دوره 16، شماره 2 - ( 6-1398 )                   جلد 16 شماره 2 صفحات 60-41 | برگشت به فهرست نسخه ها


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دانشکده مهندسی، دانشگاه فردوسی مشهد
چکیده:   (3752 مشاهده)
تبدیلات گوک (Wedgelet) و شیبک (Platelet) که پیش‌ازاین در خانواده موجک‌های هندسی وفقی برای بازنمایی تصاویر روشنایی مطرح شده‌اند، توانایی بازنمایی تنک تصاویر قطعه‌ای ثابت، و تصاویر قطعه‌ای خطی را دارند؛ اما کارایی آن‌ها در بازنمایی تصاویر قطعه‌ای غیرخطی مانند تصاویر عمق بهینه نیست. در این مقاله تبدیل صفحک[1] به‌عنوان عضو جدیدی از خانواده موجک‌های هندسی برای بازنمایی بهینه تصاویر عمق قطعه‌ای صفحه‌گون ارائه شده است. برخلاف موجک‌های هندسی پیشین که تنها از مدل‌های خطی و ثابت برای توصیف هر ناحیه هموار در تصویر استفاده می‌کنند، تبدیل صفحک برای تقریب هر ناحیه صفحه‌گون از یک مدل غیرخطی مبتنی بر توابع گویا بهره می‌گیرد. آزمایش‌ها بر روی تصاویر عمق واقعی نشان دادند که در نرخ بیت bpp 03/0 استفاده از کدگذار مبتنی بر صفحک در فشرده‌سازی تصاویر عمق نسبت به موجک هندسی گوک به‌طور میانگین تا dB 7/2 کیفیت را افزایش می‌دهد. همچنین در شرایط مشابه، در مقایسه با کدگذارهای مدرن JPEG2000 و H.264، استفاده از کدگذار عمق مبتنی بر صفحک به‌ترتیب منجر به dB 59/2 و dB 56/1 افزایش در کیفیت تصاویر بازسازی‌شده می‌شود.

[1] Planelet Transform
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نوع مطالعه: پژوهشي | موضوع مقاله: مقالات پردازش تصویر
دریافت: 1396/2/15 | پذیرش: 1397/10/19 | انتشار: 1398/6/26 | انتشار الکترونیک: 1398/6/26

فهرست منابع
1. [1] P. Henry, M. Krainin, E. Herbst, X. Ren, and D. Fox, "RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments," International Journal of Robotics Research, vol. 31, pp. 647-663, April 2012. [DOI:10.1177/0278364911434148]
2. [2] K. Khoshelham ,and S. Elberink, "Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications," Sensors, vol. 12, pp. 1437-1454, February 2012. [DOI:10.3390/s120201437] [PMID] [PMCID]
3. [3] Y. Wang, F. Zhong, Q. Peng, and X. Qin, "Depth map enhancement based on color and depth consistency," The Visual Computer, vol. 30, pp. 1157-1168, October 2014. [DOI:10.1007/s00371-013-0896-z]
4. [4] T. Whelan, H. Johannsson, M. Kaess, J. Leonard, and J. McDonald, "Robust real-time visual odometry for dense RGB-D mapping," in IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, 2013, pp. 5724-5731. [DOI:10.1109/ICRA.2013.6631400]
5. [5] م. پاک نژاد، م. رضائیان، «مدل‌سازی صفحه‌ای محیط‌های داخلی با استفاده از تصاویر RGB-D»، پردازش علائم و داده‌ها، دورة 14، شمارة 3، 1396.
