Color Machine Vision Design Methodology of a Part-Presentation Algorithm for Automated Poultry Handling

This article offers an image processing method to optimize the design of a three-level color machine vision algorithm illustrated in the context of presenting a whole chicken carcass for subsequent handling, which overcomes some common problems in image segmentation, feature identification, and pose estimation for classifying, identifying, and locating poultry meat products. First, artificial color contrast and principal component analysis are optimized to enhance the contrast between similar colors to extract targeted regions more effectively. Second, the boundaries of poultry products are mostly smooth curves that are proposed as features for object recognition. Third, image feature points are found using template matching, from which the object (size, location, and pose) can be accurately determined. The algorithm simplifies the complex edges of a poultry product into a finite number of arc-centers, which greatly improves the efficiency/accuracy of the matching for part-presentation. Evaluated with 100 randomly posed samples, the algorithm has a success rate of 93%. The 7% failures were primarily due to missing points, which can be eliminated by incorporating more template points.