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Integration of Component Image Information and Design Information by Graph to Support Product Design Information Reuse

제품 설계 정보 재사용을 위한 그래프 기반의 부품 영상 정보와 설계 정보의 병합

  • Published : 2006.12.31

Abstract

Recently, distributed collaborative development environment has been recognized an alternative environment for product development in which multidisciplinary participants are naturally involving. Reuse of Product design information has long been recognized as one of core requirements for efficient product development. This paper addresses an image-based retrieval system to support product design information reuse. In the system, product images obtained from multi-modal devices are utilized to reuse design information. The proposed system conducts the segmentation of a product image by using a labeling method and generates an attributed relational graph (ARG) that represents properties of segmented regions and their relationships. The generated ARG is extended by integrating corresponding part/assembly information. In this manner, the reuse of assembly design information using a product image has been realized. The main advantages of the presented system are following. First, the system is not dependent to specific design tools, because it utilizes multimedia images that can be obtained easily from peripheral devices. Second ratio-based features extracted from images enable image retrievals that contain various sizes of parts. Third, the system has shown outstanding search performance, because we applied various information of segmented part regions and their relationships between parts.

최근에는 제품 개발의 효율성 증대를 위해서 다양한 분야의 전문가들이 참여하는 분산된 협업적 개발 환경이 필수적으로 인식되고 있으며 이에 따른 제품 설계 정보의 재사용 문제가 새롭게 부각되고 있다. 본 논문에서는 제품 설계 정보의 재사용을 위해 멀티미디어 기기에서 획득된 제품 영상을 부품 단위로 분할하여 속성 관계 그래프를 생성하고 이를 통해 제품 설계 정보를 검색하는 시스템을 제안한다. 본 논문에서 제안한 시스템은 라벨링 방법으로 제품 영상을 분할하고, 각 분할 영역의 속성과 영역간의 관계를 표현하는 속성 관계 그래프를 생성한다. 속성 관계 그래프는 제품 설계 정보를 추가하여 확장함으로써 영상 검색을 통한 제품 설계 정보의 재사용이 가능하다. 본 시스템의 주요 이점은 다음과 같다. 첫째, 주변 장치로부터 쉽게 획득할 수 있는 멀티미디어 영상을 이용하므로 특정 설계 툴에 종속적이지 않다. 둘째, 비율을 이용한 특징 벡터에 의해 다양한 크기의 유사 부품을 포함하는 영상의 검색이 가능하다. 셋째, 분할된 각 부품의 영역에 대한 다양한 영상 정보와 그들의 관계를 적용하기 때문에 검색 능력이 뛰어나다.

