图像分析的生成层次模型

S. Geman
{"title":"图像分析的生成层次模型","authors":"S. Geman","doi":"10.1109/CVPRW.2009.5204335","DOIUrl":null,"url":null,"abstract":"A probabilistic grammar for the groupings and labeling of parts and objects, when taken together with pose and part-dependent appearance models, constitutes a generative scene model and a Bayesian framework for image analysis. To the extent that the generative model generates features, as opposed to pixel intensities, the \"inverse\" or \"posterior distribution\" on interpretations given images is based on incomplete information; feature vectors are generally insufficient to recover the original intensities. I will argue for fully generative scene models, meaning models that in principle generate actual digital pictures. I will outline an approach to the construction of fully generative models through an extension of context-sensitive grammars and a re-formulation of the popular template models for image fragments. Mostly I will focus on the problem of constructing pixel-level appearance models. I will propose an approach based on image-fragment templates, as introduced by Ullman and others. However, rather than using a correlation between a template and a given image patch as an extracted feature.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"98 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generative hierarchical models for image analysis\",\"authors\":\"S. Geman\",\"doi\":\"10.1109/CVPRW.2009.5204335\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A probabilistic grammar for the groupings and labeling of parts and objects, when taken together with pose and part-dependent appearance models, constitutes a generative scene model and a Bayesian framework for image analysis. To the extent that the generative model generates features, as opposed to pixel intensities, the \\\"inverse\\\" or \\\"posterior distribution\\\" on interpretations given images is based on incomplete information; feature vectors are generally insufficient to recover the original intensities. I will argue for fully generative scene models, meaning models that in principle generate actual digital pictures. I will outline an approach to the construction of fully generative models through an extension of context-sensitive grammars and a re-formulation of the popular template models for image fragments. Mostly I will focus on the problem of constructing pixel-level appearance models. I will propose an approach based on image-fragment templates, as introduced by Ullman and others. However, rather than using a correlation between a template and a given image patch as an extracted feature.\",\"PeriodicalId\":431981,\"journal\":{\"name\":\"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops\",\"volume\":\"98 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CVPRW.2009.5204335\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPRW.2009.5204335","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

用于零件和物体的分组和标记的概率语法,当与姿势和部分相关的外观模型一起使用时,构成了生成场景模型和用于图像分析的贝叶斯框架。在某种程度上,生成模型生成特征,而不是像素强度,对给定图像的解释的“逆”或“后验分布”是基于不完整的信息;特征向量通常不足以恢复原始强度。我将支持完全生成场景模型,即原则上生成实际数字图像的模型。我将概述一种通过扩展上下文敏感语法和重新制定流行的图像片段模板模型来构建完全生成模型的方法。我将主要关注构建像素级外观模型的问题。我将提出一种基于图像片段模板的方法,正如Ullman等人所介绍的那样。然而,而不是使用模板和给定图像补丁之间的相关性作为提取特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generative hierarchical models for image analysis
A probabilistic grammar for the groupings and labeling of parts and objects, when taken together with pose and part-dependent appearance models, constitutes a generative scene model and a Bayesian framework for image analysis. To the extent that the generative model generates features, as opposed to pixel intensities, the "inverse" or "posterior distribution" on interpretations given images is based on incomplete information; feature vectors are generally insufficient to recover the original intensities. I will argue for fully generative scene models, meaning models that in principle generate actual digital pictures. I will outline an approach to the construction of fully generative models through an extension of context-sensitive grammars and a re-formulation of the popular template models for image fragments. Mostly I will focus on the problem of constructing pixel-level appearance models. I will propose an approach based on image-fragment templates, as introduced by Ullman and others. However, rather than using a correlation between a template and a given image patch as an extracted feature.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信