A Methodology for the Development of Machine Vision Algorithms Through the use of Human Visual Models

• Main Author: Daley, Wayne D. R.
• Format: Others
• Language: en_US
• Published: Georgia Institute of Technology 2005
• Subjects: Machine vision; Color imaging; Natural products
• Online Access: http://hdl.handle.net/1853/4996

The development of machine vision algorithms for inspection and machine guidance has traditionally relied on the knowledge and experience of the developers as most of the techniques are based on heuristics and trial and error. This is especially problematic in the area of natural products where variability of the products is the rule rather than the exception. Humans are particularly good in functioning in this arena and in this thesis we look at the development of techniques derived from the functions of the human visual system (HVS). We first identify the significant processes in the HVS and highlight those that we believe are germane to the problems of interest. We then develop computational techniques using these methods and demonstrate their applicability to practical problems. This thesis uses the knowledge that the HVS is considered to consist of three sequential operations (sensing; encoding/transfer; and image interpretation) as a basis for developing a parallel procedure for a machine vision system. We have found that outputs derived from a simulation of the behaviors of receptive fields in the retina and in the higher levels of the brain can generate useful and robust features. Equivalent processes are then developed for machine applications under the guidance of a human operator to identify the areas of interest in the scene for the problem under consideration. Specifically we use the processes for encoding/transfer of data from the retina to develop techniques to enhance color contrasts, and compute color image features that are useful for defect detection and identification in real world images. This is accomplished by a transformation from image space to a characteristic response space that improves the robustness of classification. In this thesis the approach developed is applied to two industrial problems in the quality monitoring of meat and vegetables. The first problem concerns the quality monitoring of breast butterflies and the other the detection of defects on the surface of citrus. The approach is shown to derive algorithms that are robust and can be implemented at high rates of speed. Additionally we also identify a model within which further developments can be conducted as we learn more about the functioning of the HVS.