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2008 IEEE Workshop on Applications of Computer Vision最新文献

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Online Character Recognition using Regression Techniques 使用回归技术的在线字符识别
2008 IEEE Workshop on Applications of Computer Vision Pub Date : 2008-01-07 DOI: 10.1109/WACV.2008.4544038
N. Reddy, R. Kandan, K. Shashikiran, S. Sundaram, A. Ramakrishnan
{"title":"Online Character Recognition using Regression Techniques","authors":"N. Reddy, R. Kandan, K. Shashikiran, S. Sundaram, A. Ramakrishnan","doi":"10.1109/WACV.2008.4544038","DOIUrl":"https://doi.org/10.1109/WACV.2008.4544038","url":null,"abstract":"This paper introduces a scheme for classification of online handwritten characters based on polynomial regression of the sampled points of the sub-strokes in a character. The segmentation is done based on the velocity profile of the written character and this requires a smoothening of the velocity profile. We propose a novel scheme for smoothening the velocity profile curve and identification of the critical points to segment the character. We also propose another method for segmentation based on the human eye perception. We then extract two sets of features for recognition of handwritten characters. Each sub-stroke is a simple curve, a part of the character, and is represented by the distance measure of each point from the first point. This forms the first set of feature vector for each character. The second feature vector are the coefficients obtained from the B-splines fitted to the control knots obtained from the segmentation algorithm. The feature vector is fed to the SVM classifier and it indicates an efficiency of 68% using the polynomial regression technique and 74% using the spline fitting method.","PeriodicalId":439571,"journal":{"name":"2008 IEEE Workshop on Applications of Computer Vision","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116016908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Background Subtraction for Temporally Irregular Dynamic Textures 时间不规则动态纹理的背景减法
2008 IEEE Workshop on Applications of Computer Vision Pub Date : 2008-01-07 DOI: 10.1109/WACV.2008.4544010
G. Dalley, J. Migdal, W. Grimson
{"title":"Background Subtraction for Temporally Irregular Dynamic Textures","authors":"G. Dalley, J. Migdal, W. Grimson","doi":"10.1109/WACV.2008.4544010","DOIUrl":"https://doi.org/10.1109/WACV.2008.4544010","url":null,"abstract":"In the traditional mixture of Gaussians background model, the generating process of each pixel is modeled as a mixture of Gaussians over color. Unfortunately, this model performs poorly when the background consists of dynamic textures such as trees waving in the wind and rippling water. To address this deficiency, researchers have recently looked to more complex and/or less compact representations of the background process. We propose a generalization of the MoG model that handles dynamic textures. In the context of background modeling, we achieve better, more accurate segmentations than the competing methods, using a model whose complexity grows with the underlying complexity of the scene (as any good model should), rather than the amount of time required to observe all aspects of the texture.","PeriodicalId":439571,"journal":{"name":"2008 IEEE Workshop on Applications of Computer Vision","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115122635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 54
Learning Optimal Compact Codebook for Efficient Object Categorization 学习高效对象分类的最优紧凑码本
2008 IEEE Workshop on Applications of Computer Vision Pub Date : 2008-01-07 DOI: 10.1109/WACV.2008.4544027
Teng Li, Tao Mei, In-So Kweon
{"title":"Learning Optimal Compact Codebook for Efficient Object Categorization","authors":"Teng Li, Tao Mei, In-So Kweon","doi":"10.1109/WACV.2008.4544027","DOIUrl":"https://doi.org/10.1109/WACV.2008.4544027","url":null,"abstract":"Representation of images using the distribution of local features on a visual codebook is an effective method for object categorization. Typically, discriminative capability of the codebook can lead to a better performance. However, conventional methods usually use clustering algorithms to learn codebooks without considering this. This paper presents a novel approach of learning optimal compact codebooks by selecting a subset of discriminative codes from a large codebook. Firstly, the Gaussian models of object categories based on a single code are learned from the distribution of local features within each image. Then two discriminative criteria, i.e. likelihood ratio and Fisher, are introduced to evaluate how each code contributes to the categorization. We evaluate the optimal codebooks constructed by these two criteria on Caltech-4 dataset, and report superior performance of object categorization compared with traditional K-means method with the same size of codebook.","PeriodicalId":439571,"journal":{"name":"2008 IEEE Workshop on Applications of Computer Vision","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131763209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 27
A Hierarchical Scheme for Rapid Video Copy Detection 一种快速视频拷贝检测的分层方案
