بخش‌بندي تصاوير رنگي بيروني به هدف تشخيص اشياء به كمك هيستوگرام با دقت دوگانه
محورهای موضوعی : عمومى
جواد راستي
1
(مهندسی پزشکی)
سید امیرحسن منجمی
2
(اصفهان)
عباس وفایی
3
(دانشگاه اصفهان)
کلید واژه: تصاوير بيروني, خوشه‌بندي, بخش‌بندي رنگي, دقت تصوير.,
چکیده مقاله :
يكي از مسايل مهم در پردازش خودكار تصاوير بيروني، نحوه بخش‌بندي اين تصاوير به هدف تشخيص شيء در آنها مي‌باشد. مشخصات خاص اين تصاوير از جمله تنوع رنگ، اثرات نوري متفاوت، وجود سايه‌هاي رنگي، جزييات بافتي زياد و وجود اشياء كوچك و ناهمگن باعث مي‌شود مسأله بخش‌بندي تصاوير بيروني به ويژه بخش‌بندي رنگي با چالش‌هاي جدي مواجه شود. در تحقيقات قبليبراي خوشه‌بندي رنگي تصاوير بيروني به هدف بخش‌بندي ابتدايي، روشي مبتني بر الگوريتم خوشه‌بندي k-means در بستري با دقت چندگانه پيشنهاد شده بود.اين روش با استفاده از محو عمدي جزييات بافتي تصوير و حذف كلاسهاي محرز در تصاوير محو شده و سپس اضافه كردن كلاسها در تصاوير با دقت بالاتر، كارايي مناسبي براي بخش‌بندي ابتدايي اين تصاوير در مقايسه با روش k-means عادي نشان مي‌داد.در اين مقاله، يك روش تطبيق‌پذير با تصوير با استفاده از هيستوگرام حلقوي ته‌رنگ براي تشخيص كلاسهاي محرز در تصاوير محوشده در بستري با دقت دوگانه پيشنهاد گرديده است.كارايي اين الگوريتم به كمك يك روش ارزيابينظارت‌شده روي دو پايگاه داده از تصاوير بيروني بررسي شده كه حدود 20% كاهش خطاي پيكسلي در بخش‌بندي و نيز دقت و حدود 30% سرعت بيشتر در همگرايي الگوريتم خوشه‌بندي، نشانگر كيفيت بالاتر روش پيشنهادي نسبت به روش عادي است.
One of the important issues in the automatic processing of external images is how to divide these images for the purpose of recognizing something in them. The special characteristics of these images, including color diversity, different light effects, the presence of colored shadows, many texture details, and the existence of small and heterogeneous objects, make the problem of segmentation of external images, especially color segmentation, face serious challenges. In previous researches, a method based on the k-means clustering algorithm was proposed in a multi-accuracy bed for color clustering of external images for the purpose of primary segmentation. This method uses deliberate blurring of image textural details and removal of specific classes in blurred images and then added The classification of classes in images with higher accuracy showed a suitable performance for the initial segmentation of these images in comparison with the normal k-means method. In this article, an image-adaptive method using the ring histogram of the dark color to identify specific classes in blurred images in the bed is presented. It has been proposed with double precision. The efficiency of this algorithm has been investigated with the help of a supervised evaluation method on two databases of external images, which shows a 20% reduction in pixel error in segmentation, as well as a 30% higher accuracy and speed in the convergence of the clustering algorithm, indicating a higher quality. The proposed method is better than the normal method.
[1]. W. W. Mayol, "Wearable Visual Robots," Ph.D, Computer Science, University of Oxford, 2004.
[2]. M. Everingham, B. T. Thomas, and T. Troscianko, "Wearable mobility aid for low vision using scene classification in a Markov random field model framework," International Journal of Human Computer Interaction, special issue on mediated reality, vol. 15, pp. 231-244, 2003.
[3]. R. C. González and R. E. Woods, Digital Image Processing: Pearson/Prentice Hall, 2008.
[4]. R. Manduchi, "Learning Outdoor Color Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1713-1723, 2006.
[5]. J. Batlle, A. Casals, J. Freixenet, and J. Martí, "A review on strategies for recognizing natural objects in colour images of outdoor scenes," Image and Vision Computing, vol. 18(6-7), pp. 515-530, 2000.
[6]. Y.-W. Tai, J. Jia, and C.-K. Tang, "Soft Color Segmentation and Its Applications," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, pp. 1520-1537, 2007.
[7]. H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, "Color Image Segmentation: Advances & Prospects," Pattern Recognition, vol. 34, pp. 2259–2281, 2001.
