2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops最新文献_第3页

Color calibration of multi-projector displays through automatic optimization of hardware settings 通过自动优化硬件设置来校准多投影机显示器的颜色

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204322

R. M. Steele, Mao Ye, Ruigang Yang

{"title":"Color calibration of multi-projector displays through automatic optimization of hardware settings","authors":"R. M. Steele, Mao Ye, Ruigang Yang","doi":"10.1109/CVPRW.2009.5204322","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204322","url":null,"abstract":"We describe a system that performs automatic, camera-based photometric projector calibration by adjusting hardware settings (e.g. brightness, contrast, etc.). The approach has two basic advantages over software-correction methods. First, there is no software interface imposed on graphical programs: all imagery displayed on the projector benefits from the calibration immediately, without render-time overhead or code changes. Secondly, the approach benefits from the fact that projector hardware settings typically are capable of expanding or shifting color gamuts (e.g. trading off maximum brightness versus darkness of black levels), something that software methods, which only shrink gamuts, cannot do. In practice this means that hardware settings can possibly match colors between projectors while maintaining a larger overall color gamut (e.g. better contrast) than software-only correction can. The prototype system is fully automatic. The space of hardware settings is explored by using a computer-controlled universal remote to navigate each projector's menu system. An off-the-shelf camera observes each projector's response curves. A cost function is computed for the curves based on their similarity to each other, as well as intrinsic characteristics, including color balance, black level, gamma, and dynamic range. An approximate optimum is found using a heuristic combinatoric search. Results show significant qualitative improvements in the absolute colors, as well as the color consistency, of the display.","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":"126388389","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 to segment using machine-learned penalized logistic models 学习使用机器学习惩罚逻辑模型进行分割

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204343

Yong Yue, H. Tagare

引用次数: 6

Biometric data hiding: A 3 factor authentication approach to verify identity with a single image using steganography, encryption and matching 生物特征数据隐藏:一种3因素身份验证方法，使用隐写，加密和匹配来验证单个图像的身份

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204308

Neha Agrawal, M. Savvides

{"title":"Biometric data hiding: A 3 factor authentication approach to verify identity with a single image using steganography, encryption and matching","authors":"Neha Agrawal, M. Savvides","doi":"10.1109/CVPRW.2009.5204308","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204308","url":null,"abstract":"Digital Steganography exploits the use of host data to hide a piece of information in such a way that it is imperceptible to a human observer. Its main objectives are imperceptibility, robustness and high payload. DCT Domain message embedding in Spread Spectrum Steganography describes a novel method of using redundancy in DCT coefficients. We improved upon the method of DCT embedding by using the sign of the DCT coefficients to get better accuracy of retrieved data and more robustness under channel attacks like channel noise and JPEG compression artifacts while maintaining the visual imperceptibility of cover image, and even extending the method further to obtain higher payloads. We also apply this method for secure biometric data hiding, transmission and recovery. We hide iris code templates and fingerprints in the host image which can be any arbitrary image, such as face biometric modality and transmit the so formed imperceptible Stego-Image securely and robustly for authentication, and yet obtain perfect reconstruction and classification of iris codes and retrieval of fingerprints at the receiving end without any knowledge of the cover image i.e. a blind method of steganography, which in this case is used to hide biometric template in another biometric modality.","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":"122405120","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: 50

Square Loss based regularized LDA for face recognition using image sets 基于平方损失的正则化LDA图像集人脸识别

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204307

Yanlin Geng, Caifeng Shan, Pengwei Hao

引用次数: 5

An alignment based similarity measure for hand detection in cluttered sign language video 一种基于对齐的相似度方法用于杂乱手语视频中的手部检测

