Recognition of Plain Objects Using Local Region Matching

Conventional interest point based matching requires computationally expensive patch preprocessing and is not appropriate for plain objects with negligible detail. This paper presents a method for extracting distinctive interest regions from images that can be used to perform reliable matching between different views of plain objects or scene. We formulate the correspondence problem in a Naive Bayesian classification framework and a simple correlation based matching which makes our system fast, simple, efficient, and robust. To facilitate the matching using a very small number of interest regions, we also propose a method to reduce the search area inside a test scene. Using this method, it is possible to robustly identify objects among clutter and occlusion while achieving near real-time performance. Our system performs remarkably well on different plain objects where some state-of-the art methods fail. Since our system is particularly suitable for the recognition of plain object, we refer to it as Simple Plane Object Recognizer (SPOR). key words: object recognition, interest point, interest region, region matching.