[8]. H. B. M'hadheb, A. Douik, M. M. Fendri, and M. Annabi, "Reduction of color variability in color image segmentation," in IEEE International Conference on Electronics, Circuits and Systems, 2006.
[9]. I. Ashdown, "Octree color quantization," in Radiosity: A Programmer's Perspective, ed: Wiley New York 1994.
[10] P. Heckbert, "Color image quantization for frame buffer display," SIGGRAPH Comput. Graph., vol. 16, pp. 297-307, 1982.
[11]. S. J. Wan, P. Prusinkiewicz, and S. K. M. Wong, "Variance based color image quantization for frame buffer display," Color Res. Applicat, vol. 15(1), pp. 52-58, 1990.
[12]. P. Scheunders, "A comparison of clustering algorithms applied to color image quantization," Pattern Recognition Letters, vol. 18, pp. 1379-1384, 1997.
[13]. N. Vlajic and H. C. Card, "Vector quantization of images using modified adaptive resonance algorithm for hierarchical clustering," IEEE Transactions on Neural Networks, vol. 12, pp. 1147-1162, 2001.
[14]. B. Fritzke, "A Growing Neural Gas Network Learns Topologies," Advances in Neural Information Processing Systems, 1995.
[15]. A. Baraldi and P. Blonda, "A survey of fuzzy clustering algorithms for pattern recognition. II," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 29, pp. 786-801, 1999.
[16]. G .A.Carpenter , S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen, "Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps," IEEE Transactions on Neural Networks and Learning Systems, vol. 3, pp. 698-713, 1992.
[17]. N. Papamarkos, A. E. Atsalakis, and C. P. Strouthopoulos, "Adaptive color reduction," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 32, pp. 44-56, 2002.
[18]. G. Cheng, J. Yang, K. Wang, and X. Wang, "Image Color Reduction Based on Self-Organizing Maps and Growing Self-Organizing Neural Networks," in The Sixth International Conference on Hybrid Intelligent Systems, 2006, p. 24.
[19]. K. Zagoris, N. Papamarkos, and I. Koustoudis, "Color Reduction Using the Combination of the Kohonen Self-Organized Feature Map and the Gustafson-Kessel Fuzzy Algorithm," in The 5th international conference on Machine Learning and Data Mining in Pattern Recognition, Leipzig, Germany, 2007, pp. 703-715.
[20]. A. Atsalakis, N. Papamarkos, and I. Andreadis, "On estimation of the number of image principal colors and color reduction through self-organized neural networks," International Journal of Imaging Systems and Technology, vol. 12, pp. 117-127, 2002.
[21]. J. Rasti, A. Monadjemi, and A. Vafaei, "Color reduction using a multi-stage Kohonen Self-Organizing Map with redundant features," Expert Systems with Applications, vol. 38, pp. 13188-13197, 2011.
[22]. S. Kiranyaz, S. Uhlmann, and M. Gabbouj, "Dominant Color Extraction Based on Dynamic Clustering by Multi-dimensional Particle Swarm Optimization," in The Seventh International Workshop on Content-Based Multimedia Indexing, 2009, pp. 181-188.
[23]. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification: Wiley, 2001.
[24]. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms: Kluwer Academic Publishers, 1981.
[25]. M. Y. Choong, W. L. Khong, W. Y. Kow, L. Angeline, and K. T. K. Teo, "Graph-Based Image Segmentation Using K-Means Clustering and Normalised Cuts," in The Fourth International Conference on Computational Intelligence, Communication Systems and Networks, 2012, pp. 307-312.
[26]. Y. C. Hu and M. G. Lee, "K-means-based color palette design scheme with the use of stable flags," Journal of Electronic Imaging, vol. 16, pp. 033003-1 to 033003-11, 2007.
[27]. S. N. Sulaiman and N. A. M. Isa, "Adaptive fuzzy-K-means clustering algorithm for image segmentation," IEEE Transactions on Consumer Electronics, vol. 56, pp. 2661-2668, 2010.
[28]. P. Ng and C.-M. Pun, "Skin Color Segmentation by Texture Feature Extraction and K-mean Clustering," in The Third International Conference on Computational Intelligence, Communication Systems and Networks, 2011, pp. 213-218.
[29]. R. Figueiredo, L. Schnitman, and F. d. Souza, "Using Neural Network and K-means Clustering for Image Segmentation in Outdoor Scenes," in The 2nd International Congress on University-Industry Cooperation, Perugia, Italy, 2007.
