1. Region and Shape Extraction
Huiqiong Chen
Faculty of Computer Science
Dalhousie University

2. Aims Goal of this research Motivation Applications
Extract meaningful regions from image and estimate their shapes without intensive computation.
Motivation
Taking advantage of inherent structure information carried by each GET feature, the perceptual structure of region shape can be obtained easily as well as region interior attributes.
Applications
Image/Video representation
Region-based Image/Video retrieval
Video surveillance
Medical Imaging
License recognition and tracking

3. Key Idea
All meaningful regions can be represented by two types of perceptual GET-based closures in Perceptual region hierarchy.
An image can be transformed into GET space on the fly represented by a GET graph, which presents perceptual organization of GET associations.
Region detection can be achieved by perceptually grouping closures in GET graph.

4. Perceptual Region Hierarchy
Regions can be classified into two types:
Object outline: describe an whole object
Basic region: describe basic inner component of object

5. System Architecture

6. Perceptual Closure Detection

7. Example
Original image Extracted GET features

8. Example (Cont’d)
Contours in image Basic regions

9. Advantages
Provides a real-time region detection system in which region shape structure can be extracted at the same time.
It achieves high accuracy of detected regions without intensive computation.
Suitable for segmenting regions with arbitrary shapes.
Both object contours and their components can be detected, based upon which all meaningful regions can be conducted hierarchically.

10. Detected Samples Original image Extracted GET features
GET-based region contour Filled regions

11. Detected Samples (Cont’d)