1 / 17

What is Image Segmentation? Image Segmentation Methods Thresholding Boundary-based

EE4328, Section 005 Introduction to Digital Image Processing Image Segmentation Zhou Wang Dept. of Electrical Engineering The Univ. of Texas at Arlington Fall 2006. Concepts and Approaches. What is Image Segmentation? Image Segmentation Methods Thresholding Boundary-based

vernon-reese
Download Presentation

What is Image Segmentation? Image Segmentation Methods Thresholding Boundary-based

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. EE4328, Section 005 Introduction to Digital Image ProcessingImage SegmentationZhou WangDept. of Electrical EngineeringThe Univ. of Texas at ArlingtonFall 2006

  2. Concepts and Approaches • What is Image Segmentation? • Image Segmentation Methods • Thresholding • Boundary-based • Region-based: region growing, splitting and merging Partition an image into regions, each associated with an object but what defines an object? From Prof. Xin Li

  3. Thresholding Method thresholding From Prof. Xin Li histogram multiple thresholds single threshold From [Gonzalez & Woods]

  4. Thresholding Method • Global Thresholding: When does It Work? From [Gonzalez & Woods]

  5. Thresholding Method • Global Thresholding: When does It NOT Work? • A meaningful global threshold may not exist • Image-dependent global thresholding From [Gonzalez & Woods]

  6. Thresholding Method true object boundary Thresholding T = 4.5 Thresholding T = 5.5

  7. Thresholding Method • Solution • Spatially adaptive thresholding • Localized processing Split

  8. Thresholding Method spatially adaptive threshold selection Thresholding T = 4 Thresholding T = 7 Thresholding T = 4 Thresholding T = 7

  9. Thresholding Method merge local segmentation results merge merge merge merge

  10. Boundary-Based Method boundary detection classification and labeling edge detection image segmentation From Prof. Xin Li

  11. Boundary-Based Method • Advanced Method: Active Contour (Snake) Model • Iteratively update contour (region boundary) • Partial differential equation (PDE) based optimization From Prof. Xin Li

  12. Region-Based Method: Region Growing • Region Growing • Start from a seed, and let it grow (include similar neighborhood) Key: similarity measure From [Gonzalez & Woods]

  13. Region-Based Method: Split and Merge • Split and Merge • Iteratively split (non-similar region) and merge (similar regions) • Example: quadtree approach From [Gonzalez & Woods]

  14. Region-Based Method: Split and Merge • Example: Quadtree Split and Merge Procedure Iteration 1 split merge 4 regions 4 regions (nothing to merge) original image Split Step split every non-uniform region to 4 MergeStep merge all uniform adjacent regions

  15. Region-Based Method: Split and Merge • Example: Quadtree Split and Merge Procedure Iteration 2 split merge 13 regions 4 regions from Iteration 1 Split Step split every non-uniform region to 4 MergeStep merge all uniform adjacent regions

  16. Region-Based Method: Split and Merge • Example: Quadtree Split and Merge Procedure Iteration 3 split merge 10 regions 2 regions from Iteration 2 Split Step split every non-uniform region to 4 MergeStep merge all uniform adjacent regions final segmentation result

  17. Hard Problem: Textures Similarity measure makes the difference From Prof. Xin Li

More Related