3D Object Retrieval Using Canny Edge Detection Method

3D objects are an important type of data with many applications in domains such as Engineering and Computer Aided Design, Science, Simulation, Visualization, Cultural Heritage, and Entertainment. Technological progress in acquisition, modeling, processing, and dissemination of 3D geometry leads to the accumulation of large repositories of 3D objects. Consequently, there is a strong need to research and develop technology to support the effective retrieval of 3D object data from 3D repositories. As the amount of new information generated in the world rapidly increases, efficient search in collections of structured data, texts and multimedia objects. 3D objects are an important type of multimedia data with many applications such as medical, chemical, CAD, etc. Recently with the development of 3D modeling and digitizing tools, more and more models have been created, which leads to the necessity of the technique of 3D mode retrieval system. In this paper a novel methodology is presents for 3D objects retrieval by using canny edge detection. The method is based on our previously presented proof-of-concept 3D retrieval method. This work shows how to use and extend well-developed techniques in computer vision to address fundamental problems in shape representation and rendering. The main focus on the idea that edges define boundaries and those regions are contained within these edges. The purpose of canny edge detection in general is to significantly reduce the amount of data in a 3D objects. Currently our algorithm finds good feature results for 3D objects having straight edges and extract good features. Experimental results show the effectiveness of our Method.