Building a model for a three-dimensional object class in a low dimensional space for object detection.

摘要

Modeling 3D object classes requires accounting for intra-class variations in an object's appearance under different viewpoints, scale and illumination conditions. Therefore, detecting instances of 3D object classes in the presence of background clutter is difficult. This thesis presents a novel approach to model generic 3D object classes and an algorithm to detect multiple instances of an object class in an arbitrary image.;A novel aspect of the proposed approach is that all object parts and the background class are represented in the same lower dimensional space. Thus the detection algorithm can explicitly label features in an image as belonging to an object part or background. Additionally, spatial relationships between object parts are established and employed during the detection stage to localize instances of the object class in a novel image. It is shown that detecting objects based on measuring spatial consistency between object parts is superior to a bag-of-words model that ignores all spatial information.;Since generic object classes can be characterized by shape or appearance, this thesis has formulated a method to combine these attributes to enhance the object model. Class-specific local contour features are detected in an arbitrary image to form a shape map that is then employed in two novel ways to augment the appearance-based technique.;Experiments on publicly available datasets have shown that the proposed method can successfully detect instances of generic object classes such as faces, cars, bicycles, airplanes, shoes, and toaster.;Motivated by the parts-based representation, the proposed approach divides the object into different spatial regions. Each spatial region is associated with an object part whose appearance is represented by a dense set of overlapping SIFT features. The distribution of these features is then described in a lower dimensional space using supervised Locally Linear Embedding. Each object part is essentially represented by a spatial cluster in the embedding space. For viewpoint invariance, the view-sphere comprising the 3D object is divided into a discrete number of view segments. Several spatial clusters represent the object in each view segment. This thesis provides a framework for representing these clusters in either single or multiple embedding spaces.