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Current research on computer vision often needs specific techniques for particular problems. Little use has been made of high-level
aspects of computer vision, such as three-dimensional (3D) object recognition, that are appropriate for large classes of problems and
situations. In particular, high-level vision often focuses mainly on the extraction of symbolic descriptions, and pays little attention to
the speed of processing. In order to extract and recognize target intelligently and rapidly, in this paper we developed a new 3D target
recognition method based on inherent feature of objects in which cuboid was taken as model. On the basis of analysis cuboid nature
contour and greyhound distributing characteristics, overall fuzzy evaluating technique was utilized to recognize and segment the target.
Then Hough transform was used to extract and match model's main edges, we reconstruct aim edges by stereo technology in the end.
There are three major contributions in this paper. Firstly, the corresponding relations between the parameters of cuboid model's
straight edges lines in an image field and in the transform field were summed up. By those, the aimless computations and searches in
Hough transform processing can be reduced greatly and the efficiency is improved. Secondly, as the priori knowledge about cuboids
contour's geometry character known already, the intersections of the component extracted edges are taken, and assess the geometry of
candidate edges matches based on the intersections, rather than the extracted edges. Therefore the outlines are enhanced and the noise
is depressed. Finally, a 3-D target recognition method is proposed. Compared with other recognition methods, this new method has a
quick response time and can be achieved with high-level computer vision. The method present here can be used widely in vision-guide
techniques to strengthen its intelligence and generalization, which can also play an important role in object tracking, port AGV, robots
fields. The results of simulation experiments and theory analyzing demonstrate that the proposed method could suppress noise effectively, extracted target edges robustly, and achieve the real time need. Theory analysis and experiment shows the method is
reasonable and efficient.
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