Shi, Hao, Naghdy, Fazel and Cook, Christopher D (1993) Three-Dimensional object modelling and recognition using absorption in a colour liquid. In: San Diego '92.

Abstract

The application of computer vision in industry has been increasing as greater use is made of flexible automation and robotics. Quality control and sorting can also be heavily dependent on artificial vision interfaced to an intelligent decision making system. Traditionally industrial tasks requiring computer vision are simplified to a 2-D problem in a plane. This permits the use of a single camera and hence reduces the complexity of the procedures of frame grabbing, image processing and decision making. Such a solution is however not suitable when 3-D information is vital in the control or decision making processes. Generation and processing of 3-D images are required for such applications. The work presented in this paper provides a simple method of deriving a 3-D computer model for a special class of industrial objects and then using this model for machine recognition. The object is immersed in a colour liquid and the intensity of the pixels of the captured image is modulated by the depth of the object along the camera axis. The depth maps generated from the image are represented by parallel layers located in planes normal to the camera axis. The 2-D features of the layers are derived and a 3-D model is constructed for the object based on these features The object is distinguished by contour groups which are classified into three types according to their features. These 3-D features include object features and contour group features. Three steps are adopted for object recognition. The object features are first used in a basic test in order to reduce the number of possible models which an unknown object can match. Secondly, the contour features are used to test each of the contour group models. The models with a higher match rate are then selected for verification using chi-squared (2) statistical methods. Finally the 2 test is employed to verify the above test results. The object match is governed by both the 2 test test. From these tests, a model which best matches the object can be obtained.

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