2008 IEEE Workshop on Applications of Computer Vision Pub Date : 2008-01-07 DOI: 10.1109/WACV.2008.4543992
Xiao Wu, Yongdong Zhang, Sheng Tang, Tian Xia, Jintao Li
{"title":"A Hierarchical Scheme for Rapid Video Copy Detection","authors":"Xiao Wu, Yongdong Zhang, Sheng Tang, Tian Xia, Jintao Li","doi":"10.1109/WACV.2008.4543992","DOIUrl":"https://doi.org/10.1109/WACV.2008.4543992","url":null,"abstract":"Today with the rapid increasing popularity of web video sharing, digital copyright protection encounters many troubles. Video copy detection schemes are emerging to cope with the digital video piracy and illegal distribution problems. But the large amount of video data and diversity of copy attacks pose difficulties on copy detection. This paper presents a hierarchical scheme to detect video copies, especially the temporal attacked and re-encoded ones. Our algorithm which is based on the ordinal signature of intra frames and effective R*-tree indexing structure archives real time performance. Comparison experiments are conducted on the benchmarked database of CIVR 2007 copy detection showcase and demonstrate the promising results of the proposed approach.","PeriodicalId":439571,"journal":{"name":"2008 IEEE Workshop on Applications of Computer Vision","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134559043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Flexible Edge Arrangement Templates for Object Detection 用于对象检测的灵活边缘排列模板
2008 IEEE Workshop on Applications of Computer Vision Pub Date : 2008-01-07 DOI: 10.1109/WACV.2008.4544002
Yan Li, Yanghai Tsin, Yakup Genç, T. Kanade
{"title":"Flexible Edge Arrangement Templates for Object Detection","authors":"Yan Li, Yanghai Tsin, Yakup Genç, T. Kanade","doi":"10.1109/WACV.2008.4544002","DOIUrl":"https://doi.org/10.1109/WACV.2008.4544002","url":null,"abstract":"We present a novel feature representation for categorical object detection. Unlike previous approaches that have concentrated on generic interest-point detectors, we construct object-specific features directly from the training images. Our feature is represented by a collection of Flexible Edge Arrangement Templates (FEATs). We propose a two-stage semi-supervised learning approach to feature selection. A subset of frequent templates are first selected from a large template pool. In the second stage, we formulate feature selection as a regression problem and use LASSO method to find the most discriminative templates from the preselected ones. FEATs adaptively capture the image structure and naturally accommodate local shape variations. We show that this feature can be complemented by the traditional holistic patch method, thus achieving both efficiency and accuracy. We evaluate our method on three well-known car datasets, showing performance competitive with existing methods.","PeriodicalId":439571,"journal":{"name":"2008 IEEE Workshop on Applications of Computer Vision","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133616895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A non parametric approach for modeling interferometric SAR imagery and applications 干涉SAR图像建模的非参数化方法及其应用
2008 IEEE Workshop on Applications of Computer Vision Pub Date : 2008-01-07 DOI: 10.1109/WACV.2008.4544025
K. Sengupta, P. Burman
{"title":"A non parametric approach for modeling interferometric SAR imagery and applications","authors":"K. Sengupta, P. Burman","doi":"10.1109/WACV.2008.4544025","DOIUrl":"https://doi.org/10.1109/WACV.2008.4544025","url":null,"abstract":"In this paper, we present a non parametric modeling for phase maps of interferometric SAR. Cosine and Sine projections maps are generated from the SAR phase map, and each of them are individually modeled by fitting 2D basis functions. The coefficients of these basis functions describe a \"smoothed\" version of the original phase map. Several applications can be derived from this noise filtered phase map: better phase unwrapping and SAR image compression are to of the applications that we will be discussing in the paper. The approach can be extended to other imaging domains that involve large maps of directional or phase data, such as modeling of phase MRI images, modeling of wind directions in meteorological data, etc.","PeriodicalId":439571,"journal":{"name":"2008 IEEE Workshop on Applications of Computer Vision","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125235431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Efficient Active Camera Model for Video Surveillance 一种用于视频监控的高效有源摄像机模型
2008 IEEE Workshop on Applications of Computer Vision Pub Date : 2008-01-07 DOI: 10.1109/WACV.2008.4544032
K. Sankaranarayanan, James W. Davis
{"title":"An Efficient Active Camera Model for Video Surveillance","authors":"K. Sankaranarayanan, James W. Davis","doi":"10.1109/WACV.2008.4544032","DOIUrl":"https://doi.org/10.1109/WACV.2008.4544032","url":null,"abstract":"We propose an efficient active camera model to map image coordinates to the camera's pan-tilt orientations in constant time. The model is based on the elliptical locus of the projections of a fixed point on the original image plane of a moving camera. The parametric location of this point along the ellipse defines the change in camera orientation. This model does not require any knowledge of camera parameters other than the focal length. Using synthetic and real data, we show the accuracy of the model by generating seamless spherical panoramas from a set of images and demonstrate the applicability of the model with a real-time active tracking application.","PeriodicalId":439571,"journal":{"name":"2008 IEEE Workshop on Applications of Computer Vision","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115901154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
