performance results show that our technique is able to adapt to lighting variations without requiring significant additional CPU resources. © 2005 ieee.

关键词： Lighting

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On the small sample performance of Boosted classifiers

On the small sample performance of Boosted classifiers

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conference on computer vision and pattern recognition (cvpr)

作者： W. Li X. Gao Y. Zhu Visvanathan Ramesh T.E. Boult Computer Science & Engineering Lehigh University Bethlehem PA USA Real-Time Vision & Modeling Siemens Corporate Research Inc. Princeton NJ USA Computer Science University of Colorado Colorado Springs Colorado Springs CO USA

Boosting algorithms have been widely applied in the machine vision systems. Two fundamental issues that have to be solved in these systems are how much training data and how many Boosting rounds are needed to achieve a desired performance. We view the Boosting algorithm as a nonlinear estimation scheme that estimates a strong classifier from a given training sample set (that is generated by sampling a true unknown distribution), the weak classifiers, and the number of Boosting rounds T. The performance characterization of this estimator involves the derivation of the classification error statistics of the trained strong classifier as a function of the training set and the collection of the weak classifiers. Although the convergence and the error bounds for the training error and generalization error of the algorithms have been studied for several years, the estimated bounds are still loose bounds that are only meaningful for large training sets. With no effective tools for determining the error bounds, users are now collecting training samples with as much data as they can afford, with no good way to know if they are sufficient. In this paper, we characterize the classification error statistics of the trained strong classifier as a function of the true distributions of classes, the collection of the weak classifiers, and the size of the training set. We show that the statistics can be numerically computed and the results are more accurate than previous bounds in the literature. Theoretical results are verified through the simulations. Face detection is used as a case study to illustrate the application of the theory on real data.

关键词： Boosting Error analysis Machine vision Training data Character generation Sampling methods Convergence Statistical distributions Computational modeling Face detection

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Visual concepts for news story tracking: analyzing and exploiting the NIST TRESVID video annotation experiment

Visual concepts for news story tracking: analyzing and explo...

引用

conference on computer vision and pattern recognition (cvpr)

作者： J.R. Kender M.R. Naphade Computer Science Columbia University New York NY USA Business Informatics IBM Thomas J. Watson Research Center Hawthorne NY USA

In the summer of 2003, using an interactive intelligent tool, over 100 researchers in video understanding annotated from the NIST TRECVID database over 62 hours of news video spanning six months of 1998. These 47K shots with 43 3 K labels from over 1000 visual concept categories comprise the largest publicly available ground truth for this domain. Our analysis of this data, combining the tools of statistical natural language processing, machine learning, and computer vision, finds significant novel statistical patterns that can be exploited for the accurate tracking of the episodes of a given news story over time, by using semantic labels that are solely visual. We find that the ground "truth" is very muddy, but by using the feature selection tool of information gain, we extract 14 reliable visual concepts with mid-frequency use; all but one are visual concepts that refer to settings, rather than actors, objects, or events. We discover that the probability of another episode of a named story to recur after a gap of d days is proportional to 1/(d + 1). We define a novel similarity measure incorporating both semantic and temporal properties between episodes i and j as: Dice(i, j)/(1 + gap(i, j)). We exploit a low-level computer vision technique, normalized cut (Laplacian eigenmaps), for clustering these episodes into stories, and in the process document a weakness of this popular technique. We use these empirical results to make specific recommendations on how better visual semantic ontologies for news stories, and how better video annotation tools, should be designed.

关键词： NIST computer vision Deductive databases Visual databases Data analysis pattern analysis Natural language processing Machine learning Data mining Laplace equations

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A two level approach for scene recognition

A two level approach for scene recognition

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conference on computer vision and pattern recognition (cvpr)

作者： Le Lu K. Toyama G.D. Hager Computer Science Department Johns Hopkins University-Applied Physics Laboratory Baltimore MD USA Computer Science Department Johns Hopkins University Baltimore MD One Microsoft Way Microsoft Research Redmond WA

Classifying pictures into one of several semantic categories is a classical image understanding problem. In this paper, we present a stratified approach to both binary (outdoor-indoor) and multiple category of scene classification. We first learn mixture models for 20 basic classes of local image content based on color and texture information. Once trained, these models are applied to a test image, and produce 20 probability density response maps (PDRM) indicating the likelihood that each image region was produced by each class. We then extract some very simple features from those PDRMs, and use them to train a bagged LDA classifier for 10 scene categories. For this process, no explicit region segmentation or spatial context model are computed. To test this classification system, we created a labeled database of 1500 photos taken under very different environment and lighting conditions, using different cameras, and from 43 persons over 5 years. The classification rate of outdoor-indoor classification is 93.8%, and the classification rate for 10 scene categories is 90.1%. As a byproduct, local image patches can be contextually labeled into the 20 basic material classes by using loopy belief propagation (Yedidia et al., 2001) as an anisotropic filter on PDRMs, producing an image-level segmentation if desired.

