Successfully reported this slideshow.
Upcoming SlideShare
×

# ppt on region segmentation by AJAY KUMAR SINGH (NITK)

538 views

Published on

THIS IS SIMPLE INTRODUCTION OF RIGION BASED IMAGE SEGMENTATION

• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

• Be the first to like this

### ppt on region segmentation by AJAY KUMAR SINGH (NITK)

1. 1. REGION-BASED IMAGE SEGMENTATION By Ajay Kumar Singh
2. 2. Overview  Definition  Need of segmentation  Classification of methods  Region based segmentation
3. 3. Definition  Segmentation refers to the process of partitioning a image into multiple regions.  Regions:- A group of connected pixels with similar properties.  Regions are used to interpret images. A region may correspond to a particular object, or different parts of an object.
4. 4.  In most cases, segmentation should provide a set of regions having the following properties     Connectivity and compactness Regularity of boundaries Homogeneity in terms of color or texture Differentiation from neighbor regions
5. 5. Need of segmentation The goal of segmentation is to simplify the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries in images. For correct interpretation, image must be partitioned into regions that correspond to objects or parts of an object.
6. 6. Basic Formulation  Let R represent the entire image region. We want to partition R into n sub regions, R1, R2, . . ., Rn, such that: (a) Summation of Ri =R (b) Ri is a connected region for i=1, 2, . . , n (c) Ri intersection Rj =φ for all i and j , I≠ j (d) P(Ri) = TRUE for i=1, 2, . . . n (e) P( Ri summation Rj)= False , i ≠ j
7. 7. Basic Formulation      (a) segmentation must be complete – all pixels must belong to a region (b) pixels in a region must be connected (c) Regions must be disjoint (d) states that pixels in a region must all share the same property – The logic predicate P(Ri) over a region must return TRUE for each point in that region (e) indicates that regions are different in the sense of the predicate P.
8. 8. Segmentation Effect Region Segmented Image
9. 9. Approaches to segmentation  Region based approaches   group together pixels with similar properties combining proximity and similarity
10. 10. Classification  Region based approaches are based on pixel properties such as    Homogeneity Spatial proximity The most used methods are     Thresholding Clustering Region growing Split and merge
11. 11. Pixel Aggregation (Region Growing)  The basic idea is to grow from a seed pixel  At a labeled pixel, check each of its neighbors  If its attributes are similar to those of the already labeled pixel,label the neighbor accordingly  Repeat until there is no more pixel that can be labeled  For example, let  The attribute of a pixel is its pixel value  The similarity is defined as the difference between adjacent pixel values  If the difference is smaller than a threshold, they are assigned to the same region, otherwise not
12. 12. Region Growing : Algorithm a) Chose or determined a group of seed pixel which can correctly represent the required region;  b) Fixed the formula which can contain the adjacent pixels in the growth;  c) Made rules or conditions to stop the growth process 
13. 13. Region Split and Merge    After segmentation the regions may need to be refined or reformed. Split operation adds missing boundaries by splitting regions that contain part of different objects. Merge operation eliminates false boundaries and spurious regions by merging adjacent regions that belong to the same object. Split-and-merge in a hierarchical data structure
14. 14. Algorithm: Region Splitting  Form initial region in the image  For each region in an image, recursively perform:   Compute the variance in the gray values for the region If the variance is above a threshold, split the region along the appropriate boundary
15. 15.   If some property of a region is not constant Regular decomposition Methods: divide the region into a fixed number of equal-sized regions.
16. 16. Algorithm: Region Merging (1) Form initial regions in the image using thresholding ( or a similar approach) followed by component labeling. (2) Prepare a region adjacency graph (RAG) for the image. (3) For each region in an image, perform the following steps: (a) Consider its adjacent region and test to see if they are similar. (b) For regions that are similar, merge them and modify the RAG. (4) Repeat step 3 until no regions are merged.
17. 17. Applications      In image compression Object recognition Computer graphics Medical Imaging MPEG-4 video object (VO) segmentation
18. 18. References [1] Rafael C. Gonzalez and Richard E. woods “DIGITAL IMAGE PROCESSING,ˮ 2011.p. 762-770. [2] Jun Tang, “A Color Image Segmentation algorithm Based on Region Growing,ˮ China School of Electronic Engineering 2010. [3] Chaobing Huang, Quan Liu, Xiaopeng Li “Color Image Segmentation by Seeded Regionˮ China, School of information engineering, 2010. [4] Tiancan Mei, Chen Zheng, Sidong Zhong, “Hierarchical Region Based Markov Random Field for Image Segmentation”,Wuhan,China,2011 [5] en.wikipedia.org/wiki/Region_growing
19. 19. Thanks to All
20. 20. Any Question??