Shape-based Representations and Boosting for Visual Object Class Detection Models and methods for representaion and detection in single and multiple views

Abstract: Detection of generic visual object classes (i.e. cars, dogs, mugs or people) in images is a task that humans are able to solve with remarkable ease. Unfortunately this has proven a very challenging task for computer vision. Thereason is that different instances of the same class may look very different, i.e. there is a high intra-class variation. There are several causes for intra-class variation; for example (1) the imaging conditions (e.g. lighting and exposure) may change, (2) different objects of the same class typically differ in shape and appearance, (3) the position of the object relative to the camera (i.e. the viewpoint) may change and (4) some objects are articulate and may change pose. In addition the background class, i.e. everything but the target object class, is very large. It is the combination of very high intra-class variation with a large background class that makes generic object class detection difficult.This thesis addresses this challenge within the AdaBoost framework. AdaBoost constructs an ensemble of weak classifiers to solve a given classification task and allows great flexibility in the design of these weak classifiers. This thesis proposes several types of weak classifiers that specifically target some of the causes of high intra-class variation. A multi-local classifier is proposed to capture global shape properties for object classes that lack discriminative local features, projectable classifiers are proposed to handle detection from multiple viewpoints and finally gated classifiers are proposed as a generic way to handle high intra-class variation in combination with a large background class.All proposed weak classifiers are evaluated on standard datasets to allow performance comparison to other related methods.