An efficient feature descriptor based on synthetic basis functions and uniqueness matching strategy

Feature matching is an important step for many computer vision applications. This presentation introduces the development of a new feature descriptor, called SYnthetic BAsis (SYBA), for feature point description and matching. SYBA is built on the basis of the compressed sensing theory that uses synthetic basis functions to encode or reconstruct a signal. It is a compact and efficient binary descriptor that performs a number of similarity tests between a feature image region and a selected number of synthetic basis images and uses their similarity test results as the feature descriptors. SYBA is compared with four well-known binary descriptors using three benchmarking datasets as well as a newly created dataset that was designed specifically for a more thorough statistical T-test. SYBA is less computationally complex and produces better feature matching results than other binary descriptors. It is hardware-friendly and suitable for embedded vision applications. SYBA has been successfully applied to real-time vision applications such as soccer ball location in broadcast video for event annotation, unmanned aerial vehicle ground target tracking, motion classification for advanced driver assistance systems (ADAS), and visual odometry drift reduction. and the director of the Robotic Vision Laboratory. He served in the machine vision industry as system designer, researcher, and technical and project manager for eleven years before joining BYU in 2001. Companies and positions he held include, staff scientist at Innovision His last employment prior to joining BYU faculty was with Robotic Vision System Inc. (RVSI) where he served as the Director of Vision Technology and was responsible of designing the state-of-the-art high-speed semiconductor wafer inspection systems. He founded CS Tech in 1995 and Smart Vision Works, LLC in 2006. He is a co-founder and president of Smart Vision Works International that was founded in 2012 for the design and manufacturing of custom-designed machine vision systems. His current research work focuses on object recognition, high-performance real-time vision computing, and robotic and machine vision applications.