2007 IEEE Conference on Computer Vision and Pattern Recognition最新文献_第8页

3D Probabilistic Feature Point Model for Object Detection and Recognition 用于目标检测与识别的三维概率特征点模型

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383284

S. Romdhani, T. Vetter

{"title":"3D Probabilistic Feature Point Model for Object Detection and Recognition","authors":"S. Romdhani, T. Vetter","doi":"10.1109/CVPR.2007.383284","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383284","url":null,"abstract":"This paper presents a novel statistical shape model that can be used to detect and localise feature points of a class of objects in images. The shape model is inspired from the 3D morphable model (3DMM) and has the property to be viewpoint invariant. This shape model is used to estimate the probability of the position of a feature point given the position of reference feature points, accounting for the uncertainty of the position of the reference points and of the intrinsic variability of the class of objects. The viewpoint invariant detection algorithm maximises a foreground/background likelihood ratio of the relative position of the feature points, their appearance, scale, orientation and occlusion state. Computational efficiency is obtained by using the Bellman principle and an early rejection rule based on 3D to 2D projection constraints. Evaluations of the detection algorithm on the CMU-P1E face images and on a large set of non-face images show high levels of accuracy (zero false alarms for more than 90% detection rate). As well as locating feature points, the detection algorithm also estimates the pose of the object and a few shape parameters. It is shown that it can be used to initialise a 3DMM fitting algorithm and thus enables a fully automatic viewpoint and lighting invariant image analysis solution.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"27 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":"130841268","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: 24

Iterative MAP and ML Estimations for Image Segmentation 图像分割的迭代MAP和ML估计

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383007

Shifeng Chen, Liangliang Cao, Jianzhuang Liu, Xiaoou Tang

引用次数: 16

Thermal-Visible Video Fusion for Moving Target Tracking and Pedestrian Classification 热视视频融合运动目标跟踪与行人分类

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383444

A. Leykin, Yang Ran, R. Hammoud

引用次数: 69

Probabilistic visibility for multi-view stereo 多视点立体的概率可见性

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383193

Carlos Hernández, George Vogiatzis, R. Cipolla

引用次数: 117

Algorithms for Batch Matrix Factorization with Application to Structure-from-Motion 批矩阵分解算法及其在运动构造中的应用

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383062

J. Tardif, A. Bartoli, Martin Trudeau, Nicolas Guilbert, S. Roy

引用次数: 44

Recognizing Human Activities from Silhouettes: Motion Subspace and Factorial Discriminative Graphical Model 从轮廓识别人类活动:运动子空间和析因判别图形模型

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383298

Liang Wang, D. Suter

{"title":"Recognizing Human Activities from Silhouettes: Motion Subspace and Factorial Discriminative Graphical Model","authors":"Liang Wang, D. Suter","doi":"10.1109/CVPR.2007.383298","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383298","url":null,"abstract":"We describe a probabilistic framework for recognizing human activities in monocular video based on simple silhouette observations in this paper. The methodology combines kernel principal component analysis (KPCA) based feature extraction and factorial conditional random field (FCRF) based motion modeling. Silhouette data is represented more compactly by nonlinear dimensionality reduction that explores the underlying structure of the articulated action space and preserves explicit temporal orders in projection trajectories of motions. FCRF models temporal sequences in multiple interacting ways, thus increasing joint accuracy by information sharing, with the ideal advantages of discriminative models over generative ones (e.g., relaxing independence assumption between observations and the ability to effectively incorporate both overlapping features and long-range dependencies). The experimental results on two recent datasets have shown that the proposed framework can not only accurately recognize human activities with temporal, intra-and inter-person variations, but also is considerably robust to noise and other factors such as partial occlusion and irregularities in motion styles.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"29 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":"123322736","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: 230

On the Direct Estimation of the Fundamental Matrix 关于基本矩阵的直接估计

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383064

Yaser Sheikh, Asaad Hakeem, M. Shah

引用次数: 17

Using Group Prior to Identify People in Consumer Images 使用群体优先识别消费者形象中的人物

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383492

Andrew C. Gallagher, Tsuhan Chen

{"title":"Using Group Prior to Identify People in Consumer Images","authors":"Andrew C. Gallagher, Tsuhan Chen","doi":"10.1109/CVPR.2007.383492","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383492","url":null,"abstract":"While face recognition techniques have rapidly advanced in the last few years, most of the work is in the domain of security applications. For consumer imaging applications, person recognition is an important tool that is useful for searching and retrieving images from a personal image collection. It has been shown that when recognizing a single person in an image, a maximum likelihood classifier requires the prior probability for each candidate individual. In this paper, we extend this idea and describe the benefits of using a group prior for identifying people in consumer images with multiple people. The group prior describes the probability of a group of individuals appearing together in an image. In our application, we have a subset of ambiguously labeled images for a consumer image collection, where we seek to identify all of the people in the collection. We describe a simple algorithm for resolving the ambiguous labels. We show that despite errors in resolving ambiguous labels, useful classifiers can be trained with the resolved labels. Recognition performance is further improved with a group prior learned from the ambiguous labels. In summary, by modeling the relationships between the people with the group prior, we improve classification performance.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"76 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":"126212676","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: 58

Sensor and Data Systems, Audio-Assisted Cameras and Acoustic Doppler Sensors 传感器和数据系统，声控相机和声学多普勒传感器

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383533

K. Kalgaonkar, P. Smaragdis, B. Raj

引用次数: 11

Real-time Automatic Deceit Detection from Involuntary Facial Expressions 基于非自愿面部表情的实时自动欺骗检测

2007 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2007-06-17 DOI: 10.1109/CVPR.2007.383383

Zhi Zhang, Vartika Singh, T. E. Slowe, S. Tulyakov, V. Govindaraju

{"title":"Real-time Automatic Deceit Detection from Involuntary Facial Expressions","authors":"Zhi Zhang, Vartika Singh, T. E. Slowe, S. Tulyakov, V. Govindaraju","doi":"10.1109/CVPR.2007.383383","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383383","url":null,"abstract":"Being the most broadly used tool for deceit measurement, the polygraph is a limited method as it suffers from human operator subjectivity and the fact that target subjects are aware of the measurement, which invites the opportunity to alter their behavior or plan counter-measures in advance. The approach presented in this paper attempts to circumvent these problems by unobtrusively and automatically measuring several prior identified deceit indicators (DIs) based upon involuntary, so-called reliable facial expressions through computer vision analysis of image sequences in real time. Reliable expressions are expressions said by the psychology community to be impossible for a significant percentage of the population to convincingly simulate, without feeling a true inner felt emotion. The strategy is to detect the difference between those expressions which arise from internal emotion, implying verity, and those expressions which are simulated, implying deceit. First, a group of facial action units (AUs) related to the reliable expressions are detected based on distance and texture based features. The DIs then can be measured and finally a decision of deceit or verity will be made accordingly. The performance of this proposed approach is evaluated by its real time implementation for deceit detection.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"4 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":"126090416","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: 38