第五讲目标分割 周文晖 计算机学院
Hangzhou Dianzi University 杭州电子科技大学 School of Computer Science and Technology 计算机学院 周文晖 第五讲 目标分割 周文晖 计算机学院
课本：目标分割的概念和目的 •概念：将图像划分成若干具有特征一 致性且互不重叠的图像区域的过程。 Aim:to partition an image into a collection of set of pixels >Meaningful regions(coherent objects) >Linear structures (line,curve,... >Shapes(circles,ellipses,... Haug3 hou Dianzi乙niversity抗州电子科技大学 School of Computer Science and Technology计算l学院周文晖
Hangzhou Dianzi University 杭州电子科技大学 School of Computer Science and Technology 计算机学院 周文晖 课本：目标分割的概念和目的 •概念：将图像划分成若干具有特征一 致性且互不重叠的图像区域的过程。 Aim: to partition an image into a collection of set of pixels Meaningful regions (coherent objects) Linear structures (line, curve, ...) Shapes (circles, ellipses, ...)
Other variants:What is segmentation? >Segmentation =partitioning Grouping clustering Carve dense data set into (disjoint)regions Gather sets of items according to some model Divide image based on pixel similarity If items are dense,then essentially the same problem as Divide spatiotemporal volume based on image left.(e.g.,clustering pixels) similarity (shot detection) If items are sparse,then problem has a slightly different Figure/ground separation(background flavor: subtraction) Collect tokens that lie on a line (robust line fitting) Regions can be overlapping (layers) Collect pixels that share the same fundamental matrix (independent 3D rigid motion) Group 3D surface elements that belong to the same surface S.Birchfield,Clemson Univ.,ECE 847,http://www.cos.clomson.odu/-stb/oco847 Hag3 hou Dianzi Universit内抗州电子科技大学 School of Computer Science and Tecfnology计算机学院周文晖
Hangzhou Dianzi University 杭州电子科技大学 School of Computer Science and Technology 计算机学院 周文晖 Other variants：What is segmentation? Segmentation = partitioning Carve dense data set into (disjoint) regions Divide image based on pixel similarity Divide spatiotemporal volume based on image similarity (shot detection) Figure / ground separation (background subtraction) Regions can be overlapping (layers) S. Birchfield, Clemson Univ., ECE 847, http://www.ces.clemson.edu/~stb/ece847 Grouping = clustering Gather sets of items according to some model If items are dense, then essentially the same problem as left. (e.g., clustering pixels) If items are sparse, then problem has a slightly different flavor: • Collect tokens that lie on a line (robust line fitting) • Collect pixels that share the same fundamental matrix (independent 3D rigid motion) • Group 3D surface elements that belong to the same surface
Other variants:What is segmentation? .Segmentation divides an image into groups of pixels .Pixels are grouped because they share some local property(gray level,color,texture,motion,etc.) 0 21 boundaries labels pseudocolors mean colors (different ways of displaying the output) algorithm used:Pedro F.Felzenszwalb and Daniel P.Huttenlocher, Efficient Graph-Based Image Segmentation,IJCV.59(2),2004 Hangzhou Dianzi University抗州电子科技大学 School of Computer Science and Technology计算机学院周文晖 S.Birchfield,Clemson Univ.,ECE 847,http:l/www.ces.clomson.odul-sth/oce847
Hangzhou Dianzi University 杭州电子科技大学 School of Computer Science and Technology 计算机学院 周文晖 Other variants：What is segmentation? •Segmentation divides an image into groups of pixels •Pixels are grouped because they share some local property (gray level, color, texture, motion, etc.) boundaries labels pseudocolors mean colors (different ways of displaying the output) 0 7 3 11 21 3 algorithm used: Pedro F. Felzenszwalb and Daniel P. Huttenlocher, Efficient Graph-Based Image Segmentation, IJCV, 59(2), 2004 S. Birchfield, Clemson Univ., ECE 847, http://www.ces.clemson.edu/~stb/ece847
图像分割的意义 ·区域对于图像理解和识别非常重要，往往表征场景中的目标，或部分目标。 ·一幅图像可以包含多个目标，每个目标包含多个区域，每个区域对应目标的不同部分。 inference Low level features Higher level inference Iaug3 hou Dianzi University杭州电子科技大学 School of Computer Science and Technology计算机学院周文晖
Hangzhou Dianzi University 杭州电子科技大学 School of Computer Science and Technology 计算机学院 周文晖 图像分割的意义 •区域对于图像理解和识别非常重要，往往表征场景中的目标，或部分目标。 •一幅图像可以包含多个目标，每个目标包含多个区域，每个区域对应目标的不同部分。 Low level features Higher level inference inference