EAVA: A 3D Emotive Audio-Visual Avatar EAVA: 3D情感视听化身
2008 IEEE Workshop on Applications of Computer Vision Pub Date : 2008-01-07 DOI: 10.1109/WACV.2008.4544003
Hao Tang, Yun Fu, J. Tu, Thomas S. Huang, M. Hasegawa-Johnson
{"title":"EAVA: A 3D Emotive Audio-Visual Avatar","authors":"Hao Tang, Yun Fu, J. Tu, Thomas S. Huang, M. Hasegawa-Johnson","doi":"10.1109/WACV.2008.4544003","DOIUrl":"https://doi.org/10.1109/WACV.2008.4544003","url":null,"abstract":"Emotive audio-visual avatars have the potential of significantly improving the quality of Human-Computer Interaction (HCI). In this paper, the various technical approaches of a novel framework leading to a text-driven 3D Emotive Audio-Visual Avatar (EAVA) are proposed. Primary work is focused on 3D face modeling, realistic emotional facial expression animation, emotive speech synthesis, and the co-articulation of speech gestures (i.e., lip movements due to speech production) and facial expressions. Experimental results clearly indicate that a certain degree of naturalness and expressiveness has been achieved by EAVA in both audio and visual aspects. Promising potential improvements can be expected by incorporating various data-driven statistical learning models into the framework.","PeriodicalId":439571,"journal":{"name":"2008 IEEE Workshop on Applications of Computer Vision","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121327086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
A Computer Vision based Whiteboard Capture System 基于计算机视觉的白板采集系统
2008 IEEE Workshop on Applications of Computer Vision Pub Date : 2008-01-07 DOI: 10.1109/WACV.2008.4544028
R. Xu
{"title":"A Computer Vision based Whiteboard Capture System","authors":"R. Xu","doi":"10.1109/WACV.2008.4544028","DOIUrl":"https://doi.org/10.1109/WACV.2008.4544028","url":null,"abstract":"Conventional whiteboard video capture using a static camera usually results in a poor quality. In this paper, we present an autonomous whiteboard scan and capture prototype system, which consist a pair of static and pan-tilt-zoom (PTZ) cameras. The PTZ camera is used to scan the newly-updated whiteboard regions without interrupting the instructor. We will illustrate several computer vision techniques used in our system: Firstly, we present our unique camera calibration method using rough hand-drawn gridlines. Secondly, we present the image processing methods used to determine where the newly updated whiteboard region to be scanned is. Our method also accounts for the whiteboard region occlusion from the instructor.","PeriodicalId":439571,"journal":{"name":"2008 IEEE Workshop on Applications of Computer Vision","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115070949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Urban building recognition during significant temporal variations 城市建筑识别在显著的时间变化
2008 IEEE Workshop on Applications of Computer Vision Pub Date : 2008-01-07 DOI: 10.1109/WACV.2008.4544000
G. P. Nguyen, H. J. Andersen, M. Christensen
{"title":"Urban building recognition during significant temporal variations","authors":"G. P. Nguyen, H. J. Andersen, M. Christensen","doi":"10.1109/WACV.2008.4544000","DOIUrl":"https://doi.org/10.1109/WACV.2008.4544000","url":null,"abstract":"In literature, existing researches on building recognition mainly concentrate on scales, rotations, and viewpoints variance. In urban environment, large temporal variations of weather and lighting conditions should also be considered as major challenges for robust recognition. For instances, there are differences between images captured during daytime and nighttime, especially significant changes in building appearances between seasons because of the differences in light setting. To date, these large temporal variation issues have not been fully investigated. In this paper, we therefore focus on constructing a system that deals with the temporal difference factors in recognizing urban buildings. In order to build such a system, two main criteria are raised, namely the efficiency of the recognition algorithm and the speed for interactive search purpose. For recognition purpose, we exploit the MOPS features (Multi-scale Oriented Patches) in [2], which extract features of patches around interest points. To speed up the searching process, we employ the vocabulary tree based search technique in [12]. Our final system shows high performance in recognizing buildings under significant temporal variations with a fast processing reaction.","PeriodicalId":439571,"journal":{"name":"2008 IEEE Workshop on Applications of Computer Vision","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122244057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
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