A Stochastic Shape and Color Model for Defect Detection in Potatoes

Abstract

Automatic defect detection in color images of potatoes is complicated by the random variability ordinarily observed in a collection of normal potatoes. Stochastic models are developed that explicitly describe the random nature of the shape and color texture observed in normal potatoes. Shape and color simulations based on these models are realistic. Statistical tests based on the stochastic shape and color models are developed. The tests are capable of classifying an unknown object as either “acceptable potato” or “unacceptable potato.” Twelve potatoes are analyzed, and the experimental results are presented.