结合深度信息的图像分割算法研究
|
摘要
计算机视觉的一个长远目标是通过视觉图像来理解世界。随着移动智能终端设备的普及和网络技术的发展,视觉信息大量涌现。如何通过计算机视觉技术实现对视觉信息的分析,进而有效地对其进行组织、表达、管理和检索,成为当今科研领域和工业界亟待解决的问题。其中,图像分割技术受到越来越多的关注,被认为是实现图像理解的一种有效途径。
早期的图像分割技术是利用图像的颜色外观特征来完成的。图像被分割为外观特征一致的一些区域。然而,在现实世界中,物体不是仅由颜色外观特征的一致性来定义的,而是在本质上由物体在三维世界中的物理连通性所决定。
基于颜色外观特征聚类的图像分割技术通常无法得到语义一致的图像分割结果。这促使人们寻找新的图像分割技术。传统图像分割技术将图像作为二维模式来处理,然而,图像是三维世界在二维摄影平面的投影。因此,有必要结合场景深度信息来指导图像分割。这将为得到具有语义意义的图像分割结果提供一种可能的解决方案。
在上述研究背景下,本文针对结合场景深度信息的图像分割问题展开研究,提出了一系列新颖的图像分割算法,有效地解决了基于颜色外观特征的图像分割算法面临的过分割和欠分割问题。为图像理解领域的发展奠定了一定的基础。
本文的创新点和主要贡献如下：
1.传统的基于颜色特征的图像分割算法没有充分利用物体在三维世界中的分段连通性,从而存在所谓的过分割和欠分割问题。为此,本文提出了一种基于深度不连续性选取种子区域的图像分割算法：首先结合图像的深度和颜色信息抽取可靠的、与物体的真实边缘相一致的深度不连续边缘,然后完成颜色过分割处理,并在不连续边缘附近选取种子区域,最后利用图割(Graph Cut)优化算法为未贴标签的过分割区域分配标签。本文所提出的图像分割算法既能有效分离深度上不连续的三维“悬浮”物体,也能成功分离相互之间有接触(即有支撑和被支撑关系)的两个三维“非悬浮”物体,较好地解决了图像的语义分割问题。
2.深度不连续作为三维世界物体之间遮挡的线索,越来越多地被应用于图像分割。但是,以往的基于深度图的图像分割算法往往局限于将整个场景分割为深度一致的区域,而在三维世界中,相互之间具有支撑与被支撑关系的两个物体在接触处深度是连续的。这使得基于深度图的图像分割算法无法将支撑物与被支撑物分开。为此,本文提出了基于物体间支撑分析的图像分割方法：首先结合场景的深度图对物体间存在的支撑关系进行分析,将场景中表面法向量近似垂直于水平面的区域作为支撑区域。在此基础上,利用深度信息分别在支撑区域和非支撑区域完成图像分割,从而将相互间有接触的两个物体分割开。
3.场景的几何结构信息作为对场景理解和物体识别的有效途径,近年来得到广泛研究。但是,以往的场景几何结构分析往往局限于使用颜色外观特征作为线索,据此将场景近似分为地面区域、垂直区域以及天空等一些区域。以往的方法由于忽略了场景深度特征与几何结构之间存在的强相关性,导致几何结构分类结果不一定符合场景本身所包含的语义结构。为此,本文提出结合深度信息和颜色外观特征的场景几何结构分析方法。实验结果表明,本文方法显著提高了对场景几何结构分类的精确度,从而提升了图像分割的整体效果。
图像分割技术涉及到计算机视觉、机器学习以及认知科学等多个交叉学科,希望本文的研究工作能够为相关领域的研究提供一些帮助。
One of the long-term goals of computer vision is to be able to understand the world through visual images. With the popularity of mobile intelligent terminal equipments and advance in networks, large-scale visual data become available to ordinary users. How to analyze these visual data and then how to effectively organize, represent, manage and retrieve these visual data become a challenging task in both research and industry. To achieve this goal, image segmentation which is considered to be an effective way to achieve image understanding has attracted more and more attention.
In its early days, image segmentation technique is completed based on the image appearance feature clustering. An image is segmented to homogeneous regions in appearance. However, in the real world, objects are typically defined, not by homogeneity in appearance, but by physical connectedness.
