2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops最新文献_第2页

Data handling displays 数据处理显示

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204320

Maxim Lazarov, H. Pirsiavash, B. Sajadi, Uddipan Mukherjee, A. Majumder

{"title":"Data handling displays","authors":"Maxim Lazarov, H. Pirsiavash, B. Sajadi, Uddipan Mukherjee, A. Majumder","doi":"10.1109/CVPRW.2009.5204320","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204320","url":null,"abstract":"Imagine a world in which people can use their hand-held mobile devices to project and transfer content. Everyone can join the collaboration by simply bringing their mobile devices close to each other. People can grab data from each others' devices with simple hand gestures. Now imagine a large display created by tiling multiple displays where multiple users can interact with a large dynamically changing data set in a collocated, collaborative setting and the displays will take care of the data transfer and handling functions in a way that is transparent to the users. In this paper we present a novel data-handling display which works as not only a display device but also as an interaction and data transfer module. We propose simple gesture based solutions to transfer information between these data-handling modules. We achieve high scalability by presenting a fully distributed architecture in which each device is responsible for its own data and also communicates and collaborates with other devices. We also show the usefulness of our work in visualizing large datasets and at the same time allowing multiple users to interact with the data.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","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":"128076854","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: 1

Mining discriminative adjectives and prepositions for natural scene recognition 自然场景识别中区别性形容词和介词的挖掘

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204222

Bangpeng Yao, Juan Carlos Niebles, Li Fei-Fei

{"title":"Mining discriminative adjectives and prepositions for natural scene recognition","authors":"Bangpeng Yao, Juan Carlos Niebles, Li Fei-Fei","doi":"10.1109/CVPRW.2009.5204222","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204222","url":null,"abstract":"This paper presents a method that considers not only patch appearances, but also patch relationships in the form of adjectives and prepositions for natural scene recognition. Most of the existing scene categorization approaches only use patch appearances or co-occurrence of patch appearances to determine the scene categories, but the relationships among patches remain ignored. Those relationships are, however, critical for recognition and understanding. For example, a `beach' scene can be characterized by a `sky' region above `sand', and a `water' region between `sky' and `sand'. We believe that exploiting such relations between image regions can improve scene recognition. In our approach, each image is represented as a spatial pyramid, from which we obtain a collection of patch appearances with spatial layout information. We apply a feature mining approach to get discriminative patch combinations. The mined patch combinations can be interpreted as adjectives or prepositions, which are used for scene understanding and recognition. Experimental results on a fifteen class scene dataset show that our approach achieves competitive state-of-the-art recognition accuracy, while providing a rich description of the scene classes in terms of the mined adjectives and prepositions.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","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":"134517324","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

Automatically detecting action units from faces of pain: Comparing shape and appearance features 从疼痛的面部自动检测动作单位:比较形状和外观特征

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204279

P. Lucey, J. Cohn, S. Lucey, S. Sridharan, K. Prkachin

{"title":"Automatically detecting action units from faces of pain: Comparing shape and appearance features","authors":"P. Lucey, J. Cohn, S. Lucey, S. Sridharan, K. Prkachin","doi":"10.1109/CVPRW.2009.5204279","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204279","url":null,"abstract":"Recent psychological research suggests that facial movements are a reliable measure of pain. Automatic detection of facial movements associated with pain would contribute to patient care but is technically challenging. Facial movements may be subtle and accompanied by abrupt changes in head orientation. Active appearance models (AAM) have proven robust to naturally occurring facial behavior, yet AAM-based efforts to automatically detect action units (AUs) are few. Using image data from patients with rotator-cuff injuries, we describe an AAM-based automatic system that decouples shape and appearance to detect AUs on a frame-by-frame basis. Most current approaches to AU detection use only appearance features. We explored the relative efficacy of shape and appearance for AU detection. Consistent with the experience of human observers, we found specific relationships between action units and types of facial features. Several AU (e.g. AU4, 12, and 43) were more discriminable by shape than by appearance, whilst the opposite pattern was found for others (e.g. AU6, 7 and 10). AU-specific feature sets may yield optimal results.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","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":"130690799","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

Alignment of 3D point clouds to overhead images 将3D点云对准头顶图像

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204180

R. S. Kaminsky, Noah Snavely, S. Seitz, R. Szeliski

引用次数: 102

Non-rigid registration between histological and MR images of the prostate: A joint segmentation and registration framework 前列腺组织学和MR图像之间的非刚性配准:一个联合分割和配准框架

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204347

Yangming Ou, D. Shen, M. Feldman, J. Tomaszeweski, C. Davatzikos

{"title":"Non-rigid registration between histological and MR images of the prostate: A joint segmentation and registration framework","authors":"Yangming Ou, D. Shen, M. Feldman, J. Tomaszeweski, C. Davatzikos","doi":"10.1109/CVPRW.2009.5204347","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204347","url":null,"abstract":"This paper presents a 3D non-rigid registration algorithm between histological and MR images of the prostate with cancer. To compensate for the loss of 3D integrity in the histology sectioning process, series of 2D histological slices are first reconstructed into a 3D histological volume. After that, the 3D histology-MRI registration is obtained by maximizing a) landmark similarity and b) cancer region overlap between the two images. The former aims to capture distortions at prostate boundary and internal blob-like structures; and the latter aims to capture distortions specifically at cancer regions. In particular, landmark similarities, the former, is maximized by an annealing process, where correspondences between the automatically-detected boundary and internal landmarks are iteratively established in a fuzzy-to-deterministic fashion. Cancer region overlap, the latter, is maximized in a joint cancer segmentation and registration framework, where the two interleaved problems - segmentation and registration - inform each other in an iterative fashion. Registration accuracy is established by comparing against human-rater-defined landmarks and by comparing with other methods. The ultimate goal of this registration is to warp the histologically-defined cancer ground truth into MRI, for more thoroughly understanding MRI signal characteristics of the prostate cancerous tissue, which will promote the MRI-based prostate cancer diagnosis in the future studies.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","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":"123922609","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: 43

HALF-SIFT: High-Accurate Localized Features for SIFT 半SIFT:高精度的SIFT局部特征

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204283

Kai Cordes, Oliver Müller, B. Rosenhahn, J. Ostermann

引用次数: 12

Automatic recognition of fingerspelled words in British Sign Language 自动识别手指拼写单词在英国手语

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204291

Stephan Liwicki, M. Everingham

引用次数: 132

Impact of involuntary subject movement on 3D face scans 受试者非自愿运动对3D面部扫描的影响

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204324

Chris Boehnen, P. Flynn

引用次数: 4

Scenes vs. objects: A comparative study of two approaches to context based recognition 场景与对象:两种基于上下文的识别方法的比较研究

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204220

Andrew Rabinovich, Serge J. Belongie

引用次数: 13

Audio-visual speech synchronization detection using a bimodal linear prediction model 使用双峰线性预测模型的视听语音同步检测

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204303

Kshitiz Kumar, Jirí Navrátil, E. Marcheret, V. Libal, G. Ramaswamy, G. Potamianos

引用次数: 12