Fixed Pattern Noise Analysis for Feature Descriptors in CMOS APS Images

This paper provides a comparative performance evaluation of local features for images from CMOS APS sensors affected by fixed pattern noise for different combinations of common detectors and descriptors. Although numerous studies report comparisons of local features designed for ordinary visual images, their performance on images with fixed pattern noise is far less assessed. The goal of this work is to develop a tool that allows to evaluate the performance of computer vision algorithms and their implementations subject to deviations of the physical parameters of the CMOS sensor. This tool will facilitate the quantification of the high-level effects produced by circuit random noise, enabling the optimization of the sensor during the design flow with specifications much closer to the application scope. Likewise, this tool will provide the electronic designer with a relationship between high-level algorithm accuracy and maximum fixed pattern noise. Thus the contribution is double: (1) to evaluate the performance of both local float type and more recent binary type detectors and descriptors when combined under a variety of image transformations, and (2) to extract relevant information from circuit-level simulation and to develop a basic noise model to be employed in the design of the feature descriptor evaluation. The utility of this approach is illustrated by the evaluation of the effect of column-wise and pixel-wise fixed pattern noise at the sensor on the performance of different local feature descriptors.