2010 IEEE International Conference on Image Processing最新文献_第5页

A retrieval pattern-based inter-query learning approach for content-based image retrieval 一种基于检索模式的基于内容的图像检索互查询学习方法

2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5654156

Adam D. Gilbert, Ran Chang, Xiaojun Qi

引用次数: 11

An adaptive clustering and chrominance-based merging approach for image segmentation and abstraction 一种基于自适应聚类和色度的图像分割和提取方法

2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5651905

Lulu He, T. Pappas

引用次数: 6

Filterbank-based universal demosaicking 基于滤波器组的通用去马赛克

2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5649949

Jing Gu, P. Wolfe, Keigo Hirakawa

引用次数: 28

Improving histology images segmentation through spatial constraints and supervision 通过空间约束和监督改进组织学图像分割

2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5652083

Nicolas Hervé, A. Servais, E. Thervet, J. Olivo-Marin, V. Meas-Yedid

引用次数: 2

Visual saliency detection via rank-sparsity decomposition 基于秩稀疏度分解的视觉显著性检测

2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5652280

Junchi Yan, Jian Liu, Yin Li, Zhibin Niu, Yuncai Liu

引用次数: 38

SIFT in perception-based color space 基于感知的色彩空间SIFT

2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5651165

Yan Cui, A. Pagani, D. Stricker

引用次数: 13

Event tactic analysis in sports video using spatio-temporal pattern 基于时空模式的体育视频事件策略分析

2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5652340

Minh-Son Dao, Keita Masui, N. Babaguchi

{"title":"Event tactic analysis in sports video using spatio-temporal pattern","authors":"Minh-Son Dao, Keita Masui, N. Babaguchi","doi":"10.1109/ICIP.2010.5652340","DOIUrl":"https://doi.org/10.1109/ICIP.2010.5652340","url":null,"abstract":"Recently, event detection in sports videos has been gaining some remarkable results. Unfortunately, there is a lack of useful tools for users to explore and exploit these event clips on their own demands. In this paper, a novel method using spatio-temporal patterns to analyze event tactics in sports videos is introduced. The proposed method aims to understand tactics of events such as distributions and speeds of players, or attacking/defensive formations throughout a time when such events happen without tracking objects. The major contribution of the proposed method is to model event tactics by using sequence of symbols. Each symbol represents a distribution of players in a certain period of time. Therefore, a sequence of symbols intrinsically is concerned as spatio-temporal patterns. By using these patterns, an event tactic is detected, explained, and integrated into an event video to create a visualizing abstract. This visualizing abstract is very useful to help users understand an event tactic without watching whole clip. Moreover, users could query by an example or by a text to find all events sharing the same tactic. Thorough testing with over 100 goal clips in soccer domain demonstrate the superiority of the proposed method in terms of precision recall ratios.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114309091","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: 3

A learning framework for robust hashing of face images 人脸图像鲁棒哈希的学习框架

2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5651070

Kamil Senel, M. K. Mihçak, V. Monga

{"title":"A learning framework for robust hashing of face images","authors":"Kamil Senel, M. K. Mihçak, V. Monga","doi":"10.1109/ICIP.2010.5651070","DOIUrl":"https://doi.org/10.1109/ICIP.2010.5651070","url":null,"abstract":"Robust image hashing has been actively researched over the last decade with varied applications in image content authentication and identification under distortions. In the existing literature on robust image hashing, hash algorithms are ignorant of the class of images being hashed. There are however significant application domains such as that of face image hashing where apriori knowledge of the image class as well as permissible distortions can benefit hash algorithm design. In this paper, we present a two stage cascade of dimensionality reduction constructs for face image hashing. The first stage aims to project the face image to a space where geometric distortions manifest approximately as additive noise. For this purpose, we use the non-negative matrix approximations based hash vector developed by Monga et al. which is known to possess excellent geometric attack robustness. In the second stage, we employ oriented principal component analysis (OPCA) based on estimating signal as well as noise statistics in a learning phase and deriving a projection that mitigates the effect of noise. We obtain both experimentally based ROC curves as well as analytical ones via a detection theoretic analysis of the proposed framework. The ROC curves reveal clearly that incorporating such a learning phase greatly reduces error probabilities.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114628030","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: 2

Tensor error correction for corrupted values in visual data 可视化数据中损坏值的张量误差校正

2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5654055

Yin Li, Yue Zhou, Junchi Yan, Jie Yang, Xiangjian He

引用次数: 11

Non-linear optimization for robust estimation of vanishing points 消失点鲁棒估计的非线性优化

2010 IEEE International Conference on Image Processing Pub Date : 2010-12-03 DOI: 10.1109/ICIP.2010.5652381

M. Nieto, L. Salgado

{"title":"Non-linear optimization for robust estimation of vanishing points","authors":"M. Nieto, L. Salgado","doi":"10.1109/ICIP.2010.5652381","DOIUrl":"https://doi.org/10.1109/ICIP.2010.5652381","url":null,"abstract":"A new method for robust estimation of vanishing points is introduced in this paper. It is based on the MSAC (M-estimator Sample and Consensus) algorithm and on the definition of a new distance function between a vanishing point and a given orientation. Apart from the robustness, our method represents a flexible and efficient solution, since it allows to work with different type of image data, and its iterative nature makes better use of the available information to obtain more accurate estimates. The key issue of the work is the proposed distance function, that makes the error to be independent from the position of an hypothesized vanishing point, which allows to work with points at the infinity. Besides, the estimation process is guided by a non-linear optimization process that enhances the accuracy of the system. The robustness of our proposal, compared with other methods in the literature is shown with a set of tests carried out for both synthetic data and real images. The results show that our approach obtain excellent levels of accuracy and that is definitely robust against the presence of large amounts of outliers, outperforming other state of the art approaches.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124511504","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: 20