It is hard to get semantic consistency object segmentation for image segmentation techniques based on appearance clustering. This has prompted people to look for new image segmentation techniques. Traditional image segmentation techniques analyzed the image as a2D pattern rather than treated it as a projection from3D world. We believe it is necessary to use depth information to guide image segmentation. The using of depth information will provide a possible solution for image segmentation.
In this thesis, we consider how to perform image segmentation combining depth cue and color cue. Several image segmentation methods combining depth information and color information are proposed. The proposed methods can get semantic consistency segmentation and overcome over-segmentation or under-segmentation in traditional2D image segmentation methods based on appearance features. The main contributions are illustrated as follows:
1. Appearance based image segmentation methods ignore the physical connectedness which makes it hard to avoid over-segmentation and under-segmentation for them. We propose a novel image segmentation method in which seed regions are selected based on depth discontinuities. First, we combine depth and color cues to extract depth discontinuity boundaries which correspond to object boundaries. Then over segmentation of color image is performed and color segments neighbor to the depth discontinuity boundaries in each side are selected as seed regions for different objects. Graph cut is used to optimize the energy for labeling the remaining segments which are not selected as seed regions at last. Compared to conventional methods, our approach can effectively separate different objects with discontinuous boundaries. At the same time, our method can also separate two objects contact each other (i.e. support and supported relationship). Semantic image segmentation is solved successfully.
2. Depth discontinuity which corresponds to occlusion in3D world is widly used in image segmentation. Most existed image segmentation methods based on depth map segmented a scene to homogeneous regions in depth. These methods ignored the depth continuous at the connections between support and supported objects and these methods couldn't separate support and supported objects apart. In this thesis, we proposed a support analysis based image segmentation method. We use depth map to analyze the support relation between different objects in a scene. In color over-segmentation, segments with surface normals approximately orthogonal to horizontal plane are set as support regions, the remaining segments are set as supported regions. Then we perform depth segmentation in support and supported regions, separately. Our method can separate support and supported objects apart successfully.
3. Geometric structure has been widely studied as useful information in scene understanding and object recognition. Geometric classes describe the3D orientation of an image region with respect to the camera. Traditional methods only used appearance features to estimate geometric structure (currently horizontal, vertical, or sky). These methods ignored the correlation between geometric classes and depth features and made inconsistent geometric structure estimation. We proposed geometric structure estimation combining depth and appearance features. Our experiments demonstrate the incorporation of depth features allows us to achieve state-of-the-art classification results than previous works. In addition, we also improve the image segmentation using estimated geometric structure.
Image segmentation is closely related with many different domains, such as computer vision, machine learning and cognitive science. We hope that our work can be helpful for relating researches.
早期的图像分割技术是利用图像的颜色外观特征来完成的。图像被分割为外观特征一致的一些区域。然而,在现实世界中,物体不是仅由颜色外观特征的一致性来定义的,而是在本质上由物体在三维世界中的物理连通性所决定。
基于颜色外观特征聚类的图像分割技术通常无法得到语义一致的图像分割结果。这促使人们寻找新的图像分割技术。传统图像分割技术将图像作为二维模式来处理,然而,图像是三维世界在二维摄影平面的投影。因此,有必要结合场景深度信息来指导图像分割。这将为得到具有语义意义的图像分割结果提供一种可能的解决方案。
在上述研究背景下,本文针对结合场景深度信息的图像分割问题展开研究,提出了一系列新颖的图像分割算法,有效地解决了基于颜色外观特征的图像分割算法面临的过分割和欠分割问题。为图像理解领域的发展奠定了一定的基础。
本文的创新点和主要贡献如下：
1.传统的基于颜色特征的图像分割算法没有充分利用物体在三维世界中的分段连通性,从而存在所谓的过分割和欠分割问题。为此,本文提出了一种基于深度不连续性选取种子区域的图像分割算法：首先结合图像的深度和颜色信息抽取可靠的、与物体的真实边缘相一致的深度不连续边缘,然后完成颜色过分割处理,并在不连续边缘附近选取种子区域,最后利用图割(Graph Cut)优化算法为未贴标签的过分割区域分配标签。本文所提出的图像分割算法既能有效分离深度上不连续的三维“悬浮”物体,也能成功分离相互之间有接触(即有支撑和被支撑关系)的两个三维“非悬浮”物体,较好地解决了图像的语义分割问题。