6. [5] M. Paknezhad ,and M. Rezaeian, "Indoor Planar Modeling Using RGB-D Images," Signal and Data Processing, vol. 14, pp. 143-160, 2017. [DOI:10.29252/jsdp.14.3.143]
7. [6] J. Smisek, M. Jancosek, and T. Pajdla, "3D with Kinect," in IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain, 2011, pp. 1154-1160. [DOI:10.1109/ICCVW.2011.6130380]
8. [7] C. Mutto, P. Zanuttigh, and G. Cortelazzo, "Microsoft Kinect™ Range Camera," in Time-of-Flight Cameras and Microsoft Kinect™, C. Mutto, P. Zanuttigh, and G. Cortelazzo, Eds., ed: Springer US, 2012, pp. 33-47. [DOI:10.1007/978-1-4614-3807-6_3]
9. [8] J. Fu, D. Miao, W. Yu, S. Wang, Y. Lu, and S. Li, "Kinect-like depth data compression," IEEE Transactions on Multimedia, vol. 15, pp. 1340 - 1352, October 2013. [DOI:10.1109/TMM.2013.2247584]
10. [9] P. Merkle, Y. Morvan, A. Smolic, D. Farin, K. Müller, P. de With, et al., "The effects of multiview depth video compression on multiview rendering," Signal Processing: Image Communi-cation, vol. 24, pp. 73-88, January 2009. [DOI:10.1016/j.image.2008.10.010]
11. [10] K. Muller, P. Merkle, and T. Wiegand, "3-D Video Representation Using Depth Maps," Proceedings of the IEEE, vol. 99, pp. 643-656, April 2010. [DOI:10.1109/JPROC.2010.2091090]
12. [11] J. Ruiz-Hidalgo, J. Morros, P. Aflaki, F. Calderero, and F. Marqués, "Multiview depth coding based on combined color/depth segmentation," Journal of Visual Communication and Image Representation, vol. 23, pp. 42-52, January 2012. [DOI:10.1016/j.jvcir.2011.08.001]
13. [12] I. Daribo, H. Saito, R. Furukawa, S. Hiura, and N. Asada, "Effects of Wavelet-Based Depth Video Compression," in 3D-TV System with Depth-Image-Based Rendering, C. Zhu, Y. Zhao, L. Yu, and M. Tanimoto, Eds., ed: Springer New York, 2013, pp. 277-298. [DOI:10.1007/978-1-4419-9964-1_10]
14. [13] M. Maitre ,and M. Do, "Depth and depth-color coding using shape-adaptive wavelets," Journal of Visual Communication and Image Represen-tation, vol. 21, pp. 513-522, July-August 2010. [DOI:10.1016/j.jvcir.2010.03.005]
15. [14] D. Donoho, "Wedgelets: nearly minimax esti-mation of edges," Annals of Statistics, vol. 27, pp. 859-897, April 1999. [DOI:10.1214/aos/1018031261]
16. [15] A. Lisowska, Geometrical Multiresolution Adaptive Transforms - Theory and Applications, 1st ed.: Springer International Publishing, 2014. [DOI:10.1007/978-3-319-05011-9]
17. [16] J. Romberg, M. Wakin, and R. Baraniuk, "Multiscale wedgelet image analysis: fast decompositions and modeling," in International Conference on Image Processing, Rochester, New York, 2002, pp. 585-588.
18. [17] ح. باقرزاده، ا. هراتی، ز. امیری، و ر. کامیابی گل، «رفع نوفه ویدئو توسط تبدیل قیچک قطعه‌ای»، پردازش علائم و داده‌ها، دورة 15، شمارة 2، 1397.
19. [17] H. Bagherzadeh, A. Harati, Z. Amiri, and R. KamyabiGol, "Video Denoising Using block Shearlet Transform," Signal and Data Processing, vol. 15, pp. 17-30, 2018. [DOI:10.29252/jsdp.15.2.17]
20. [18] R. Willett and R. Nowak, "Platelets: a multiscale approach for recovering edges and surfaces in photon-limited medical imaging," IEEE Transactions on Medical Imaging, vol. 22, pp. 332-350, March 2003. [DOI:10.1109/TMI.2003.809622] [PMID]
21. [19] M. Dou, L. Guan, J. Frahm, and H. Fuchs, "Exploring High-Level Plane Primitives for Indoor 3D Reconstruction with a Hand-held RGB-D Camera," in Computer Vision - ACCV 2012 Workshops. vol. 7729, J.-I. Park and J. Kim, Eds., ed: Springer Berlin Heidelberg, 2013, pp. 94-108. [DOI:10.1007/978-3-642-37484-5_9]
22. [20] R. Kaushik and J. Xiao, "Accelerated patch-based planar clustering of noisy range images in indoor environments for robot mapping," Robotics and Autonomous Systems, vol. 60, pp. 584-598, April 2012. [DOI:10.1016/j.robot.2011.12.001]
23. [21] M. Brown, D. Burschka, and G. Hager, "Advances in computational stereo," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp. 993-1008, August 2003. [DOI:10.1109/TPAMI.2003.1217603]
24. [22] D. Scharstein and R. Szeliski, "A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms," International Journal of Computer Vision, vol. 47, pp. 7-42, April-June 2002.
25. [23] N. Thakoor, J. Sungying, and G. Jean, "Real-time Planar Surface Segmentation in Disparity Space," in IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, USA, 2007, pp. 1-8. [DOI:10.1109/CVPR.2007.383414]
26. [24] D. Sebai, F. Chaieb, K. Mammou, and F. Ghorbel, "Piece-wise linear function estimation for platelet-based depth maps coding using edge detection," in Proceedings of SPIE 8290, Three-Dimensional Image Processing (3DIP) and Applications II, Burlingame, California, United States, 2012, p. 82901C. [DOI:10.1117/12.909286]
27. [25] Z. Yu, W. Han, and Z. Jiying, "Depth map compression based on platelet coding and quadratic curve fitting," in IEEE 27th Canadian Conference on Electrical and Computer Engi-neering (CCECE), Halifax, Nova Scotia, Canada, 2014, pp. 1-4.
