Open Access Journal of Scientific, Technology & Engineering Research

International Journal of Technology Enhancements and Emerging Engineering Research (ISSN 2347-4289)

IJTEEE >> Volume 3 - Issue 7, July 2015 Edition

International Journal of Technology Enhancements and Emerging Engineering Research  
International Journal of Technology Enhancements and Emerging Engineering Research

Website: http://www.ijteee.org

ISSN 2347-4289

Design Features Recognition Using Image Processing Techniques

[Full Text]



Sreenivasulu Reddy, Poornachandra Sekhar, Hitheshwar Naik



Keywords : Feature extraction and recognition, Image processing techniques, geometric data extraction, SOBEL edge detection algorithm, Canny edge detection algorithm, MINBOUNDSUITE algorithm, RGB image.



ABSTRACT: Design features refer as manufacturing information sets of shape related attributes of a work part. Features recognition is the most relevant methods of image analysis. In this paper, an ideal method is developed to extract and recognize different shape features using digital image processing techniques. The geometric data extraction algorithm is developed with SOBEL, CANNY Edge detection algorithms for features extraction and MINBOUNDSUITE algorithm for features recognition. The methods involved are colored (RGB) image to black and white image (Binary) conversion, boundary and edge detection, area based filtering, use of bounding box and its properties for calculating object metrics. The algorithm was developed in MATLab package.



[1] Huang Z, Yip-Hoi D. (2002), High-level feature recognition using feature relationship graphs. Computer Aided Design, No. 34, pp. 561–582.

[2] Emad S. Abouel Nasr and Ali K. Kamrani. (2006), A new methodology for extracting manufacturing features from CAD system, Computers & Industrial Engg, No. 51, pp. 389–415.

[3] Shalinee Patel, Pinal Trivedi, and Vrundali Gandhi, “2D Basic Shape Detection Using Region Properties”, International Journal of Engineering.

[4] Du .C.-J. and Sun .D.-W, “Recent developments in the applications of image processing techniques for food quality evaluation”, Trends in Food Science & Technology 15 (2004), 230–249.

[5] Kenneth. R. Castleman, Z. G. Zhu. “Digital Image Processing”. Publishing House of Electronics Industry, Beijing, 1999.

[6] Irwin Sobel, 2014, “History and Definition of SOBEL Edge Detection Algorithm”.

[7] Canny, J., “A Computational Approach to Edge Detection”, IEEE Trans. Pattern Analysis and Ma-chine Intelligence, 1986.

[8] Moeslund, T. (2009, March 23). “Canny Edge De-tection”. Retrieved December 3, 2014

[9] Freeman, R. Shapira, “Determining the Minimum-Area Encasing Rectangle for an Arbitrary Closed Curve”, Communications of the ACM Vol. 18 No. 7 (1975).

[10] Zhang K.F, Wright A.J. and Davies B.J. (1989), A feature-recognition knowledge base for process planning of rotational mechanical components, International Journal of Advanced Manufacturing Technology, Vol. 4, pp.13–25.

[11] Shah, J. J., Mantyla, M. and Nau, D., Advances in Feature Based Manufacturing. Elsevier/North-Holland, Amsterdam, 1994.

[12] Proyecto Fin de Carrera, “Feature Recognition Algorithms” June 16, 2010

[13] Meisel .W, “Computer-Oriented Approaches to Pattern Recognition”. Academic Press. New York, 1972.

[14] Zhou, P., Ye, W., & Wang, Q. (2011). “An Im-proved Canny Algorithm for Edge Detection”. Journal of Computational Information Systems