2007 IEEE Conference on Computer Vision and Pattern Recognition最新文献_第5页

Application of the Reeb Graph Technique to Vehicle Occupant's Head Detection in Low-resolution Range Images Reeb图技术在低分辨率距离图像中汽车乘员头部检测中的应用

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383450

P. Devarakota, M. Castillo-Franco, R. Ginhoux, B. Mirbach, B. Ottersten

{"title":"Application of the Reeb Graph Technique to Vehicle Occupant's Head Detection in Low-resolution Range Images","authors":"P. Devarakota, M. Castillo-Franco, R. Ginhoux, B. Mirbach, B. Ottersten","doi":"10.1109/CVPR.2007.383450","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383450","url":null,"abstract":"In [3], a low-resolution range sensor was investigated for an occupant classification system that distinguish person from child seats or an empty seat. The optimal deployment of vehicle airbags for maximum protection moreover requires information about the occupant's size and position. The detection of occupant's position involves the detection and localization of occupant's head. This is a challenging problem as the approaches based on local shape analysis (in 2D or 3D) alone are not robust enough as other parts of the person's body like shoulders, knee may have similar shapes as the head. This paper discusses and investigate the potential of a Reeb graph approach to describe the topology of vehicle occupants in terms of a skeleton. The essence of the proposed approach is that an occupant sitting in a vehicle has a typical topology which leads to different branches of a Reeb Graph and the possible location of the occupant's head are thus the end points of the Reeb graph. The proposed method is applied on real 3D range images and is compared to Ground truth information. Results show the feasibility of using topological information to identify the position of occupant's head.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133915491","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

Unsupervised Clustering using Multi-Resolution Perceptual Grouping 基于多分辨率感知分组的无监督聚类

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.382986

T. Syeda-Mahmood, Fei Wang

引用次数: 5

A Topic-Motion Model for Unsupervised Video Object Discovery 无监督视频对象发现的主题-运动模型

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383220

David Liu, Tsuhan Chen

引用次数: 47

A New Method for Object Tracking Based on Regions Instead of Contours 一种基于区域代替轮廓的目标跟踪新方法

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383454

N. Gómez, R. Alquézar, F. Serratosa

{"title":"A New Method for Object Tracking Based on Regions Instead of Contours","authors":"N. Gómez, R. Alquézar, F. Serratosa","doi":"10.1109/CVPR.2007.383454","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383454","url":null,"abstract":"This paper presents a new method for object tracking in video sequences that is especially suitable in very noisy environments. In such situations, segmented images from one frame to the next one are usually so different that it is very hard or even impossible to match the corresponding regions or contours of both images. With the aim of tracking objects in these situations, our approach has two main characteristics. On one hand, we assume that the tracking approaches based on contours cannot be applied, and therefore, our system uses object recognition results computed from regions (specifically, colour spots from segmented images). On the other hand, we discard to match the spots of consecutive segmented images and, consequently, the methods that represent the objects by structures such as graphs or skeletons, since the structures obtained may be too different in consecutive frames. Thus, we represent the location of tracked objects through images of probabilities that are updated dynamically using both recognition and tracking results in previous steps. From these probabilities and a simple prediction of the apparent motion of the object in the image, a binary decision can be made for each pixel and abject.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133489389","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: 5

Learning and Matching Line Aspects for Articulated Objects 铰接对象的线方面学习与匹配

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383021

Xiaofeng Ren

引用次数: 19

Photometric Self-Calibration of a Projector-Camera System 投影-摄像系统的光度自校正

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383468

Ray Juang, A. Majumder

引用次数: 30

Inferring Grammar-based Structure Models from 3D Microscopy Data 从3D显微镜数据推断基于语法的结构模型

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383031

J. Schlecht, Kobus Barnard, Ekaterina H. Spriggs, B. Pryor

引用次数: 26

On the Efficacy of Correcting for Refractive Effects in Iris Recognition 虹膜识别中屈光效应的校正效果研究

