4 March 2014 Smart imaging for power-efficient extraction of Viola-Jones local descriptors
Author Affiliations +
Abstract
In computer vision, local descriptors permit to summarize relevant visual cues through feature vectors. These vectors constitute inputs for trained classifiers which in turn enable different high-level vision tasks. While local descriptors certainly alleviate the computation load of subsequent processing stages by preventing them from handling raw images, they still have to deal with individual pixels. Feature vector extraction can thus become a major limitation for conventional embedded vision hardware. In this paper, we present a power-efficient sensing processing array conceived to provide the computation of integral images at different scales. These images are intermediate representations that speed up feature extraction. In particular, the mixed-signal array operation is tailored for extraction of Haar-like features. These features feed the cascade of classifiers at the core of the Viola-Jones framework. The processing lattice has been designed for the standard UMC 0.18μm 1P6M CMOS process. In addition to integral image computation, the array can be reprogrammed to deliver other early vision tasks: concurrent rectangular area sum, block-wise HDR imaging, Gaussian pyramids and image pre-warping for subsequent reduced kernel filtering.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
J. Fernández-Berni, J. Fernández-Berni, R. A. Carmona-Galán, R. A. Carmona-Galán, R. del Río, R. del Río, Juan A. Leñero-Bardallo, Juan A. Leñero-Bardallo, M. Suárez-Cambre, M. Suárez-Cambre, Á. Rodríguez-Vázquez, Á. Rodríguez-Vázquez, } "Smart imaging for power-efficient extraction of Viola-Jones local descriptors", Proc. SPIE 9022, Image Sensors and Imaging Systems 2014, 902209 (4 March 2014); doi: 10.1117/12.2042384; https://doi.org/10.1117/12.2042384
PROCEEDINGS
9 PAGES


SHARE
RELATED CONTENT


Back to Top