Modeling Stereopsis via Markov Random Field

	Modeling Stereopsis via Markov Random Field
	Ming, Yansheng; Hu, Zhanyi
发表期刊	NEURAL COMPUTATION
	2010-08-01
卷号	22 期号:8 页码:2161-2191
文章类型	Article
摘要	Markov random field (MRF) and belief propagation have given birth to stereo vision algorithms with top performance. This article explores their biological plausibility. First, an MRF model guided by physiological and psychophysical facts was designed. Typically an MRF-based stereo vision algorithm employs a likelihood function that reflects the local similarity of two regions and a potential function that models the continuity constraint. In our model, the likelihood function is constructed on the basis of the disparity energy model because complex cells are considered as front-end disparity encoders in the visual pathway. Our likelihood function is also relevant to several psychological findings. The potential function in our model is constrained by the psychological finding that the strength of the cooperative interaction minimizing relative disparity decreases as the separation between stimuli increases. Our model is tested on three kinds of stereo images. In simulations on images with repetitive patterns, we demonstrate that our model could account for the human depth percepts that were previously explained by the second-order mechanism. In simulations on random dot stereograms and natural scene images, we demonstrate that false matches introduced by the disparity energy model can be reliably removed using our model. A comparison with the coarse-to-fine model shows that our model is able to compute the absolute disparity of small objects with larger relative disparity. We also relate our model to several physiological findings. The hypothesized neurons of the model are selective for absolute disparity and have facilitative extra receptive field. There are plenty of such neurons in the visual cortex. In conclusion, we think that stereopsis can be implemented by neural networks resembling MRF.
WOS标题词	Science & Technology ; Technology ; Life Sciences & Biomedicine
关键词[WOS]	BINOCULAR DEPTH-PERCEPTION ; VISUAL-CORTEX ; BELIEF PROPAGATION ; DISPARITY ; NEURONS ; COMPUTATION ; MECHANISM ; MACAQUE ; V1 ; DISCRIMINATION
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:000279109600009
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/2960
专题	多模态人工智能系统全国重点实验室_机器人视觉
作者单位	Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Ming, Yansheng,Hu, Zhanyi. Modeling Stereopsis via Markov Random Field[J]. NEURAL COMPUTATION,2010,22(8):2161-2191.
APA	Ming, Yansheng,&Hu, Zhanyi.(2010).Modeling Stereopsis via Markov Random Field.NEURAL COMPUTATION,22(8),2161-2191.
MLA	Ming, Yansheng,et al."Modeling Stereopsis via Markov Random Field".NEURAL COMPUTATION 22.8(2010):2161-2191.