Keywords

References

  1. F. J. Barry, N. Rossiter, K. M. Chao, 'An agent system for collaborative version control in engineering,' Integrated Manufacturing Systems, Vol.11, No.4, pp.258-266, 2000 https://doi.org/10.1108/09576060010326384
  2. D. G. Ullman, 'The mechanical design process,' 2nd ed. New York, McGraw-Hill, 1997
  3. N. Iyer, S. Jayanti, K. Lou, Y. Kalyanaraman, K. Ramani, 'Shape-based searching for product lifecycle applications,' Computer-Aided Design, pp.1435-1446, 2005 https://doi.org/10.1016/j.cad.2005.02.011
  4. M. Elad, A. Tal, S. Ar, 'Content based retrieval of VRML objects an iterative and interactive approach,' Eurographics Mltimedia Workshop, pp.97-108, 2001
  5. M. Kazhdan, T. Funkhouser, S. Rusinkiewicz, 'Rotation invariant, spherical harmonic representation of 3D shape descriptors,' Proceedings of ACM, Eurographics Symposium on Geometry Processing, pp.167-175, 2003
  6. D. Vranic, D. Saupe, J. Richter, 'Tools for 3D object retrieval: Karhunen-Loeve transform and spherical harmonics,' Proceedings of IEEE 2001 Workshop on Multimedia Signal Processing, pp.293-298, 2001
  7. R. Osada, T. Funkhouser, C. Chazelle, D. Dobkin, 'Shape distributions,' ACM Trans on Graph, pp.807-832, 2002 https://doi.org/10.1145/571647.571648
  8. M. Ankerst, G. Kastenmu lIer, H. P. Kriegel, T. Seidl, '3D shape histograms for similarity search and classification in spatial databases,' Proceedings of Sixth Symposium on Large Spatial Databases, pp.207-226, 1999
  9. M. El-Mehalawi, R. Miller, 'A database system of mechanical components based on geometric and topological similarity,' Part II: Indexing, Retrieval, Matching, and Similarity Assessment, Comput Aided Design, pp.95-105, 2003 https://doi.org/10.1016/S0010-4485(01)00178-6
  10. C. M. Cyr, B. Kimia, '3D object recognition using shape similarity-based aspect graph,' Proceedings of ICCV'01, pp.254-261, 2001 https://doi.org/10.1109/ICCV.2001.10043
  11. C. Carson, 'Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, No.8, August, 2002 https://doi.org/10.1109/TPAMI.2002.1023800
  12. J. D. Bock, P. D. Bock, W. Philips, 'Watershed and Normalized Cuts as basic tools for Perceptual Grouping,' ProRISC, pp.238-245, 2004
  13. N. Alon, 'Eigenvalues and Expanders,' Combinatorica, Vol.6, No.2, pp.83-96, 1986 https://doi.org/10.1007/BF02579166
  14. R. Bajcsy, S. W. Lee and ALeonarids, 'Color image segmentation with detection of highlights and local illumination induced by inter-reflection,' Proceedings of International Conference on Pattern Recognition, Atlantic City, NJ, pp.785-790, 1990 https://doi.org/10.1109/ICPR.1990.118217
  15. D. Wang, P. hakghton, I. Wang, nad, A. vincent, 'Motion estimation using segmentation and consistency constrint,' Proceedings of SPIE Conf. Visual Comm. Image Processing, Vol.3024, pp.667-708, 1997 https://doi.org/10.1117/12.263268
  16. J. Fan, D. K. Y. Yau, 'Automatic Image Segmentation by Integrating Color-Edge Extraction and Seeded Region Growing,' IEEE Transactions on Image Processing, Vol.10, No.10, pp.1454-1466, 2001 https://doi.org/10.1109/83.951532
  17. I. S. Dhillon, Y. Guan, B. Kulis, 'Kernel k-means, Spectral Clustering and Normalized Cuts,' SIGKDD, pp.551-556, 2004 https://doi.org/10.1145/1014052.1014118
  18. S. Aksoy, R. Haralick, 'Feature Normalization and Likelihood-based Similarity Measures for Image Retrieval,' Pattern Recognition Letters, Vol.22, No.5, pp.563-582, 2001 https://doi.org/10.1016/S0167-8655(00)00112-4
  19. Saupe D, Vranic V. D. '3D Model retrieval with spherical harmonics and moments,' Proceedings of the DAGM, Munich, Germany, pp.392 - 397, 2001
  20. 이형재, 김용일, 양형정, '제품 설계 정보와 영상 데이터의 병합을 위한 에지 기반 라벨링에 의한 영상분할,' 정보처리학회 추계학술발표논문집(상) 제12권, 제2호, 2005
  21. J. Canny, 'A Computational Approach to Edge Detection,' IEEE Trans. Pattern Analysis and Machine Intelligence, pp.679-714, 1986 https://doi.org/10.1109/TPAMI.1986.4767851
  22. D. Maar, E. Hildreth, 'Theory of edge detection,' Proceedings Royal Soc. London, Vol. 207, pp.187-217, 1980 https://doi.org/10.1098/rspb.1980.0020
  23. J. Fan, D. K. Y. You, AK Elmagarmid, WG Aref, 'Automatic Image Segmentation by Integrating Color-Edge Extraction and Seeded Region Growing,' IEEE transaction on image processing, Vol. 10, No. 10, 2001 https://doi.org/10.1109/83.951532
  24. S. Shamik, Q. Gang, P. Sakti, 'A Histogram with Perceptually Smooth Color Transition for Image Retrieval,' CVPRIP, 2002
  25. X. S. Zhou, S. Thomas, 'Edge-Based Structural Features for Content-Based Image Retrieval,' Pattern Recognition Letters, Vol.22, No.5, pp.457-468, 2001 https://doi.org/10.1016/S0167-8655(00)00124-0
  26. Y. Landom, H. J. Wolfson, 'Geometric hashing: A General and Efficient Model-Based Recognition Schem,' Proceedings of ICCV 1998
  27. S. Ruiz-Correa, L. Shapiro, 'Meila M. A new signature based method for efficient 3D object recognition,' Proceedings of CVPR'00, 2000 https://doi.org/10.1109/CVPR.2001.990554
  28. B. Ozer, W. Wolf, A. N. Akansu, 'A Graph Based Object Description for Information Retrieval in Digital Image and Video Libraries,' In Proceedings of IEEE Workshop on Content-based Access of Image and Video Libraries, pp.79-83. 1999
  29. G. Cybenko, A. Bhasin, K. Cohen, 'Pattern recognition of 3D CAD objects,' Smart Eng Syst Des, pp.11-13, 1997
  30. H. Rea, J. Corney, D. Clark, J. Pritchard, M. Breaks, R. Macleod, 'Part sourcing in a global market,' Proceedings of ICeCE'01, Beijing, 2001
  31. B. Huet, E. R. Hancock, 'Inexact Graph Retrieval,' In Proceedings of IEEE Workshop on Content-based Access of Image and Video Libraries, Colorado. USA, pp. 40-44. 1999