2.深度不连续作为三维世界物体之间遮挡的线索,越来越多地被应用于图像分割。但是,以往的基于深度图的图像分割算法往往局限于将整个场景分割为深度一致的区域,而在三维世界中,相互之间具有支撑与被支撑关系的两个物体在接触处深度是连续的。这使得基于深度图的图像分割算法无法将支撑物与被支撑物分开。为此,本文提出了基于物体间支撑分析的图像分割方法：首先结合场景的深度图对物体间存在的支撑关系进行分析,将场景中表面法向量近似垂直于水平面的区域作为支撑区域。在此基础上,利用深度信息分别在支撑区域和非支撑区域完成图像分割,从而将相互间有接触的两个物体分割开。
3.场景的几何结构信息作为对场景理解和物体识别的有效途径,近年来得到广泛研究。但是,以往的场景几何结构分析往往局限于使用颜色外观特征作为线索,据此将场景近似分为地面区域、垂直区域以及天空等一些区域。以往的方法由于忽略了场景深度特征与几何结构之间存在的强相关性,导致几何结构分类结果不一定符合场景本身所包含的语义结构。为此,本文提出结合深度信息和颜色外观特征的场景几何结构分析方法。实验结果表明,本文方法显著提高了对场景几何结构分类的精确度,从而提升了图像分割的整体效果。
图像分割技术涉及到计算机视觉、机器学习以及认知科学等多个交叉学科,希望本文的研究工作能够为相关领域的研究提供一些帮助。
One of the long-term goals of computer vision is to be able to understand the world through visual images. With the popularity of mobile intelligent terminal equipments and advance in networks, large-scale visual data become available to ordinary users. How to analyze these visual data and then how to effectively organize, represent, manage and retrieve these visual data become a challenging task in both research and industry. To achieve this goal, image segmentation which is considered to be an effective way to achieve image understanding has attracted more and more attention.
In its early days, image segmentation technique is completed based on the image appearance feature clustering. An image is segmented to homogeneous regions in appearance. However, in the real world, objects are typically defined, not by homogeneity in appearance, but by physical connectedness.
It is hard to get semantic consistency object segmentation for image segmentation techniques based on appearance clustering. This has prompted people to look for new image segmentation techniques. Traditional image segmentation techniques analyzed the image as a2D pattern rather than treated it as a projection from3D world. We believe it is necessary to use depth information to guide image segmentation. The using of depth information will provide a possible solution for image segmentation.
In this thesis, we consider how to perform image segmentation combining depth cue and color cue. Several image segmentation methods combining depth information and color information are proposed. The proposed methods can get semantic consistency segmentation and overcome over-segmentation or under-segmentation in traditional2D image segmentation methods based on appearance features. The main contributions are illustrated as follows:
1. Appearance based image segmentation methods ignore the physical connectedness which makes it hard to avoid over-segmentation and under-segmentation for them. We propose a novel image segmentation method in which seed regions are selected based on depth discontinuities. First, we combine depth and color cues to extract depth discontinuity boundaries which correspond to object boundaries. Then over segmentation of color image is performed and color segments neighbor to the depth discontinuity boundaries in each side are selected as seed regions for different objects. Graph cut is used to optimize the energy for labeling the remaining segments which are not selected as seed regions at last. Compared to conventional methods, our approach can effectively separate different objects with discontinuous boundaries. At the same time, our method can also separate two objects contact each other (i.e. support and supported relationship). Semantic image segmentation is solved successfully.
2. Depth discontinuity which corresponds to occlusion in3D world is widly used in image segmentation. Most existed image segmentation methods based on depth map segmented a scene to homogeneous regions in depth. These methods ignored the depth continuous at the connections between support and supported objects and these methods couldn't separate support and supported objects apart. In this thesis, we proposed a support analysis based image segmentation method. We use depth map to analyze the support relation between different objects in a scene. In color over-segmentation, segments with surface normals approximately orthogonal to horizontal plane are set as support regions, the remaining segments are set as supported regions. Then we perform depth segmentation in support and supported regions, separately. Our method can separate support and supported objects apart successfully.
