Limited Resource Feature Detection, Description, and Matching

Limited-Resource Feature Detection, Description, and Matching Spencer G Fowers Department of Electrical and Computer Engineering Doctor of Philosophy The aims of this research work are to develop a feature detection, description, and matching system for low-resource applications. This work was motivated by the need for a vision sensor to assist the flight of a quad-rotor UAV. This application presented a realworld challenge of autonomous drift stabilization using vision sensors. The initial solution implemented a basic feature detector and matching system on an FPGA. The research then pursued ways to improve the vision system. Research began with color feature detection, and the Color Difference of Gaussians feature detector was developed. CDoG provides better results than gray scale DoG and does not require any additional processing than gray scale if implemented in a parallel architecture. The CDoG Scale-Invariant Feature Transform modification was developed which provided color feature detection and description to the gray scale SIFT descriptor. To demonstrate the benefits of color information, the CDSIFT algorithm was applied to a real application: library book inventory. While color provides added benefit to the CDSIFT descriptor, CDSIFT descriptors are still computationally intractable for a low-resource hardware implementation. Because of these shortcomings, this research focused on developing a new feature descriptor. The BAsis Sparse-coding Inspired Similarity (BASIS) descriptor was developed with low-resource systems in mind. BASIS utilizes sparse coding to provide a generic description of feature characterstics. The BASIS descriptor provided improved accuracy over SIFT, and similar accuracy to SURF on the task of aerial UAV frame-to-frame feature matching. However, basis dictionaries are non-orthogonal and can contain redundant information. In addition to a feature descriptor, an FPGA-based feature correlation (or matching) system needed to be developed. TreeBASIS was developed to answer this need and address the redundancy issues of BASIS. TreeBASIS utilizes a vocabulary tree to drastically reduce descriptor computation time and descriptor size. TreeBASIS also obtains a higher level of accuracy than SIFT, SURF, and BASIS on the UAV aerial imagery task. Both BASIS and TreeBASIS were implemented in VHDL and are well suited for low-resource FPGA applications. TreeBASIS provides a complete feature detection, description, and correlation system-on-a-chip for low-resource FPGA vision systems.