To the Graduate Council: I am submitting herewith a thesis written by ...

Chapter 2: Literature Review 8Shape is the geometric information invariant to a particular class of transformationssuch as affine, translation, rotation and scaling and is considered to be the “words” ofthe visual language. Shape analysis is an important aspect in image understanding.Since so many objects in our world are strongly determined by geometric properties,the applications of shape analysis extend over a broad spectrum of science andtechnology. Indeed, when properly and carefully applied shape analysis provides richpotential for applications in diverse areas, spanning computer vision, graphics,material sciences, biology and even neuroscience.2.2 Shape Analysis on 2D ImagesShape description looks for effective and perceptually important shape features basedon either shape boundary information or interior content. By perceptually similarshapes we are referring to shapes that are rotated, translated, scaled and are affinetransformed. Many shape representation techniques have been developed in the pastand shape analysis still remains as an interesting field of research. A few suchrepresentation techniques are the shape signatures, shape histograms, moments,curvature, shape context and shape matrix. We would like to direct the reader to[Zhang and Lu, 2004] for a recent and comprehensive survey on 2D shaperepresentation for various applications.2.2.1 Classification of MethodsShape representation techniques are generally classified into two classes based onwhether shape features are extracted from the contour only or from the whole region.Zhang and Lu [Zhang and Lu, 2004] subdivide each of these classes further intostructural and global approaches based on the primitives used to describe the shape.They discuss methods that operate on the space domain and transform domain toextract shape information and classify shape description methods as shown in Figure2.1.Contour-based approaches are more popular in computer vision literature. Suchmethods assume that human beings discriminate shapes mainly by their featurecontours. The contour-based approach is limited by noise and by data that do not havesufficient information (occlusions) in the boundary contour. Region-based methodsare considered to be more robust and are dependable for accurate retrieval as theyattempt to extract shape information from the entire region and not just its boundary.