3. Geometric structure has been widely studied as useful information in scene understanding and object recognition. Geometric classes describe the3D orientation of an image region with respect to the camera. Traditional methods only used appearance features to estimate geometric structure (currently horizontal, vertical, or sky). These methods ignored the correlation between geometric classes and depth features and made inconsistent geometric structure estimation. We proposed geometric structure estimation combining depth and appearance features. Our experiments demonstrate the incorporation of depth features allows us to achieve state-of-the-art classification results than previous works. In addition, we also improve the image segmentation using estimated geometric structure.
Image segmentation is closely related with many different domains, such as computer vision, machine learning and cognitive science. We hope that our work can be helpful for relating researches.
引文
[1]S. Russell and P. Norvig, "Artificial Intelligence:A Modern Approach," Prentice Hall,2010.
[2]D.G. Treicher, Are you missing the boat in training aid? Film and AV Communication 1 (1967) 14-16.
[3]A. Saxena, L. Wong, M. Quigley, and A. Y. Ng, "A vision-based system for grasping novel objects in cluttered environments," In International Symposium on Robotics Research (ISRR),2007.
[4]D. Thirde, M. Borg, J. Ferryman, F. Fusier, V. Valentin, F. Bremond, and M. Thon-nat, "A real-time scene understanding system for airport apron monitoring," In Proc. of the International Conference on Computer Vision Systems (ICCVS),2006.
[5]D. Mirota, R. H. Taylor, M. Ishii, and G. D. Hager. Direct endoscopic video reg-istration for sinus surgery. In Medical Imaging 2009:Visualization, Image-guided Procedures and Modeling. Proceedings of the SPIE,2009.
[6]D. Ritendra, J. Dhiraj, Li Jia, and Wang, James Z, "Image retrieval:Ideas, influences, and trends of the new age," ACM Computing Surveys (CSUR), 40(2),2008.
[7]Lowe, D. G, "Distinctive image features from scale-invariant keypoints," Springer,60:91-110,2004.
[8]L. Fei-Fei and P. Perona, "A Bayesian Hierarchical Model for Learning Natural Scene Categories," Proc. of IEEE Computer Vision and Pattern Recognition, pp. 524-531,2005
[9]L. Fei-Fei, R. Fergus, and A. Torralba. "Recognizing and Learning Object Categories," CVPR short course,2007.
[10]G. Qiu, "Indexing chromatic and achromatic patterns for content-based colour image retrieval," Pattern Recognition, vol.35, pp.1675-1686,2002.
[11]Z. Tu, X. Chen, Y. Alan, and S. Zhu, "Image parsing:Unifying segmentation, detection, and recognition," International Journal of Computer Vision 63(2): 113-140,2005.
[12]L. Bastian, L. Ales, and S. Bernt, "An implicit shape model for combined object categorization and segmentation", Springer,2006.
[13]J. Winn, N. Joijic, "Locus:Learning object classes with unsupervised segmentation," In Proceedings of the IEEE International Conference on Computer Vision, Beijing,2005.
[14]G. Humphrey, "The psychology of the gestalt. Journal of Educational Psychology," 15(7),401-412,1924.
[15]R. Shapley, N. Rubin, and D. Ringach, "Visual segmentation and illusory contours." The visual neurosciences:1119-1128, MIT,2004.
[16]R. V. D. HEYDT, "Image parsing mechanisms of the visual cortex," The visual neurosciences:1139-1150, MIT,2004.
[17]N. R. Pal and S. K. Pal, "A review on image segmentation techniques." Pattern Recognition 26(9):1277-1294,1993.
[18]H. Zhang, J. E. Fritts, and S. A. Goldman, "Image segmentation evaluation:A survey of unsupervised methods," Computer Vision and Image Understanding 110(2):260-280,2008.
[19]D. Marr, Vision:A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman and Company, New York, 1996.
[20]L. G. Shapiro and G. C. Stockman, "Computer Vision", pp.279-325, New Jersey, Prentice-Hall,2001.
[21]Stan Z. Li, "Markov Random Field Modeling in Image Analysis," springer, 2001.
[22]J. M. Morel and S. Solimini, "Variational methods in image segmentation," Birkhauser Boston Inc,1995.
[23]D. Mumford and J. Shah, "Optimal approximations by piecewise smooth functions and associated variational problems," Communications on pure and applied mathematics 42(5), pp.577-685,1989.
