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International Conference on pattern recognition

作者： Steffen Wachenfeld Sebastian Terlunen Xiaoyi Jiang Computer Vision and Pattern Recognition Group Department ofComputer Science University of Munster Germany

In this paper we present an algorithm for the recognition of 1D barcodes using camera phones, which is highly robust regarding the the typical image distortions. We have created a database of barcode images, which covers typical distortions, such as inhomogeneous illumination, reflections, or blurriness due to camera movement. We present results from experiments with over 1,000 images from this database using a Matlab implementation of our algorithm, as well as experiments on the go, where a Symbian C++ implementation running on a camera phone is used to recognize barcodes in daily life situations. The proposed algorithm shows a close to 100% accuracy in real life situations and yields a very good resolution dependent performance on our database, ranging from 90.5% (640 × 480) up to 99.2% (2592 × 1944). The database is freely available for other researchers.

关键词： Robustness Cameras Image recognition pattern recognition Image databases Displays Lighting Optical reflection Morphological operations computer vision

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Palmprint recognition using kernel PCA of Gabor features

Palmprint recognition using kernel PCA of Gabor features

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International International Symposium on computer and Information Sciences (ISCIS)

作者： Murat Ekinci Murat Aykut Computer Vision and Pattern Recognition Laboratory Department of Computer Engineering Karadeniz Technical University Trabzon Turkey

This paper presents a new method for automatic palmprint recognition based on kernel PCA method by integrating the Gabor wavelet representation of palm images. Gabor wavelets are first applied to derive desirable palmprint features. The Gabor transformed palm images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. These images can produce salient features that are most suitable for palmprint recognition. The kernel PCA method then nonlinearly maps the Gabor-wavelet image into a high-dimensional feature space. The proposed algorithm has been successfully tested on two different public data sets from the PolyU palmprint databases for which the samples were collected in two different sessions.

关键词： Kernel Principal component analysis Image recognition Testing Biometrics Face recognition Lighting pattern recognition computer vision Image databases

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Graph theoretical framework for simultaneously integrating visual and textual features for efficient web image clustering 08

Graph theoretical framework for simultaneously integrating v...

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17th International Conference on World Wide Web 2008, WWW'08

作者： Rege, Manjeet Dong, Ming Hua, Jing Machine Vision Pattern Recognition Lab. Department ot Computer Science Wayne State University Detroit MI 48202 United States Graphics and Imaging Lab. Department ot Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781605580852

With the explosive growth of Web and the recent development in digital media technology, the number of images on the Web has grown tremendously. Consequently, Web image clustering has emerged as an important application. Some of the initial efforts along this direction revolved around clustering Web images based on the visual features of images or textual features by making use of the text surrounding the images. However, not much work has been done in using multimodal information for clustering Web images. In this paper, we propose a graph theoretical framework for simultaneously integrating visual and textual features for efficient Web image clustering. Specifically, we model visual features, images and words from surrounding text using a tripartite graph. Partitioning this graph leads to clustering of the Web images. Although, graph partitioning approach has been adopted before, the main contribution of this work lies in a new algorithm that we propose - Consistent Isoperimetric High-order Co-clustering (CIHC), for partitioning the tripartite graph. Computationally, CIHC is very quick as it requires a simple solution to a sparse system of linear equations. Our theoretical analysis and extensive experiments performed on real Web images demonstrate the performance of CIHC in terms of the quality, efficiency and scalability in partitioning the visual feature-image-word tripartite graph.

关键词： Digital storage

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A modified approach of identification and tracking of heart boundary from MRI image sequences

A modified approach of identification and tracking of heart ...

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IEEE Region 10 International Conference TENCON

作者： B.B. Chaudhuri Sucharita Mitra Arindam Bhattacharya Sutama Mitra Saugata Dutta Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta India Department of Computer Science and Engineering Heritage Institute of Technology India

In the present work, a robust algorithm for automatic identification and segmentation of heart portion from cardiac Magnetic Resonance video Image (MRI) is presented. At first, an outline has been generated to get the region of interest (ROI) by employing the moving object criterion, which eventually reduces the processing time significantly. In the next step, Expectation Maximization (EM) algorithm is used to segment the grey scale images into 5 distinct regions. This is done to make them more suitable for further processing and easy to use in the developed software. Finally Level set algorithm added with automatic contour generation module is used for tracking the exact heart boundary to segment it out from the rest of the image. This algorithm gives equally persistent result for both long axis and shot axis cardiac MRI data consisting of a movie (in AVI format) containing 32 separate frames of grayscale images.

关键词： Heart Magnetic resonance imaging Image sequences Image segmentation Deformable models Shape Level set Biomedical imaging Noise shaping Gray-scale

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Rural cardiac healthcare system-A scheme for developing countries

Rural cardiac healthcare system-A scheme for developing coun...

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IEEE Region 10 International Conference TENCON

作者： Sucharita Mitra M. Mitra B.B. Chaudhuri Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Applied Physics Faculty of Technology University of Calcutta India

A simple, easy to implement and low cost mobile health(m-health) solution using general packet radio service (GPRS) of globally extended cellular mobile network for economically deprived countries is described in this paper. According to different statistical surveys, heart disease is a major health burden throughout the world but more importantly the third world countries like India, Pakistan, Bangladesh. We consider here a telecardiology system, where the ECG images captured by mobile camera are sent using Multimedia Massage Service (MMS) to a data extraction and processing unit. This unit can extract digitized data from those images, which may be restored as database in a dedicated server and also analyzed by an inbuilt knowledgebase as well as a rough decision generating system. Ultimately, the system will generate an automated report that will suggest the primary treatment and preventive measures to be taken by the patient. The report will be sent back to the patient by Short Message Service (SMS) through the same mobile system. The main advantage of the system is that it will not require constant monitoring or presence of a doctor at the initial stage. The ECG of the patient with critical abnormality or unknown abnormality will be directly sent to the doctorpsilas mobile and intimation will also be sent to the patient site directly. The extreme popularity of mobile phone with camera in the third world countries makes this architecture practically realizable.

