2009 IEEE Conference on Computer Vision and Pattern Recognition最新文献

Abnormal crowd behavior detection using social force model 基于社会力模型的人群异常行为检测

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206641

Ramin Mehran, Alexis Oyama, M. Shah

引用次数: 1650

Extraction of tubular structures over an orientation domain 取向域上管状结构的提取

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206782

M. Pechaud, R. Keriven, G. Peyré

引用次数: 69

Learning color and locality cues for moving object detection and segmentation 学习运动物体检测和分割的颜色和位置线索

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206857

Feng Liu, Michael Gleicher

{"title":"Learning color and locality cues for moving object detection and segmentation","authors":"Feng Liu, Michael Gleicher","doi":"10.1109/CVPR.2009.5206857","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206857","url":null,"abstract":"This paper presents an algorithm for automatically detecting and segmenting a moving object from a monocular video. Detecting and segmenting a moving object from a video with limited object motion is challenging. Since existing automatic algorithms rely on motion to detect the moving object, they cannot work well when the object motion is sparse and insufficient. In this paper, we present an unsupervised algorithm to learn object color and locality cues from the sparse motion information. We first detect key frames with reliable motion cues and then estimate moving sub-objects based on these motion cues using a Markov Random Field (MRF) framework. From these sub-objects, we learn an appearance model as a color Gaussian Mixture Model. To avoid the false classification of background pixels with similar color to the moving objects, the locations of these sub-objects are propagated to neighboring frames as locality cues. Finally, robust moving object segmentation is achieved by combining these learned color and locality cues with motion cues in a MRF framework. Experiments on videos with a variety of object and camera motion demonstrate the effectiveness of this algorithm.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117234308","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

Contextual classification with functional Max-Margin Markov Networks 基于功能最大边际马尔可夫网络的上下文分类

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206590

Daniel Munoz, Andrew Bagnell

引用次数: 336

Classifier grids for robust adaptive object detection 鲁棒自适应目标检测的分类网格

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206616

P. Roth, Sabine Sternig, H. Grabner, H. Bischof

{"title":"Classifier grids for robust adaptive object detection","authors":"P. Roth, Sabine Sternig, H. Grabner, H. Bischof","doi":"10.1109/CVPR.2009.5206616","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206616","url":null,"abstract":"In this paper we present an adaptive but robust object detector for static cameras by introducing classifier grids. Instead of using a sliding window for object detection we propose to train a separate classifier for each image location, obtaining a very specific object detector with a low false alarm rate. For each classifier corresponding to a grid element we estimate two generative representations in parallel, one describing the object's class and one describing the background. These are combined in order to obtain a discriminative model. To enable to adapt to changing environments these classifiers are learned on-line (i.e., boosting). Continuously learning (24 hours a day, 7 days a week) requires a stable system. In our method this is ensured by a fixed object representation while updating only the representation of the background. We demonstrate the stability in a long-term experiment by running the system for a whole week, which shows a stable performance over time. In addition, we compare the proposed approach to state-of-the-art methods in the field of person and car detection. In both cases we obtain competitive results.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124904814","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: 82

Robust guidewire tracking in fluoroscopy 透视中稳健的导丝跟踪

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206692

Peng Wang, Terrence Chen, Ying Zhu, Wei Zhang, S. Zhou, D. Comaniciu

{"title":"Robust guidewire tracking in fluoroscopy","authors":"Peng Wang, Terrence Chen, Ying Zhu, Wei Zhang, S. Zhou, D. Comaniciu","doi":"10.1109/CVPR.2009.5206692","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206692","url":null,"abstract":"A guidewire is a medical device inserted into vessels during image guided interventions for balloon inflation. During interventions, the guidewire undergoes non-rigid deformation due to patients' breathing and cardiac motions, and such 3D motions are complicated when being projected onto the 2D fluoroscopy. Furthermore, in fluoroscopy there exist severe image artifacts and other wire-like structures. All these make robust guidewire tracking challenging. To address these challenges, this paper presents a probabilistic framework for robust guidewire tracking. We first introduce a semantic guidewire model that contains three parts, including a catheter tip, a guidewire tip and a guidewire body. Measurements of different parts are integrated into a Bayesian framework as measurements of a whole guidewire for robust guidewire tracking. Moreover, for each part, two types of measurements, one from learning-based detectors and the other from online appearance models, are applied and combined. A hierarchical and multi-resolution tracking scheme is then developed based on kernel-based measurement smoothing to track guidewires effectively and efficiently in a coarse-to-fine manner. The presented framework has been validated on a test set of 47 sequences, and achieves a mean tracking error of less than 2 pixels. This demonstrates the great potential of our method for clinical applications.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125082165","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: 73