[30]. R. Huang, N. Sang, D. Luo, and Q. Tang, "Image Segmentation via Coherent Clustering in Lab Color Space," Pattern Recognition Letters, vol. 32, pp. 891-902, 2011.
[31]. جواد راستي، سيد اميرحسن منجمي و عباس وفايي، «كاهش رنگ تصاوير بيروني به هدف بخش‌بندي ابتدايي با استفاده از خوشه‌بندي سلسله‌مراتبي با حذف تدريجي در هرم گوسي»، ششمين کنفرانس ماشين بينايي و پردازش تصوير، دانشگاه اصفهان، آبان 1389.
[32]. A. Roy, S. K. Parui, D. Nandi, and U. Roy, "Color image segmentation using a semi-wrapped gaussian mixture model," in The 4th international conference on Pattern recognition and machine intelligence, Moscow, Russia, 2011, pp. 148-153.
[33]. M. Recky and F. Leberl, "Windows Detection Using K-means in CIE-Lab Color Space," in The 20th International Conference on Pattern Recognition, 2010, pp. 356-359.
[34]. S. Haykin, Neural Networks: A Comprehensive Foundation: Prentice Hall PTR, 1994.
[35] H. J. Aantonisse, "Image segmentation in pyramids," Computer Graphics and Image Processing vol. 19, pp. 367–383, 1982.
[36]. R. Marfil, L. Molina-Tanco, A. Bandera, J. A. Rodriguez, and F. Sandoval, "Pyramid segmentation algorithms revisited," Pattern Recognition, vol. 39, pp. 1430-1451, 2006.
[37]. G. Ramella and G. S. Baja, "Color Quantization by Multiresolution Analysis," in The 13th International Conference on Computer Analysis of Images and Patterns, Germany, 2009, pp. 525-532.
[38]. A. Atsalakis and N. Papamarkos, "Color reduction and estimation of the number of dominant colors by using a self-growing and self-organized neural gas," Engineering Applications of Artificial Intelligence, vol. 19, pp. 769-786, 2006.
[39]. S. Makrogiannis, G. Economou, and S. Fotopoulos, "A region dissimilarity relation that combines feature-space and spatial information for color image segmentation," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 35, pp. 44-53, 2005.
[40]. Y. J. Zhang, Advances in Image And Video Segmentation: IRM Press, 2006.
[41]. J. Rasti, A. Monadjemi, and A. Vafaei, "A Graph-Based Vision System for Automatic Object Detection in Outdoor Scenes," in The 22nd International DAAAM Symposium, Vienna, Austria, 2011, pp. 0167-0168.
[42]. A. Bosch, X. Munoz, and J. Freixenet, "Segmentation and description of natural outdoor scenes," Image and Vision Computing, vol. 25, pp. 727-740, 2007.
[43]. H. Zhang, J. E. Fritts, and S. A. Goldman, "A Co-Evaluation Framework for Improving Segmentation Evaluation," in SPIE Defense and Security Symposium - Signal Processing, Sensor Fusion, and Target Recognition XIV, 2005, pp. 420-430.
[44]. A. Alonso-Betanzos, B. Arcay-Varela, and A. Castro-Martínez, "Analysis and evaluation of hard and fuzzy clustering segmentation techniques in burned patient images," Image and Vision Computing, vol. 18, pp. 1045-1054, 2000.
[45]. D. Collins, W. A. Wright, and P. Greenway, "The sowerby image database," presented at the The 7th IEEE International Conference of Image Processing and Its Applications, Manchester, England, 1999.
[46]. X. He, R. S. Zemel, and M. Carreira-Perpi, "Multiscale conditional random fields for image labeling," in IEEE computer society conference on Computer vision and pattern recognition, Washington, D.C., USA, 2004, pp. 695-703.
[47]. A. Likas, M. Vlassis, and J. Verbeek, "The global k-means clustering algorithm," Pattern Recognition vol. 36, pp. 451-461, 2003.
[48]. جواد راستي، «ارائه يك روش بخش‌بندي مبتني بر الگوريتم‌هاي هوشمند به منظور تشخيص اشياء در تصاوير بيروني»، پايان‌نامه دکترا، گروه مهندسي کامپيوتر، دانشگاه اصفهان، 1391.
[49]. F. Y. Shih and S. Cheng, "Automatic seeded region growing for color image segmentation," Image and Vision Computing, vol. 23, pp. 877-886, 2005.
[50]. R. Datta, D. Joshi, J. Li, and J. Z. Wang, "Image retrieval: Ideas, influences, and trends of the new age," ACM Computing Surveys, vol. 40, pp. 1-60