Statistical Models of Shape

1. Front Matter

   Pages 1-9

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2. Introduction

   • Rhodri Davies, Carole Twining, Chris Taylor

   Pages 1-8

3. Statistical Models of Shape and Appearance

   • Rhodri Davies, Carole Twining, Chris Taylor

   Pages 1-39

4. Establishing Correspondence

   • Rhodri Davies, Carole Twining, Chris Taylor

   Pages 1-17

5. Objective Functions

   • Rhodri Davies, Carole Twining, Chris Taylor

   Pages 1-28

6. Re-parameterisation of Open and Closed Curves

   • Rhodri Davies, Carole Twining, Chris Taylor

   Pages 1-22

7. Parameterisation and Re-parameterisation of Surfaces

   • Rhodri Davies, Carole Twining, Chris Taylor

   Pages 1-30

8. Optimisation

   • Rhodri Davies, Carole Twining, Chris Taylor

   Pages 1-30

9. Non-parametric Regularization

   • Rhodri Davies, Carole Twining, Chris Taylor

   Pages 1-54

10. Evaluation of Statistical Models

    • Rhodri Davies, Carole Twining, Chris Taylor

    Pages 1-27

11. Back Matter

    Pages 1-44

    PDF

The goal of image interpretation is to convert raw image data into me- ingful information. Images are often interpreted manually. In medicine, for example, a radiologist looks at a medical image, interprets it, and tra- lates the data into a clinically useful form. Manual image interpretation is, however, a time-consuming, error-prone, and subjective process that often requires specialist knowledge. Automated methods that promise fast and - jective image interpretation have therefore stirred up much interest and have become a signi?cant area of research activity. Early work on automated interpretation used low-level operations such as edge detection and region growing to label objects in images. These can p- ducereasonableresultsonsimpleimages,butthepresenceofnoise,occlusion, andstructuralcomplexity oftenleadstoerroneouslabelling. Furthermore,- belling an object is often only the ?rst step of the interpretation process. In order to perform higher-level analysis, a priori information must be incor- rated into the interpretation process. A convenient way of achieving this is to use a ?exible model to encode information such as the expected size, shape, appearance, and position of objects in an image. The use of ?exible models was popularized by the active contour model, or ‘snake’ [98]. A snake deforms so as to match image evidence (e.g., edges) whilst ensuring that it satis?es structural constraints. However, a snake lacks speci?city as it has little knowledge of the domain, limiting its value in image interpretation.

• Computer Vision
• Computer visison
• Correspondence
• Medical image analysis
• Minimum description length
• Pattern recognition
• Statistical shape models
• image analysis

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