Uriel Rosa

Introduction

Cameras designed for robotic, and/or autonomous vehicle vision applications have typically been adapted from existing human-intended pipelines. However, robotic handling of images does not necessarily have to emulate the human vision systems for achieving high performance, or reduced costs. The semantic labeling of images classified by CNN (convolutional neural network) approaches might be substantially influenced by the design parameters of the cameras acquiring the images. In this study, camera pipeline parameters were modified to investigating the effects of replacing typical in focus RGB images with similar images reprocessed as monochrome, defocused to include chromatic aberration effects and defocused monochrome images. The ieCameraDesigner ISET [1] application software was configured in four distinct camera designs to generate rgb, monochromatic, defocused and a combination of these effects in distinct pipelines. Natural images of African mammals downloaded from David Cardinal’s data set [2] were screened for close similarity to produce a base dataset of containing images processed by a state-of-the-art CNN, the Resnet-50. The goal of this study is to evaluate simulated effects of the camera parameters monochromatic and chromatic defocusing on the performance of semantic labeling computed by convolutional neural networks.

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