Bhikadiya, Jatin V., authorDraper, Bruce A., advisorBeveridge, Ross J., advisorBates, Daniel J., committee member2015-08-272015-08-272015http://hdl.handle.net/10217/167024Object recognition is one of the most challenging tasks in computer vision. A common approach in recognizing an object begins by detecting local features in image using a feature detector and describing detected features in terms of feature vectors using a feature descriptor. Many local feature detectors and feature descriptors have been proposed in literature. This work evaluates performance of two successful feature detectors and five feature descriptors on three datasets with unique characteristics. Based on the information content in a given dataset we find general trends on the performance of local features. Our findings will guild computer vision practitioners selecting between alternative local feature detector and local feature descriptor to design highly accurate recognition systems.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.feature descriptorsobject recognitionbag of featuresperformance evaluationfeature detectorsText