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An Expert System for Industrial Machine Vision
Yoshifumi KITMURA, Hiroaki SATO, and Hideyuki TAMURA
Information Systems Research Center, Canon Inc. Kashimada, Saiwai-ku, Kawasaki 21 1, Japan

Abstract An expert system for vision algorithm design is presented. Its knowledge-base includes human experts' knowledge about image processing techniques, and is capable of solving given vision problems. As a problem domain, we choose vision algorithms for a parts-feeder, which determines the attitude of mechanical parts on a conveyor-belt and rejects parts with inappropriate attitudes. The expert system for parts feeder is designed to consist of three components: FSE (Feature selection expert), IPE (Image processing expert), DTG (Decision tree generator). The knowledge for vision algorithm design to determine parts attitude is discussed. A framework to represent knowledge for finding solutions for pattern classificationproblem is established.

1. Introduction Recently, several expert systems for image processing have been investigated[1][2][3]. Their knowledge-basesinclude human experts' knowledge about image processing techniques, and can generate a sequence of image processing operations to solve the given problem. DIA-Expert system (Digital-Image-Analysis Expert System)[4] is a typical example. When an input image and the goal of analysis are given, it continues to decompose the goal into subgoals until the sequence of executable image processing modules could be found. Here, we apply this framework to the algorithm design for an industrial machine vision[5]. Machine vision systems for advanced automation must solve problems such as the discriminationor positioning of mechanical parts and the detection of their surface flaws. The contemporary image analysis and pattern recognition techniques may find the solutions to the large portion of these problems.Under the restrictions of speed and time, however, the very limited methods are applicable. The objective parts and flaws will appear almost infinitely in the future products. Thus, in every case we must find the possible solutions under the given conditions. The expert system is capable of designing such vision systems and their algorithms. As a practical task, we consider vision algorithms for a parts-feeder. They determine attitude of mechanical parts on a conveyor-belt and reject the inappropriate ones to assembly machines. The expert system may assist the measurements of shape features of parts and generates the decision tree of object recognition. It is assumed that the expert system may help a production engineer finding the solution when the similar tasks and images as ever are given. The idea of this expert system was proposed in [6]. In this paper, the detail of developed system is described, emphasizing the knowledge to select effective shape features and the framework of its representation.

2. The problem : Determining the attitude of parts for an industrial parts-feeder In order to assemble mechanical parts with an industrial robot, it is expected that a few attitudes out of several stable states of the same part are accepted by the system. Hence we use the machine vision system shown in Fig.1, in which all the parts on the conveyor-belt are observed by a TV camera, and then the inappropriate ones are rejected through image processing operations and decision tree, which determines the attitude by comparing measurements of shape feature values with a threshold value. Here it is assumed that only one kind of part is being i l handled, and that each image w l contain only one part. The image processing procedures to determine the attitude are usually realized as a sequence of simple operations on binary images. In the case of the part in Fig.2, the attitude A is determined by the measurements of two shape feature values; the areas of maximum component,which discriminatethe attitudes (A B) and (C D), and angular feature (angular difference between the perimeter point where the maximum radius occurs and that where the minimum occurs) which discriminate (A) and (B). Both feature values are measured through simple operations on binary images. An example of decision tree to determine the attitude of this part is shown in Fig.3. Such a simple sequence is often very effective in practical vision systems. It may be possible to generate the part attitude determination program using a geometric model of part[6]. However, generated procedure must be robust in the variety of image input conditions, such as fluctuation of lighting condition, soil on the conveyor-belt, difference of parts surface reflectance and so on. Furthermore, the stable image processing procedure in working environments is generated through statistical pattern classification using many image samples. However, the number of parts increases as new products are manufactured. It is not so difficult to design a vision algorithm in each case, but numerous number of parts newly designed day by day cause troublesome work to production engineers.

3. Organization of the system The expert system is designed in order to solve the above problem. When the name of part, the attitude to be accepted, and sample images are given, it generates sequences of image processing operations to measure the shape feature values and decision tree. The expert system for parts feeder is designed to consist of three components:FSE (Feature selection expert),IPE (Image processing expert), DTG (Decisiontree generator) .The Fig.4 shows the organization of expert system. FSE has the knowledge to select the effective shape features for the given part. P E has the knowledge about the usage of image processing operations and it generates the sequence of image processing operations to measure the shape feature values which FSE has selected. DTG calculates the effectiveness value through the estimation of the distribution of measured shape

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