[24]D. Cremers, M. Rousson, and R. Deriche, "A Review of Statistical Approaches to Level Set Segmentation:Integrating Color, Texture, Motion and Shape," International Journal of Computer Vision vol.72(2), pp.195-215,2007.
[25]D. Comaniciu, and P. Meer, "Mean shift:a robust approach toward feature space analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):603-619,2002.
[26]S. Belongie, C. Carson, H. Greenspan, and J. Malik, "Color-and texture-based image segmentation using EM and its application to content-based image retrieval," cvpr, pp.657-682,1998.
[27]A. Y. Yang, J. Wright, and Y. Ma, S. Sastry, "Unsupervised segmentation of natural images via lossy data compression," CVIU, vol.110(2), pp.212-225, 2008.
[28]P. Felzenszwalb and D. Huttenlocher, "Efficient Graph-Based Image Segmentation," International Journal of Computer Vision, vol.59(2), pp.167-181,2004.
[29]J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22(8), pp. 888-905,2000.
[30]J. Malik, S. Belongie, T. Leung, and J. Shi, "Contour and texture analysis for image segmentation," International Journal of Computer Vision, vol.43(1), pp.7-27,2001.
[31]D. A. Tolliver and G. L. Miller, "Graph partitioning by spectral rounding: Applications in image segmentation and clustering," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.1053-1060,2006.
[32]T. Cour, F. Benezit, and J. Shi, "Spectral segmentation with multiscale graph decomposition," IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005.
[33]J. Canny, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.8(6), pp.679-698,1986.
[34]P. Perona and J. Malik, "Detecting and localizing edges composed of steps, peaks and roofs," Third International Conference on Computer Vision,1990.
[35]D. R. Martin, C. C. Fowlkes, and J. Malik, "Learning to detect natural image boundaries using local brightness, color, and texture cues," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26(5), pp.530-549,2004.
[36]P. Felzenszwalb and D. McAllester, "A min-cover approach for finding salient curves," IEEE Conference on Computer Vision and Pattern Recognition Workshop,2006.
[37]X. Ren, C. C. Fowlkes, and J. Malik, "Scale-invariant contour completion using conditional random fields," Tenth IEEE International Conference on Computer Vision,2005.
[38]P. Dollar, Z. Tu, and S. Belongie, "Supervised learning of edges and object boundaries," IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2006.
[39]Q. Zhu, G. Song, and J. Shi, "Untangling cycles for contour grouping," IEEE 11th International Conference on Computer Vision,2007.
[40]J. Mairal, M. Leordeanu, F. Bach, M. Hebert and J. Ponce, "Discriminative sparse image models for class-specific edge detection and image interpretation," ECCV, Springer, pp.43-56,2008.
[41]X. Ren, "Multi-scale improves boundary detection in natural images," ECCV 2008.
[42]J. Malik, S. Belongie, T. Leung, and J. shi, "Contour and texture analysis for image segmentation," International Journal of Computer Vision vol.43(1), pp. 7-27.
[43]P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, "From contours to regions:An empirical evaluation," Computer Vision and Pattern Recognition,2009.
[44]M. P. Kumar, P. H. S. Ton, and A. Zisserman, "OBJ CUT," IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005.
[45]J. Shotton, A. Blake, and R. Cipolla, "Contour-based learning for object detection," Tenth IEEE International Conference on Computer Vision,2005.
[46]J. Shotton, J. Winn, C. Rother, and A. Criminisi, "TextonBoost:Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation," ECCV,2006.
[47]J. Winn and J. Shotton, "The Layout Consistent Random Field for Recognizing and Segmenting Partially ccluded Objects," CVPR,2006.
[48]J. Shotton, M. Johnson, and R. Cipolla, "Semantic Texton Forests for Image Categorization and Segmentation," CVPR,2008.
[49]S. Gould, R. Fulton, and D. Koller, "Decomposing a scene into geometric and semantically consistent regions," IEEE 12th International Conference on Computer Vision,2009.
[50]Florian Schroff, "Semantic Image Segmentation and Web-Supervised Visual Learning," Doctor of Philosophy Thesis, University of Oxford,2009.
[51]C. Rother, V. Kolmogorov, and A. Blake, "Grabcut:Interactive foreground extraction using iterated graph cuts," ACM SIGGRAPH,2004.
