A Memory Reduction Method for 3D Object Recognition Based on Selection of Local Features
نویسندگان
چکیده
The task of object recognition can be classified into two categories: generic and specific. Generic object recognition is to recognize classes of objects such as “a chair” and “a car”. Specific object recognition, on the other hand, is for identifying object instances such as a specific type of chair and car, in other words “the chair” and “the car”. This paper concerns the latter, especially methods which employ local features such as SIFT (scale-invariant feature transform)[1] for modeling and recognizing 3D objects. As a recognition method, we focus here on a simple one based on voting by matching local features. Although such a simple method offers high recognition rate, it poses the following problems caused by a large number of local features. First, matching of local features (finding their nearest neighbors) requires a long processing time. Second, many local features need immense amounts of storage.
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