Learning shape prior models for object matching 学习物体匹配的形状先验模型

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206568

Tingting Jiang, F. Jurie, C. Schmid

引用次数: 51

Fast concurrent object localization and recognition 快速并发目标定位和识别

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206805

Tom Yeh, John J. Lee, Trevor Darrell

{"title":"Fast concurrent object localization and recognition","authors":"Tom Yeh, John J. Lee, Trevor Darrell","doi":"10.1109/CVPR.2009.5206805","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206805","url":null,"abstract":"Object localization and recognition are important problems in computer vision. However, in many applications, exhaustive search over all object models and image locations is computationally prohibitive. While several methods have been proposed to make either recognition or localization more efficient, few have dealt with both tasks simultaneously. This paper proposes an efficient method for concurrent object localization and recognition based on a data-dependent multi-class branch-and-bound formalism. Existing bag-of-features recognition techniques which can be expressed as weighted combinations of feature counts can be readily adapted to our method. We present experimental results that demonstrate the merit of our algorithm in terms of recognition accuracy, localization accuracy, and speed, compared to baseline approaches including exhaustive search, implicit-shape model (ISM), and efficient sub-window search (ESS). Moreover, we develop two extensions to consider non-rectangular bounding regions-composite boxes and polygons-and demonstrate their ability to achieve higher recognition scores compared to traditional rectangular bounding boxes.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125493676","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: 77

Flow mosaicking: Real-time pedestrian counting without scene-specific learning 流动马赛克:实时行人计数，不需要特定场景的学习

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206648

Yang Cong, Haifeng Gong, Song-Chun Zhu, Yandong Tang

引用次数: 90

Learning general optical flow subspaces for egomotion estimation and detection of motion anomalies 学习用于自运动估计和运动异常检测的一般光流子空间

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206538

Richard Roberts, C. Potthast, F. Dellaert

{"title":"Learning general optical flow subspaces for egomotion estimation and detection of motion anomalies","authors":"Richard Roberts, C. Potthast, F. Dellaert","doi":"10.1109/CVPR.2009.5206538","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206538","url":null,"abstract":"This paper deals with estimation of dense optical flow and ego-motion in a generalized imaging system by exploiting probabilistic linear subspace constraints on the flow. We deal with the extended motion of the imaging system through an environment that we assume to have some degree of statistical regularity. For example, in autonomous ground vehicles the structure of the environment around the vehicle is far from arbitrary, and the depth at each pixel is often approximately constant. The subspace constraints hold not only for perspective cameras, but in fact for a very general class of imaging systems, including catadioptric and multiple-view systems. Using minimal assumptions about the imaging system, we learn a probabilistic subspace constraint that captures the statistical regularity of the scene geometry relative to an imaging system. We propose an extension to probabilistic PCA (Tipping and Bishop, 1999) as a way to robustly learn this subspace from recorded imagery, and demonstrate its use in conjunction with a sparse optical flow algorithm. To deal with the sparseness of the input flow, we use a generative model to estimate the subspace using only the observed flow measurements. Additionally, to identify and cope with image regions that violate subspace constraints, such as moving objects, objects that violate the depth regularity, or gross flow estimation errors, we employ a per-pixel Gaussian mixture outlier process. We demonstrate results of finding the optical flow subspaces and employing them to estimate dense flow and to recover camera motion for a variety of imaging systems in several different environments.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115199983","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: 63