Abstract
Image content analysis is important for automated image organization, labeling, and search. Partitioning an image into meaningful regions is one of the fundamental problems in image analysis. Anaglyph images and videos are more and more popular, such as in Flickr and YouTube. The anaglyph images provide disparity cue in a single image, which could be useful for image analysis. This paper exploits disparity cue for image partition. An image partition method for anaglyph is proposed. The disparity or depth cue is integrated with the traditional single-view image segmentation. A concept called dominant disparity is proposed, corresponding to each single-view image segment, which largely tolerates the disparity errors and image over-segmentations. A cue integration algorithm is developed. The integration is at the level of image segments rather than pixels, and object-level image segmentation is achieved. Experiments on both synthetic and real anaglyph images demonstrate the effectiveness of the proposed image partition method for anaglyph image analysis. To the best of our knowledge, our work is for the first time to perform anaglyph image partition.
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Forsyth D, Ponce J (2003) Computer vision: a modern approach. Prentice Hall, New Jersey
Zhu SC, Yuille AL (1996) Region competition: unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE PAMI 18(9):884–900
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE PAMI 22(8):888–905
Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE TPAMI 23(11):1222–1239
Delong A, Osokin A, Isack H, Boykov Y (2012) Fast approximate energy minimization with label costs. IJCV 96(1):1–27
Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916
Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59(2):167–181
Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE PAMI 24(5):603–619
De A, Guo C (2014) An image segmentation method based on the fusion of vector quantization and edge detection with applications to medical image processing. Int J Mach Learn Cybern 5(4):543–551
Ma J, Tian D, Gong M, Jiao L (2014) Fuzzy clustering with non-local information for image segmentation. Int J Mach Learn Cybern. doi:10.1007/s13042-014-0227-3
Zhang C, Wang L, Yang R (2010) Semantic segmentation of urban scenes using dense depth maps. In: Daniilidis K, Maragos P, Paragios N (eds) ECCV (4), volume 6314 of Lecture Notes in Computer Science. Springer, pp 708–721
Silberman N, Hoiem D, Kohli P, Fergus R (2012) Indoor segmentation and support inference from rgbd images. In: Fitzgibbon AW, Lazebnik S, Perona P, Sato Y, Schmid C (eds) ECCV (5), volume 7576 of Lecture Notes in Computer Science. Springer, pp 746–760
Zhang Q, Liu S, An P, Zhang Z (2009) Object segmentation based on disparity estimation. In: GEC ’09: Proceedings of the first ACM/SIGEVO summit on genetic and evolutionary computation. ACM, New York, pp 1053–1056
Chi L, Guo L, Yu L, Chen Y (2010) Object segmentation based on accurate disparity estimation. In: 2010 2nd international conference on information science and engineering (ICISE). IEEE, pp 4609–4612
Gu D, Zhao Y, Yuan Y, Hu G (2012) Human segmentation based on disparity map and grabcut. In: 2012 international conference on computer vision in remote sensing (CVRS). IEEE, pp 67–71
Rollmann W (1853) Notiz zur stereoskopie. Annalen der Physik 165(6):350–351
Ideses I, Yaroslavsky L (2005) Three methods that improve the visual quality of color anaglyphs. J Optics A Pure Appl Optics 7:755–762
Li S, Ma L, Ngi Ngan K (2013) Anaglyph image generation by matching color appearance attributes. Signal Process Image Commun 28(6):597C607
Lu Z, ur Rehman S, Sikandar Lal Khan M, Li H (2013) Anaglyph 3D stereoscopic visualization of 2D video based on fundamental matrix. In: Proceedings of 2013 international conference on virtual reality and visualization (ICVRV 2013). IEEE, pp 305–308
Matsuura F, Fujisawa N (2008) Anaglyph stereo visualization by the use of a single image and depth information. J Visual 11(1):79–86
Zingarelli MRU, Antonio de Andrade L, Goularte R (2012) Revglyph: a technique for reverting anaglyph stereoscopic videos. In: SAC 2012, pp 1005–1011
Joulin A, Kang SB (2013) Recovering stereo pairs from anaglyphs. In: CVPR. IEEE, pp 289–296
Gallagher A (2010) Detecting anaglyph images with channel alignment features. In: ICIP, pp 2985–2988
Hothersall D (2003) History of psychology. McGraw-Hill, New York
Kersten D, Yuille AL (2003) Bayesian models of object perception. Curr Opin Neurobiol 13:150–158
Scharstein D, Szeliski R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47:7–42
Klaus A, Sormann M, Karner K (2006) Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In: ICPR, pp 15–18
Bleyer M, Gelautz M (2005) Graph-based surface reconstruction from stereo pairs using image segmentation. SPIE Symp Electron Imaging (Videometrics VIII) 5665:288–299
Felzenszwalb PF, Huttenlocher DP (2004) Efficient belief propogation for early vision. In: CVPR, pp 261–268
Ladicky L et al (2010) What, where & how many? Combining object detectors and crfs’. In: ECCV, pp 424–437
Gould S, Fulton R, Koller D (2009) Decomposing a scene into geometric and semantically consistent regions. In: ICCV, pp 1–8
Hoiem D, Rother C, Winn J (2007) 3D layout CRF for multi-view object class recognition and segmentation. In: CVPR, pp 1–8
Shotton J, Winn J, Rother C, Criminisi A (2006) Textonboost: joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: ECCV, pp 1–15
Ladicky L et al (2010) Joint optimisation for object class segmentation and dense stereo reconstruction. In: IJCV 100(2):122–133
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The work is partially supported by the National Natural Science Foundation of China (No. 61202312), and a NSF CITeR award.
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Wu, Q., Guo, G. & Liang, J. A cue integration method for anaglyph image partition. Int. J. Mach. Learn. & Cyber. 7, 983–993 (2016). https://doi.org/10.1007/s13042-014-0304-7
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DOI: https://doi.org/10.1007/s13042-014-0304-7