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A NEW ADAPTED CANNY FILTER FOR EDGE DETECTION IN RANGE IMAGES


(Received: 25-May-2021, Revised: 20-Jul.-2021 , Accepted: 11-Aug.-2021)
Image segmentation remains as one of the most important tasks for image analysis and understanding. It deals with raw images in order to prepare them to be usable in automatic high-level processes, such as classification or information retrieval. We present in this paper a new adapted edge detector for range images. Its principle is inspired from the Canny detector, so the inherent features of range images will be considered. Usually, Canny detector is used with greyscale or color images, where its direct application with depths does not provide satisfactory results. From the raw image, containing measured depths, a relief image that consists of an image of normal vectors to the local surfaces is computed. So, angles between neighboring vectors are used to compute an angle-based gradient. The latter is integrated in the Canny algorithm, so an edge map is produced for the range image. Real images from the ABW database were used in experimentation, where the proposed new detector has outperformed the original Canny one by a ratio of 18%.

[1] A. Hoover, G. Jean-Baptiste, X. Jiang, P. J. Flynn, H. Bunke, D. B. Goldgof, K. W. Bowyer, D. W. Eggert, A. W. Fitzgibbon and R. B. Fisher, "An Experimental Comparison of Range Image Segmentation Algorithms," IEEE Trans. on Pattern Analysis and Machine Intell., vol. 18, no. 7, pp. 673–689, 1996.

[2] C. S. Won, R. M. Gray and M. Robert, Stochastic Image Processing, Springer Science & Business Media, ISBN 978-1-4419-8857-7, 2004.

[3] H. Bustince, M. Pagola, A. Jurio, E. Barrenechea, J. Fernández, P. Couto and P. Melo-Pinto, "A Survey of Applications of the Extensions of Fuzzy Sets to Image Processing," Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition, Springer, Heidelberg, vol. 256, pp. 3-32, 2009.

[4] M. Versaci and F. C. Morabito, "Image Edge Detection: A New Approach Based on Fuzzy Entropy and Fuzzy Divergence," International Journal of Fuzzy Systems, vol. 23, no. 5, pp. 918–936, 2021.

[5] J. Fang, H. Liu, L. Zhang, J. Liu and H. Liu, "Region-edge-based Active Contours Driven by Hybrid and Local Fuzzy Region-based Energy for Image Segmentation," Information Sciences, vol. 546, no. 6, pp. 397-419, 2021.

[6] T.J. Fan, G.G. Medioni and R. Nevatia, "Segmented Description of 3-D Surfaces," IEEE Journal on Robotics and Automation, vol. 3, no. 6, pp. 527–538, 1987.

[7] X. Jiang and H. Bunke, "Edge Detection in Range Images Based on Scan Line Approximation," Computer Vision and Image Understanding, vol. 73, no. 2, pp. 183–199, 1999.

[8] M. Basu, "Gaussian-based Edge-detection Methods: A Survey, " IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol. 32, no. 3, pp. 252–260, 2002.

[9] Y. Ding, X. Ping, M. Hu and D. Wang, "Range Image Segmentation Based on Randomized Hough Transform," Pattern Recognition Letters, vol. 26, no. 13, pp. 2033–2041, 2005. 

[10] A. Bab Hadiashar and N. Gheissari, "Range Image Segmentation Using Surface Selection Criterion," IEEE Transactions on Image Processing, vol. 15, no. 7, pp. 2006–2018, 2006.

[11] D. Holz and S. Behnke, "Fast Range Image Segmentation and Smoothing Using Approximate Surface Reconstruction and Region Growing," Intelligent Autonomous Systems, vol. 12, pp. 61–73, 2013.

[12] D. Holz and S. Behnke, "Approximate Triangulation and Region Growing for Efficient Segmentation and Smoothing of Range Images," Robotics and Autonomous Systems, vol. 62, no. 9, pp. 1282–1293, 2014.

[13] S. Gupta, R. B. Girshick, P. Andres Arbelaez and J. Malik, "Learning Rich Features from RGB-D Images for Object Detection and Segmentation," Proc. of the European Conference on Computer Vision, arXiv:1407.5736, pp. 345–360, 2014.

