CASIA OpenIR  > 毕业生  > 硕士学位论文
Alternative TitleThe Computational Model of Selective Visual Attention
Thesis Advisor胡占义
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword选择性注意 基于物体的选择性注意 计算模型 感知物体 预注意阶段 注视点转移 返回抑制 Selective Attention Object-based Selective Attention Computational Model Perceptual Object Preattentive Attention Attention Shif
Abstract选择性注意是普遍的心理学现象。在认知心理学和神经生物学中,它是很热门的研究 领域。随着主动视觉的兴起,选择性注意在计算机视觉中所起的作用逐渐被人们所重视。 本文的主要目的是建立一种有效的从底到顶的基于物体的选择性注意模型。 很多的心理学实验证明选择性注意的基本单元是“物体”,而目前文献中出现的选择性 注意的计算模型大多都是基于“空间”的。在这种背景下,我们建立了一个完全基于“感 知物体”的计算模型。这不仅更符合心理学研究的结果,且更有利于视觉的后期处理。 首先我们给出了一种合理的评价一个给定区域是否为“感知物体”的评价函数。该评 价函数值被称为区域的均匀度,它反映了区域内部在灰度和颜色上的均匀性以及区域内部 和外部邻域之间的反差。一个区域的均匀度越高则该区域就越有可能是感知物体且显著度 越高在此基础上,我们设计了一个次优算法在整幅图像中寻找均匀度最高的图像区域, 该区域将是第一个被选择注意的区域。我们的算法快速且在大多数情况下是成功的。最后 根据建模的目的,我们设计了一个分阶层的注视点转移机制,该机制将有利于一般情况下 的图像分析。 我们模型的优点在于:1)它完全基于感知物体;2)由于注视点转移机制是分阶层的, 所以我们的选择性注意在物理空间和特征空间上具有多尺度特性;3)我们的模型提供了多 种能结合高层信息指导的接口,这为建立完整的选择性注意机制打下了基础。 在我们提出的四条衡量选择性注意的标准下,我们的模型是有效的,能很好服务于通 用的图像分析系统。
Other AbstractSelective visual attention is a common psychological phenomenon. In cognitive psychological and neurophysiological domain, it is a very hot research area. With the development of active vision research, the important role of visual attention in computer vision area is coming to be recognized by more and more people. The main task of this paper is to setup an efficient computational model of bottom-up object-based selective attention. Numerous psychological experiments prove that "object" is the unit of visual attention but most of the computational models of visual attention in literature are space-based. Therefore, we proposed a novel computational model of selective attention completely from the perspective of"perceptual object". In this way, our model is not only more accordant with the psychological experiments but also more beneficial to high-level visual processes. First, we give out a reasonable evaluation function to evaluate the possibility of a given region to be a perceptual object. The evaluation value of this function is called "homogeneity value". The greater is the homogeneity value of the given region, the higher is the possibility of it to be a perceptual object and the more salient is it. Next, we design a sub-optimal algorithm to find the region that has the greatest homogeneity value all over the give image. This region will be the first focus of attention. This algorithm is fast and in most cases it is successful. Finally, we design a hierarchical mechanism of attention shift. This mechanism is beneficial to the general tasks of image analysis. The advantages of our model include: 1) It is based on "object"; 2) Due to the hierarchical character of our attention shift mechanism, our model is multi-scale in both of physical and feature space; 3) Our model provides multiple interfaces to combine high-level guidance. Measured by the four standards proposed by us, our model is efficient and can be easily used by general image-analysis systems.
Other Identifier698
Document Type学位论文
Recommended Citation
GB/T 7714
王璐. 选择性视觉注意的计算模型[D]. 中国科学院自动化研究所. 中国科学院研究生院,2003.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[王璐]'s Articles
Baidu academic
Similar articles in Baidu academic
[王璐]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[王璐]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.