Holistic Shape-Based Object Recognition Using Bottom-Up Image Structures

HOLISTIC SHAPE-BASED OBJECT RECOGNITION USING BOTTOM-UP IMAGE STRUCTURES Praveen Srinivasan Jianbo Shi, Associate Professor of Computer and Information Science Object recognition performance that rivals human ability is one of the primary goals of computer vision research. While recognition may take many forms, key tasks include detection, estimating object pose, and segmenting the object from the background. This thesis explores the use of holistic shape matching for recognition using bottom-up image structures such as image segments and contours for all of these tasks. Holistic shape matching utilizes global information about object shape for matching, rather than local image features which often contain too little information to match reliably to the object model. By examining different tasks related to object recognition, we demonstrate the value of holistic shape matching in a broad range of problems, including perceptual grouping, human pose estimation, and object recognition. First, we introduce a method for perceptual grouping of contours in an image into larger groups that uses holistic shape matching to estimate the motion of image contours to a second, related image and group them according to similarties in motion. Holistic shape matching provides scoring for different motion hypotheses, and the final grouping is achieved using a min-cut graph cut to infer the cluster assignment for each contour. Secondly, we describe a method for human pose estimation using image segments that incrementally merges segments into hypotheses for increasingly larger regions of the human body. These hypotheses are verified by matching against a set of shape exemplars using a shape matching method that is articulation-invariant and incorporates holistic shape information. Lastly, we present a two-step method for automatically learning an object detector