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204266

Ashwin Thangali, S. Sclaroff

{"title":"An alignment based similarity measure for hand detection in cluttered sign language video","authors":"Ashwin Thangali, S. Sclaroff","doi":"10.1109/CVPRW.2009.5204266","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204266","url":null,"abstract":"Locating hands in sign language video is challenging due to a number of factors. Hand appearance varies widely across signers due to anthropometric variations and varying levels of signer proficiency. Video can be captured under varying illumination, camera resolutions, and levels of scene clutter, e.g., high-res video captured in a studio vs. low-res video gathered by a Web cam in a user's home. Moreover, the signers' clothing varies, e.g., skin-toned clothing vs. contrasting clothing, short-sleeved vs. long-sleeved shirts, etc. In this work, the hand detection problem is addressed in an appearance matching framework. The histogram of oriented gradient (HOG) based matching score function is reformulated to allow non-rigid alignment between pairs of images to account for hand shape variation. The resulting alignment score is used within a support vector machine hand/not-hand classifier for hand detection. The new matching score function yields improved performance (in ROC area and hand detection rate) over the vocabulary guided pyramid match kernel (VGPMK) and the traditional, rigid HOG distance on American Sign Language video gestured by expert signers. The proposed match score function is computationally less expensive (for training and testing), has fewer parameters and is less sensitive to parameter settings than VGPMK. The proposed detector works well on test sequences from an inexpert signer in a non-studio setting with cluttered background.","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":"126974158","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: 10

Dual domain auxiliary particle filter with integrated target signature update 集成目标特征更新的双域辅助粒子滤波

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204143

C. M. Johnston, N.A. Mould, J. Havlicek, Guoliang Fan

{"title":"Dual domain auxiliary particle filter with integrated target signature update","authors":"C. M. Johnston, N.A. Mould, J. Havlicek, Guoliang Fan","doi":"10.1109/CVPRW.2009.5204143","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204143","url":null,"abstract":"For the first time, we formulate an auxiliary particle filter jointly in the pixel domain and modulation domain for tracking infrared targets. This dual domain approach provides an information rich image representation comprising the pixel domain frames acquired directly from an imaging infrared sensor as well as 18 amplitude modulation functions obtained through a multicomponent AM-FM image analysis. The new dual domain auxiliary particle filter successfully tracks all of the difficult targets in the well-known AMCOM closure sequences in terms of both centroid location and target magnification. In addition, we incorporate the template update procedure into the particle filter formulation to extend previously studied dual domain track consistency checking mechanism far beyond the normalized cross correlation (NCC) trackers of the past by explicitly quantifying the differences in target signature evolution between the modulation and pixel domains. Experimental results indicate that the dual domain auxiliary particle filter with integrated target signature update provides a significant performance advantage relative to several recent competing algorithms.","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":"121749060","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

Fast features for time constrained object detection 快速特征的时间约束的目标检测

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204293

G. Overett, L. Petersson

{"title":"Fast features for time constrained object detection","authors":"G. Overett, L. Petersson","doi":"10.1109/CVPRW.2009.5204293","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204293","url":null,"abstract":"This paper concerns itself with the development and design of fast features suitable for time constrained object detection. Primarily we consider three aspects of feature design; the form of the precomputed datatype (e.g. the integral image), the form of the features themselves (i.e. the measurements made of an image), and the models/weak- learners used to construct weak classifiers (class, non-class statistics). The paper is laid out as a guide to feature designers, demonstrating how appropriate choices in combining the above three characteristics can prevent bottlenecks in the run-time evaluation of classifiers. This leads to reductions in the computational time of the features themselves and, by providing more discriminant features, reductions in the time taken to reach specific classification error rates. Results are compared using variants of the well known Haar-like feature types, Rectangular Histogram of Oriented Gradient (RHOG) features and a special set of Histogram of Oriented Gradient features which are highly optimized for speed. Experimental results suggest the adoption of this set of features for time-critical applications. Time-constrained comparisons are presented using pedestrian and road sign detection problems. Comparison results are presented on time-error plots, which are a replacement of the traditional ROC performance curves.","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":"134033434","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

Multi-modal laughter recognition in video conversations 视频对话中的多模态笑声识别

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204268

Sergio Escalera, Eloi Puertas, P. Radeva, O. Pujol

引用次数: 17

Regularization of diffusion tensor field using coupled robust anisotropic diffusion filters 利用耦合鲁棒各向异性扩散滤波器的扩散张量场正则化

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204342

Songyuan Tang, Yong Fan, Hongtu Zhu, P. Yap, Wei Gao, Weili Lin, D. Shen

引用次数: 3

Using closed captions to train activity recognizers that improve video retrieval 使用封闭字幕训练活动识别器，提高视频检索

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204202

S. Gupta, R. Mooney

引用次数: 16