关键词： Medical services Electrocardiography Cameras Data mining Costs Packet radio networks Ground penetrating radar Cellular networks Cardiac disease Multimedia systems

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On minimizing errors in 3D reconstruction for stereo camera systems

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pattern recognition and Image Analysis 2007年第2期17卷 337-348页

作者： Wenhardt, S. Denzler, J. Niemann, H. Department of Pattern Recognition University of Erlangen-Nurnberg Martensstr. 3 91058 Erlangen Germany Department of Computer Vision Friedrich Schiller University of Jena Ernst-Abbe-Platz 2 07743 Jena Germany

Active reconstruction of 3D surfaces deals with the control of camer a viewpoints to minimize error and uncertainty in the reconstructed shape of an object. In this paper we develop a mathematical relationship between the setup and focal lengths of a stereo camera system and the corresponding error in 3D reconstruction of a given surface. We explicitly model the noise in the image plane, which can be interpreted as pixel noise or as uncertainty in the localization of corresponding point features. The results can be used to plan sensor positioning, e.g., using information theoretic concepts for optimal sensor data selection. © Nauka/Interperiodica 2007.

关键词： Image reconstruction

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Segmentation of Very Low Resolution Screen-Rendered Text

Segmentation of Very Low Resolution Screen-Rendered Text

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International Conference on Document Analysis and recognition

作者： S. Wachenfeld S. Fleischer H.-U. Klein X. Jiang Computer Vision and Pattern Recognition Group Department of Computer Science University of Muenster Germany

The lower the resolution of a given text is, the more difficult it becomes to segment it into single characters. The resolution of screen-rendered text can be very low. This paper focuses on smoothed screen-rendered text of very low resolution with typical x-heights of 4 to 7 pixels which is much lower than in other low resolution OCR situations. We propose a recognition-based segmentation algorithm which makes use of over segmentation by dynamic programming, candidate rating by single character classifiers and a graph based search algorithm for an optimal cut sequence. The algorithm is described in detail and experimental results are presented which show the performance on example screen- shot images taken from the public Screen-Word database.

关键词： Image segmentation Classification algorithms Optical character recognition software Dynamic programming Focusing Image databases computer vision pattern recognition computer science Character recognition

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Annotated Databases for the recognition of Screen-Rendered Text

Annotated Databases for the Recognition of Screen-Rendered T...

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International Conference on Document Analysis and recognition

作者： S. Wachenfeld H.-U. Klein X. Jiang Computer Vision and Pattern Recognition Group Department of Computer Science University of Münster Germany

The recognition of screen-rendered text is a novel task. It is performed e.g. by translation tools which allow users to click on any text on the screen and give a translation. Also some commercial OCR programs start to address the problem of reading screenshots. Optical character recognition on screen-shot images can be very challenging due to very small and smoothed fonts. In order to build and compare recognition approaches for screen-rendered text, the availability of standard databases is a fundamental prerequisite. In this paper two freely available databases are presented, one that consists of annotated screenshot images of 28080 single characters and another holding 400 words extracted from documents plus 2 400 generated isolated words. Both databases include meta-information such as x-height, font type, style and rendering conditions. At the example of a developed recognition system, it is shown how these databases can serve for training, testing and optimization.

关键词： Text recognition Image databases Optical character recognition software System testing pattern recognition Rendering (computer graphics) Operating systems Character recognition Character generation Application software

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Deriving semantics for image clustering from accumulated user feedbacks

Deriving semantics for image clustering from accumulated use...

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15th ACM International Conference on Multimedia, MM'07

作者： Chen, Yanhua Rege, Manjeet Dong, Ming Fotouhi, Farshad Machine Vision Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States Database and Multimedia Systems Group Department of Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781595937025

Image clustering solely based on visual features without any knowledge or background information suffers from the problem of semantic gap. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for image clustering. Accumulated relevance feedback in a CBIR system is treated as user provided supervision for guiding the image clustering. We consider the set of positive images in the feedback as constraints on the clustering specifying that the images "must" be clustered together. Similarly, negative images provide constraints specifying that they "cannot" be clustered along with the positive images. Through an iterative algorithm, we perform symmetric tri-factorization of the image-image similarity matrix to infer the clustering. Theoretically, we prove the correctness of SS-NMF by showing that the algorithm is guaranteed to converge. Through experiments conducted on general purpose image datasets, we demonstrate the superior performance of SS-NMF for clustering images effectively. Copyright 2007 ACM.

关键词： Image analysis

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Clustering Web images with multi-modal features

Clustering Web images with multi-modal features

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15th ACM International Conference on Multimedia, MM'07

作者： Rege, Manjeet Dong, Ming Hua, Jing Machine Vision Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States Graphics and Imaging Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781595937025

Web image clustering has drawn significant attention in the research community recently. However, not much work has been done in using multi-modal information for clustering Web images. In this paper, we address the problem of Web image clustering by simultaneous integration of visual and textual features from a graph partitioning perspective. In particular, we modelled visual features, images, and words from the surrounding text of the images using a tripartite graph. This graph is actually considered as a fusion of two bipartite graphs that are partitioned simultaneously by the proposed Consistent Isoperimetric High-order Co-clustering(CIHC) framework. Although a similar approach has been adopted before, the main contribution of this work lies in the computational efficiency, quality in Web image clustering and scalability to large image repositories that CIHC is able to achieve. We demonstrate this through experimental results performed on real Web images. Copyright 2007 ACM.

关键词： Image analysis

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