Off-the-shelf system for color and multispectral based recognition and control

Abstract

Traditional color based machine vision systems rely on single-color, simple-outline models for object recognition. In controlled environments with carefully chosen lighting, this model is often reasonable. Mobile robots typically operate in environments where objects of interest are likely to appear as mixtures of colors and textures with complex outlines. To use color or multispectral imagery for identification and decision-making, systems that can be trained by example to recognize such objects have distinct advantages. WAY-2C is a system for color-based recognition that relies on a non-parametric statistical model. It is quickly trained by example and provides robust classifications in the presence of real-world complexities.