28. [26] I. Daribo, C. Tillier, and B. Pesquet-Popescu, "Adaptive wavelet coding of the depth map for stereoscopic view synthesis," in IEEE 10th Workshop on Multimedia Signal Processing, Cairns, Queensland, Australia, 2008, pp. 413-417. [DOI:10.1109/MMSP.2008.4665114]
29. [27] O. Kwan-Jung, Y. Sehoon, A. Vetro, and H. Yo-Sung, "Depth Reconstruction Filter and Down/Up Sampling for Depth Coding in 3-D Video," IEEE Signal Processing Letters, vol. 16, pp. 747-750, September 2009. [DOI:10.1109/LSP.2009.2024112]
30. [28] O. Kwan-Jung, A. Vetro, and H. Yo-Sung, "Depth Coding Using a Boundary Reconstruction Filter for 3-D Video Systems," IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, pp. 350-359, March 2011. [DOI:10.1109/TCSVT.2011.2116590]
31. [29] K. Min-Koo and H. Yo-Sung, "Depth Video Coding Using Adaptive Geometry Based Intra Prediction for 3-D Video Systems," IEEE Transactions on Multimedia, vol. 14, pp. 121-128, February 2012. [DOI:10.1109/TMM.2011.2169238]
32. [30] F. Pece, J. Kautz, and T. Weyrich, "Three Depth-Camera Technologies Compared," in 1st BEAMING Workshop, Barcelona, Spain, 2011, pp. 1-4.
33. [31] S. Mehrotra, Z. Zhengyou, C. Qin, Z. Cha, and P. Chou, "Low-complexity, near-lossless coding of depth maps from kinect-like depth cameras," in IEEE 13th International Workshop on Multimedia Signal Processing (MMSP), Saint-Malo, France, 2011, pp. 1-6. [DOI:10.1109/MMSP.2011.6093803]
34. [32] D. Sandberg, P. Forssen, and J. Ogniewski, "Model-Based Video Coding Using Colour and Depth Cameras," in International Conference on Digital Image Computing Techniques and Applications (DICTA), Noosa, QLD, Australia, 2011, pp. 158-163. [DOI:10.1109/DICTA.2011.33]
35. [33] D. Donoho and X. Huo, "Beamlets and Multiscale Image Analysis," in Multiscale and Multi-resolution Methods. vol. 20, T. Barth, T. Chan, and R. Haimes, Eds., ed: Springer Berlin Heidelberg, 2002, pp. 149-196. [DOI:10.1007/978-3-642-56205-1_3]
36. [34] A. Lisowska, "Second Order Wedgelets in Image Coding," in The International Conference on Computer as a Tool, Warsaw, Poland, 2007, pp. 237-244. [DOI:10.1109/EURCON.2007.4400239]
37. [35] A. Lisowska, "Moments-Based Fast Wedgelet Transform," Journal of Mathematical Imaging and Vision, vol. 39, pp. 180-192, February 2011. [DOI:10.1007/s10851-010-0233-3]
38. [36] A. Lisowska, "Smoothlet Transform: Theory and Applications," in Advances in Imaging and Electron Physics. vol. 178, W. H. Peter, Ed., ed: Elsevier, 2013, pp. 97-145. [DOI:10.1016/B978-0-12-407701-0.00002-9]
39. [37] F. Friedrich, L. Demaret, H. Führ, and K. Wicker, "Efficient Moment Computation over Polygonal Domains with an Application to Rapid Wedgelet Approximation," SIAM Journal on Scientific Computing, vol. 29, pp. 842-863, April 2007. [DOI:10.1137/050646597]
40. [38] V. Kiani, A. Harati, and A. Vahedian, "Iterative Wedgelet Transform: An efficient algorithm for computing wedgelet representation and approximation of images," Journal of Visual Communication and Image Representation, vol. 34, pp. 65-77, January 2016. [DOI:10.1016/j.jvcir.2015.10.009]
41. [39] V. Kiani, A. Harati, and A. Vahedian, "A relaxation approach to computation of second-order wedgelet transform with application to image compression," Signal Processing: Image Communication, vol. 41, pp. 115-127, February 2016. [DOI:10.1016/j.image.2015.12.005]
42. [40] R. Shukla, P. Dragotti, M. Do, and M. Vetterli, "Rate-distortion optimized tree-structured com-pression algorithms for piecewise polynomial images," IEEE Transactions on Image Pro-cessing, vol. 14, pp. 343-359, March 2005. [DOI:10.1109/TIP.2004.840710] [PMID]
43. [41] A. Handa, T. Whelan, J. McDonald, and A. Davison, "A Benchmark for RGB-D Visual Odometry, 3D Reconstruction and SLAM," in IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, 2014, pp. 1524-1531. [DOI:10.1109/ICRA.2014.6907054]
44. [42] K. Lai, L. Bo, and D. Fox, "Unsupervised feature learning for 3D scene labeling," in IEEE Inter-national Conference on Robotics and Automation (ICRA), Hong Kong, China, 2014, pp. 3050-3057. [DOI:10.1109/ICRA.2014.6907298]
45. [43] W. Zhou, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, pp. 600-612, 2004. [DOI:10.1109/TIP.2003.819861] [PMID]

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