Image Feature Extraction Acceleration

Image feature extraction is instrumental for most of the best-performing algorithms in computer vision. However, it is also expensive in terms of computational and memory resources for embedded systems due to the need of dealing with individual pixels at the earliest processing levels. In this regard, conventional system architectures do not take advantage of potential exploitation of parallelism and distributed memory from the very beginning of the processing chain. Raw pixel values provided by the front-end image sensor are squeezed into a high-speed interface with the rest of system components. Only then, after deserializing this massive dataflow, parallelism, if any, is exploited. This chapter introduces a rather different approach from an architectural point of view. We present two Application-Specific Integrated Circuits (ASICs) where the 2-D array of photo-sensitive devices featured by regular imagers is combined with distributed memory supporting concurrent processing. Custom circuitry is added per pixel in order to accelerate image feature extraction right at the focal plane. Specifically, the proposed sensing-processing chips aim at the acceleration of two flagships algorithms within the computer vision community: J. Fernández-Berni (B) · R. Carmona-Galán · R. del Río · Á. Rodríguez-Vázquez Institute of Microelectronics of Seville (CSIC Universidad de Sevilla), C/ Américo Vespucio s/n, 41092 Seville, Spain e-mail: [email protected] R. Carmona-Galán e-mail: [email protected] R. del Río e-mail: [email protected] Á. Rodríguez-Vázquez e-mail: [email protected] V.M. Brea ·M. Suárez · D. Cabello Centro de Investigación en Tecnoloxías da Información (CITIUS), University of Santiago de Compostela, Santiago de Compostela, Spain e-mail: [email protected] D. Cabello e-mail: [email protected] © Springer International Publishing Switzerland 2016 A.I. Awad and M. Hassaballah (eds.), Image Feature Detectors and Descriptors, Studies in Computational Intelligence 630, DOI 10.1007/978-3-319-28854-3_5 109 110 J. Fernández-Berni et al. the Viola-Jones face detection algorithm and the Scale Invariant Feature Transform (SIFT). Experimental results prove the feasibility and benefits of this architectural solution.