[52]V. Lempitsky, P. Kohli, C. Rother, and T. Sharp, "Image Segmentation with A Bounding Box Prior," ICCV,2009.
[53]J. Wang, and M. F. Cohen, "An iterative optimization approach for unified image segmentation and matting," Tenth IEEE International Conference on Computer Vision,2005.
[54]A. Levin, D. Lischinski, and Y. Weiss, "A Closed-Form Solution to Natural Image Matting," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30(2) pp.228-242,2008.
[55]V. Kolmogorov and R. Zabin, "What energy functions can be minimized via graph cuts?" IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26(2) pp.147-159,2004.
[56]Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts." IEEE Transactions on Pattern Analysis and Machine Intelligence:1222-1239,2001.
[57]Boykov, Y. and O. Veksler, "Graph cuts in vision and graphics:Theories and applications," Handbook of mathematical models in computer vision, Springer, pp.79-96,2006.
[58]F. Estrada, A. Jepson, and C. Chennubhotla, "Spectral embedding and min-cut for image segmentation," British Machine Vision Conference,2004.
[59]A. Hoover, G. Jean-Baptiste, X. Jiang, P. J. Flynn, H. Bunke, D. B. Goldgof, K. Bowyer, D. W. Eggert, A. Fitzgibbon, and R. B. Fisher, "An experimental comparison of range image segmentation algorithms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18(7), pp.673-689,1996.
[60]M. W. Powell, K. W. Bowyer, X. Jiang, and H. Bunke, "Comparing curved-surface range image segmenters," IEEE Sixth International Conference on Computer Vision,1998.
[61]X. Wang and H. Wang, "Markov random field modeled range image segmentation," Pattern Recognition Letters, vol.25(3), pp.367-375,2004.
[62]N. Yokoya and M. D. Levine, "Range image segmentation based on differential geometry:A hybrid approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11(6), pp.643-649,1989.
[63]Y. Ding, X. Ping, M. Hu, and D. Wang, "Range image segmentation based on randomized hough transform," Pattern Recognition Letters, vol.26(13), pp. 2033-2041,2005.
[64]A. Bab-Hadiashar and D. Suter, "Robust range segmentation," IEEE Fourteenth International Conference on Pattern Recognition,1998.
[65]H. Feng, Z. Tu, and S. Zhu, "Range image segmentation by an effective jump-diffusion method," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26(9), pp.1138-1153,2004.
[66]A. D. Sappa and M. Devy, "Fast range image segmentation by an edge detection strategy," Third International Conference on 3-D Digital Imaging and Modeling, 2001.
[67]D. Han, B. Lee, J. Cho, and D. Hwang, "Real-time object segmentation using disparity map of stereo matching," Applied Mathematics and Computation, vol. 205(2), pp.770-777,2008.
[68]A. Gupta, "Range image segmenation for 3-d object recognition," MS-CIS, Univerity of Pennsylvania,1988.
[69]T.-J. Fan, G. Medioni, and R. Nevatial, "Recognizing 3-D objects using surface descriptions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11(11), pp.1140-1157,1989.
[70]A. Guzman-Arenas and A. Guzmaan, "Computer recognition of three-dimensional objects in a visual scene," Massachusetts Institute of Technology,1968.
[71]J. B. Paul and C. J. Ramesh, "Three-dimensional object recognition," Computing Surveys, Vol.17, No.1, March 1985.
[72]G. Gordon, T. Darrell, M. Harville, and J. Woodfill, "Background estimation and removal based on range and color," IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1999.
[73]V. Kolmogorov, A. Criminisi, A. Blake, G. Cross, and C. Rother, "Probabilistic Fusion of Stereo with Color and Contrast for Bi-Layer Segmentation," International Journal of Computer Vision, vol.76(2), pp.107-107,2008.
[74]N. D. Doulamis, A. D. Doulamis, Y. S. Avrithis, K. S. Ntalianis, and S. D. Kollias, "Efficient summarization of stereoscopic video sequences, " IEEE Transactions on Circuits and Systems for Video Technology, vol.10(4), pp. 501-517,2000.
[75]A. D. Doulamis, N. D. Doulamis, K. S. Ntalianis, and S. D. Kollias, "Unsupervised semantic object segmentation of stereoscopic video sequences," IEEE INT. CONF. ON INTELLIGENCE, INFORMATION AND SYSTEMS, 1999.