关键词： Layout Image segmentation Data mining Linear discriminant analysis Context modeling System testing Image databases Spatial databases Cameras Belief propagation

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Computational method for temporal pattern discovery in biomedical genomic databases

Computational method for temporal pattern discovery in biome...

引用

2005 ieee Computational Systems Bioinformatics conference, CSB 2005

作者： Rafiq, Mohammed I. O'Connor, Martin J. Das, Amar K. Stanford Medical Informatics MSOB X233 Stanford CA 94305

ISBN: (纸本)0769523447

With the rapid growth of biomedical research databases, opportunities for scientific inquiry have expanded quickly and led to a demand for computational methods that can extract biologically relevant patterns among vast amounts of data. A significant challenge is identifying temporal relationships among genotypic and clinical (phenotypic) data. Few software tools are available for such pattern matching, and they are not interoperable with existing databases. We are developing and validating a novel software method for temporal pattern discovery in biomedical genomics. In this paper, we present an efficient and flexible query algorithm (called TEMF) to extract statistical patterns from time-oriented relational databases. We show that TEMF-as an extension to our modular temporal querying application (Chronus II)-can express a wide range of complex temporal aggregations without the need for data processing in a statistical software package. We show the expressivity of TEMF using example queries from the Stanford HIV Database. © 2005 ieee.

关键词： pattern recognition

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Improved thumbprint and its application for intrusion detection

Improved thumbprint and its application for intrusion detect...

引用

Third International conference on computer Network and Mobile Computing, ICCNMC 2005

作者： Yang, Jianhua Huang, Shou-Hsuan Stephen Department of Computer Science University of Houston 4800 Calhoun Rd. Houston TX 77204 United States

This paper proposes RTT-thumbprint to traceback intruders, and to detect stepping-stone intrusion;RTT-thumbprint is a sequence of timestamp pairs of a send packet and its corresponding echoed packets. Each pair of timestamps represents a round trip time (RTT) of a packet. Besides the advantages of efficiency, secrecy, and robustness, RTT-thumbprint has the ability to defeat intruder's random delay and chaff manipulation. An exhaustive and a heuristic algorithm are proposed to correlate RTT-thumbprints. The results showed that the heuristic algorithm performs as good as the exhaustive one but is more efficient © Springer-Verlag Berlin Heidelberg 2005.

关键词： pattern recognition

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Discriminant Analysis: A Least Squares Approximation View

Discriminant Analysis: A Least Squares Approximation View

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

作者： Peng Zhang Jing Peng N. Riedel EECS Department Tulane University New Orleans LA USA Mathematics Department Tulane University New Orleans LA USA

Linear discriminant analysis (LDA) is a very important approach to selecting features in classification such as facial recognition. However it suffers from the small sample size (SSS) problem where LDA cannot be solved numerically. The SSS problem occurs when the number of training samples is less than the number of dimensions, which is often the case in practice. Researchers have proposed several modified versions of LDA to deal with this problem. However, a solid theoretical analysis is missing. In this paper, we analyze LDA and the SSS problem based on learning theory. LDA is derived from Fisher’s criterion. However, when formulated as a least square approximation problem, LDA has a direct connection to regularization network (RN) algorithms. Many learning algorithms such as support vector machines (SVMs) can be viewed as regularization networks. LDA turns out to be an RN without the regularization term, which is in general an ill-posed problem. This explains why LDA suffers from the SSS problem. In order to transform the ill-posed problem into a well-posed one, the regularization term is necessary. Thus, based on statistical learning theory, we derive a new approach to discriminant analysis. We call it discriminant learning analysis (DLA). DLA is wellposed and behaves well in the SSS situation. Experimental results are presented to validate our proposal.

关键词： Least squares approximation Linear discriminant analysis Kernel Statistical learning Mathematics Face recognition Solids Approximation algorithms Machine learning Support vector machines

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