Nearest Neighborhood Grayscale Operator for Hardware-Efficient Microscale Texture Extraction

Abstract

First-stage feature computation and data rate reduction play a crucial role in an efficient visual information processing system. Hardware-based first stages usually win out where power consumption, dynamic range, and speed are the issue, but have severe limitations with regard to flexibility. In this paper, the local orientation coding (LOC), a nearest neighborhood grayscale operator, is investigated and enhanced for hardware implementation. The features produced by this operator are easy and fast to compute, compress the salient information contained in an image, and lend themselves naturally to various medium-to-high-level postprocessing methods such as texture segmentation, image decomposition, and feature tracking. An image sensor architecture based on the LOC has been elaborated, that combines high dynamic range (HDR) image aquisition, feature computation, and inherent pixel-level ADC in the pixel cells. The mixed-signal design allows for simple readout as digital memory.