9-1-图像分割-概述

Berkeley segmentation database:
/Research/Projects/CS/vision/grouping/segbench/
Top-down segmentation
E. Borenstein and S. Ullman, “Class-specific, top-down segmentation,” ECCV 2002 A. Levin and Y. Weiss, “Learning to Combine Bottom-Up and TopDown Segmentation,” ECCV 2006.
图像识别与人工智能研究所,多谱信息处理国家重点实验室
图像分割
陶文兵
华中科技大学图像识别与人工智能研究所 多谱信息处理技术国家重点实验室
分割的目的和意义
图像分割是计算机视觉研究中的基础问题和难点之一
图像分割就是把图像分成各具特性的区域并提取出感兴趣目标
小 语义符号 操 作 对 象 数 据 大 原始图像 图像理解 目标识别 图像分析 特征提取 图像分割 低 高 语 义 抽 象 程 度
Top-down segmentation
Normalized cuts
Top-down segmentation
E. Borenstein and S. Ullman, “Class-specific, top-down segmentation,” ECCV 2002 A. Levin and Y. Weiss, “Learning to Combine Bottom-Up and TopDown Segmentation,” ECCV 2006.
Types of segmentations
Oversegmentation
Undersegmentation
Multiple Segmentations
Segmentation as a result
Rother et al. 2004
Segmentation for efficiency
14
Three basic theory in Image Segmentation
Statistics
Variational Graph
Two basic Model in Image Segmentation
Statistics formulation
Variational Model
Eb (C )
Remarks:
L (C )
0
g (C (s))ds
g 1/ (1 I )
The function g is an edge indicator function that vanishes at object. The shorter the curve, the larger the gradient of the curve, the less the energy.
Chan-Vese(CV) model
CV Model-piecewise constant MS model (Chan and Vese,
2001)
T. Chan and L. Vese, ―Active contours without edges,‖ IEEE Trans. Image Process., vol. 10, no. 2, pp. 266–277, Feb. 2001. Cited times: 4514
Major processes for segmentation
Bottom-up: group tokens with similar features Top-down: group tokens that likely belong to the same object
[Levin and Weiss 2006]
MAP-MRF: Maximum a posteriori-Markov random field
E (L ) E data (L ) E smooth (L )
D (L ) V
pP p p ( p,q )N
p ,q
(L p , Lq )
Dp is a data penalty function, Vp,q is an interactive function
F (c1 , c2 , C ) 1 (u0 ( x, y) c1 ) 2 (u0 ( x, y) c2 ) ds
CV 2 2 1 2
Remarks:
Assume that u is a piecewise constant function . For such case, the second term disappears from the MS functions. Two phase problem, c1 is the average of region 1, c2 is the average of region 2.
Data penalties indicate individual label-preferences of pixels based on observed intensities and prespecified likelihood function. Interaction function encourage spatial coherence by penalizing discontinuities between neighboring pixels.
Geodesic active contours model
Geodesic active contours model (GAC) (Caselles et al., 1997)
V. Caselles, R. Kimmel, and G. Sapiro, ―Geodesic active contours,‖ Int. J. Comput. Vis., vol. 22, pp. 61–79, 1997. (3709)
The goals of segmentation
• Separate image into coherent “objects”

“Bottom-up” or “top-down” process? Supervised or unsupervised?
image
human segmentation
Snake model
Snake model (Kass et al., 1988)
Kass, M., A. Witkin, and D. Terzopoulos: 1988, ―Snakes: Active contour models‖. International Journal of Computer Vision,vol. 1, pp. 321–331 (13622)
Region-based Active Contour
Optimization Method
1、模拟退火(simulated annealing) 2、水平集算法(Level Set)
3、图割算法(Graph cuts)
4、期望最大化算法(Expectation-Maximization —EM) 5、置信传播(Belief propagation) 4、对偶算法 (primal dual)
目标描述
原始像素
图像处理
图像工程的三层模型
图像分割的难点和挑战性

对一般图像中的大量视觉模型进行建模的复杂性 图像理解本身的内在模糊性 当没有一个明确的任务来指导注意机制
2
image segmentation …
Goal: Break up the image into meaningful or perceptually similar regions
Edge-based Active Contour
Mumford-Shah functional
Mumford and Shah functional (Mumford and Shah, 1989)
Mumford, D. and J. Shah: 1989, ‗Optimal approximations by piecewise smooth functions and associated variational problems‘. Communications on Pure and Applied Mathematics 42, 577–685. (3122)
F
MS
(u, C ) (u0 u ) dx
2
\C
u dx C
2
Remarks:
The minimization of Mumford-Shah functional results in an optimal contour C that segments the given image u0 into several regions. Image u is an optimal piecewise smooth approximation of the given image u0 Image u is smooth within each of the connected components in the image domain separated by the contour C.
[Felzenszwalb and Huttenlocher 2004]
[Hoiem et al. 2005, Mori 2005]
[Shi and Malik 2001]
Segments as primitives for recognition