Object Recognition

نویسندگان

  • Juan Andrade-Cetto
  • Michael Villamizar
چکیده

Object recognition is a subproblem of the more general problem of perception, and can be defined as follows. Given a scene consisting of one or more objects, can we identify and localize those objects that are sufficiently visible to the sensory system? It is generally assumed that a description of each object to be recognized is available to the computer and can be used to facilitate the task of identification and localization. These descriptions can either be model-based or appearance-based, or a combination of both. Model-based object representation is based on geometric features, whereas appearance-based representation uses a large set of images for training but does not require any insight into the geometric structure of the objects. Object recognition is a key component of many intelligent vision systems, such as those used in hand-eye coordination for bin picking, inspection, and mobile robotics. Various types of object recognition problems can be stated based on the dimensionality of their spatial description: (1) recognition of a 2-D object from a single 2-D image; (2) recognition of a 3-D object from a single 2-D image; (3) recognition of a 3-D object from a 3-D image (a range map); (4) recognition of a 2-D or 3-D object from multiple 2-D images taken from different viewpoints; and so on. About 40 years ago, research in computer vision began with attempts at solving the problem of how to recognize a general 3-D object using a single 2-D image. Since humans can perform this task effortlessly, it was believed then that designing a computer-based system for accomplishing the same would be easy. However, forty years later this problem remains largely unsolved. In contrast, much progress has been made in recognizing 2-D objects in single 2-D images and in recognizing 3-D objects in range maps. Although not as impressive, considerable progress has also been made in the recognition of 2-D or 3-D objects using multiple 2-D images, as in binocular or multiple-camera stereo. The earliest successful system for the recognition of 2-D objects, such as gaskets used in industrial products, using single camera images was the VS100 Vision Module fielded by SRI [1]. We believe that it was this system that launched industrial interest in computer vision. Another early industrial vision system that also became well known and that is of historical importance is the CONSIGHT system [2]. The HYPER system [3] was used for identifying overlapping flat electromechanical components, and used heuristic tree pruning to speed up the search for a scene-to-model match. Two early systems for the recognition of 3-D objects from single 2-D images are the ACRONYM system [4] and the SCERPO system [5], which used perceptual organization ideas to cope with the lack of depth information from a single image. Some studies on the

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تاریخ انتشار 2003