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383380

J. R. Price, T. Gee, V. Paquit, K. Tobin

{"title":"On the Efficacy of Correcting for Refractive Effects in Iris Recognition","authors":"J. R. Price, T. Gee, V. Paquit, K. Tobin","doi":"10.1109/CVPR.2007.383380","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383380","url":null,"abstract":"In this study, we aim to determine if iris recognition accuracy might be improved by correcting for the refractive effects of the human eye when the optical axes of the eye and camera are misaligned. We undertake this investigation using an anatomically-approximated, three-dimensional model of the human eye and ray-tracing. We generate synthetic iris imagery from different viewing angles using first a simple pattern of concentric rings on the iris for analysis, and then synthetic texture maps on the iris for experimentation. We estimate the distortion from the concentric-ring iris images and use the results to guide the sampling of textured iris images that are distorted by refraction. Using the well-known Gabor filter phase quantization approach, our model-based results indicate that the Hamming distances between iris signatures from different viewing angles can be significantly reduced by accounting for refraction. Over our experimental conditions comprising viewing angles from 0 to 60 degrees, we observe a median reduction in Hamming distance of 27.4% and a maximum reduction of 70.0% when we compensate for refraction. Maximum improvements are observed at viewing angles o/20deg-25deg.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131856873","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: 15

Real-Time License Plate Recognition on an Embedded DSP-Platform 基于嵌入式dsp平台的实时车牌识别

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383412

Clemens Arth, Florian Limberger, H. Bischof

{"title":"Real-Time License Plate Recognition on an Embedded DSP-Platform","authors":"Clemens Arth, Florian Limberger, H. Bischof","doi":"10.1109/CVPR.2007.383412","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383412","url":null,"abstract":"In this paper we present a full-featured license plate detection and recognition system. The system is implemented on an embedded DSP platform and processes a video stream in real-time. It consists of a detection and a character recognition module. The detector is based on the AdaBoost approach presented by Viola and Jones. Detected license plates are segmented into individual characters by using a region-based approach. Character classification is performed with support vector classification. In order to speed up the detection process on the embedded device, a Kalman tracker is integrated into the system. The search area of the detector is limited to locations where the next location of a license plate is predicted. Furthermore, classification results of subsequent frames are combined to improve the class accuracy. The major advantages of our system are its real-time capability and that it does not require any additional sensor input (e.g. from infrared sensors) except a video stream. We evaluate our system on a large number of vehicles and license plates using bad quality video and show that the low resolution can be partly compensated by combining classification results of subsequent frames.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133746885","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: 145

Object retrieval with large vocabularies and fast spatial matching 基于大词汇量和快速空间匹配的对象检索

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383172

James Philbin, Ondřej Chum, M. Isard, Josef Sivic, Andrew Zisserman

{"title":"Object retrieval with large vocabularies and fast spatial matching","authors":"James Philbin, Ondřej Chum, M. Isard, Josef Sivic, Andrew Zisserman","doi":"10.1109/CVPR.2007.383172","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383172","url":null,"abstract":"In this paper, we present a large-scale object retrieval system. The user supplies a query object by selecting a region of a query image, and the system returns a ranked list of images that contain the same object, retrieved from a large corpus. We demonstrate the scalability and performance of our system on a dataset of over 1 million images crawled from the photo-sharing site, Flickr [3], using Oxford landmarks as queries. Building an image-feature vocabulary is a major time and performance bottleneck, due to the size of our dataset. To address this problem we compare different scalable methods for building a vocabulary and introduce a novel quantization method based on randomized trees which we show outperforms the current state-of-the-art on an extensive ground-truth. Our experiments show that the quantization has a major effect on retrieval quality. To further improve query performance, we add an efficient spatial verification stage to re-rank the results returned from our bag-of-words model and show that this consistently improves search quality, though by less of a margin when the visual vocabulary is large. We view this work as a promising step towards much larger, \"web-scale \" image corpora.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115196452","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: 3111