[14] A. Garcia-Garcia, S. Orts-Escolano, S. Oprea, V. Villena-Martinez, P. Martinez-Gonzalez and J. Garcia- Rodriguez, "A Survey on Deep Learning Techniques for Image and Video Semantic Segmentation, " Applied Soft Computing, vol. 70, pp. 41–65, 2018.

[15] B. Parvin and G. Medioni, "Segmentation of Range Images into Planar Surfaces by Split and Merge," Computer Vision Pattern Recognition, pp. 415-417, 1986.

[16] R. W. Taylor, M. Savini and A. P. Reeves, "Fast Segmentation of Range Imagery into Planar Regions," Computer Vision, Graphics and Image Processing, vol. 45, no. 1, pp. 42-60, 1989.

[17] A. V. Bhavsar and A. N. Rajagopalan, "Inpainting Large Missing Regions in Range Images," Proc. of the 20th IEEE International Conference on Pattern Recognition, pp. 3464-3467, Istanbul, Turkey, 2010.

[18] P. J. Besl and R. C. Jain, "Segmentation through Variable-order Surface Fitting," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 2, pp. 167-192, 1988.

[19] N. Yokoya and M. D. Levine, "Range Image Segmentation Based on Differential Geometry: A Hybrid Approach," IEEE Trans. on Pattern Analysis and Machine Intell., vol. 11, no. 6, pp. 643-649, 1989.

[20] T. Kasvand, "The k1k2 Space in Range Image Analysis," Proc. of the 9th IEEE International Conference on Pattern Recognition, IEEE Computer Society, pp. 923-926, Rome, Italy, 1988.

[21] A. Gupta and R. K. Bajcsy, "Integrated Approach for Surface and Volumetric Segmentation of Range Images Using Biquadrics and Superquadrics," Applications of Artificial Intelligence X: Machine Vision and Robotics, International Society for Optics and Photonics, vol. 1708, pp. 210-227, 1992.

[22] A. Gupta and R. Bajcsy, "Volumetric Segmentation of Range Images of 3D Objects Using Super Quadric Models," CVGIP: Image Understanding, vol. 58, no. 3, pp. 302-326, 1993.

[23] X. Jiang and H. Bunke, "Fast Segmentation of Range Images into Planar Regions by Scan Line Grouping," Machine Vision and Applications, vol. 7, no. 2, pp. 115-122, 1994.

[24] A. Davignon, "Contribution of Edges and Regions to Range Image Segmentation," Applications of Artificial Intelligence X: Machine Vision and Robotics, International Society for Optics and Photonics, vol. 1708, pp. 228-239, 1992.

[25] Y. C. Wong, L. J. Choi, R. S. S. Singh, H. Zhang and A. R. Syafeeza, "Deep Learning-based Racing BIB Number Detection and Recognition," Jordanian Journal of Computers and Information Technology (JJCIT), vol. 5, no. 3, pp. 181-194. 2019.

[26] C. Mohamed and M. Smaine, "Edge Detection in Range Images Using a Modified Canny Filter," Proc. of the IEEE International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS), vol. 1, pp. 1-7, Skikda, Algeria, 2019.

[27] J. Canny, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pp. 679-698, 1986.

[28] J. P. D'Haeyer, "Gaussian Filtering of Images: A Regularization Approach," Signal Processing, vol. 18, no. 2, pp. 169-181, 1989.

[29] S. Bhardwaj and A. Mittal, "A Survey on Various Edge Detector Techniques," Procedia-Technology, vol. 4, pp. 220-226, 2012.

[30] D. J. Olive, "Multiple Linear Regression," Linear Regression Book, Springer, Cham, pp. 17-83, 2017.

[31] S. Mazouzi and Z. Guessoum, "A Fast and Fully Distributed Method for Region-based Image Segmentation," Journal of Real-time Image Processing, vol. 18, no. 3, pp. 793-806, 2021.

[32] A. Richtsfeld, T. Morwald, J. Prankl, M. Zillich and M. Vincze, "Segmentation of Unknown Objects in Indoor Environments," Proc. of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4791–4796, Vilamoura-Algarve, Portugal, 2012.

[33] L. R. Dice, "Measures of the Amount of Ecologic Association between Species," Ecology, vol. 26, no. 3, pp. 297-302, 1945.