Modeling Objects Using Interest Points, Edges, and Regions

This dissertation explores the application of different low level image features for the task of object category recognition and segmentation. In particular, it proposes novel fast and efficient models to represent object categories using interest points, edges, and regions. For interest points, a part based generate object template is proposed, which models object categories as a joint occurrence of appearance and location of texture patches around interest points. The model makes novel independence assumptions and proposes variational learning techniques which make learning and inference faster. The model is shown to have good detection and localization performance on standard datasets. This template model is enhanced for automatic object segmentation by incorporating image edge information. A model edge response which encodes the position of object edges with respect to the template is learned from image edges. Object segmentation is obtained by integrating the edge and interest point cues in a random field model. Towards object representation using regions, this thesis first presents a multiscale image segmentation algorithm which represents the image as a hierarchical tree of regions, i.e., a segmentation tree. The algorithm models edges between regions as rapidly varying ramp discontinuities, for which parameters are estimated and used to obtain the final segmentation. The thesis then proposes a model that represents object categories in terms of individual regions and their arrangement in the above segmentation tree. The performance of the model is tested with respect to its localization and segmentation accuracy on standard datasets.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Arora, Himanshu

Contributors dc:contributor

• Ahuja, Narendra

• Computer Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI3301100

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81054