[76]C. Cigla and A. Aydin Alatan, "Depth Assisted Object Segmentation in Multi-View Video," 3DTV Conference:The True Vision-Capture, Transmission and Display of 3D Video,2008.
[77]C. Zhang, L. Wang, and R. Yang, "Semantic segmentation of urban scenes using dense depth maps," ECCV, Springer, pp.708-721,2010.
[78]N. Silberman and R. Fergus, "Indoor scene segmentation using a structured light sensor," IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona,2011.
[79]X. Ren, I. P. Computing, L. Bo, and D. Fox, "RGB-(D) Scene Labeling: Features and Algorithms," IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2012.
[80]K. Fukunaga and L. Hostetler, "The estimation of the gradient of a density function, with applications in pattern recognition," IEEE Transactions on Information Theory, vol.21(1), pp.32-40,1975.
[81]Y. Cheng, "Mean shift, mode seeking, and clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.17(8), pp.790-799,1995.
[82]R. O. Duda and P. E. Hart, "Pattern classification and scene analysis," Wiley, 1973.
[83]M. Tabb and N. Ahuja, "Multiscale image segmentation by integrated edge and region detection," IEEE Transactions on Image Processing, vol.6(5), pp. 642-655,1997.
[84]T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, "Introduction to algorithms," The MIT press,2009.
[85]Z. Wu and R. Leahy, "An optimal graph theoretic approach to data clustering: theory and its application to image segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15(11), pp.1101-1113,1993.
[86]G. H. Golub and C. F. Van Loan, "Matrix computations," John Hopkins Press, 1989.
[87]M. R. Everingham, H. Muller, and B. Thomas, "Evaluating image segmentation algorithms using the pareto front," In European Conference on Computer Vision (ECCV), volume 4, pp.34-48,2002.
[88]C. W. Shaffrey, I. H. Jermyn, and N. G. Kingsbury, "Psychovisual evaluation of image segmentation algorithms," In Advanced Concepts for Intelligent Vision Systems,2002.
[89]F. J. Estrada and A. D. Jepson, "Quantitative evaluation of a novel image segmentation algorithm," CVPR,2005.
[90]J. Freixenet, X. Munoz, D. Raba, J. Marti, and X. Cuff. "Yet another survey on image segmentation:Region and boundary information integration," In European Conference on Computer Vision (ECCV), pp.408-422,2002.
[91]Q. Huang and B. Dom, "Quantitative methods of evaluating image segmentation," In IEEE Intl. Conf. on Image Processing (ICIP), pp.53-56, 1995.
[92]D. Martin, "An Empirical Approach to Grouping and Segmentation," PhD thesis, Univ. of California, Berkeley,2002.
[93]H. I. Christensen and P. J. Phillips, "Empirical Evaluation methods in Computer Vision," World Scientific Publishing Company, July 2002.
[94]T. Malisiewicz and A. A. Efros, "Improving spatial support for objects via multiple segmentations," In BMVC, September 2007.
[95]D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics," In ICCV,2001.
[96]C. Harris and M. Stephens, "A combined corner and edge detector," Proceedings of the 4th AIvey Vision Conference, pp.147-15],1988.
[97]K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir and L. Van Gool, "A comparison of affine region detectors," In IJCV, vol.65(1/2), pp. 43-72,2005.
[98]H. Bay, A. Ess, T. Tuytelaars, and Luc Van Gool, "SURF:Speeded Up Robust Features," Computer Vision and Image Understanding (CVIU), Vol.110, No.3, pp.346-359,2008.
[99]D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol.60(2), pp.91-110,2004.
[100]J. Sivic and A. Zisserman, "Video Google:a text retrieval approach to object matching in videos," Ninth IEEE International Conference on Computer Vision,2003.
[101]Y. Rubner, C. Tomasi, L. Guibas, "A metric for distributions with applications to image databases," Sixth International Conference on Computer Vision,1998.
[102]F. L. Hitchcock, "The distribution of a product from several sources to numerous localities," J. Math. Phys., vol.20, pp.224-230,1941.
[103]J. A. Bilmes, "A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models," Technical Report TR-97-021, International Computer Science Institute and Computer Science Division, University of California at Berkeley, April 1998.
[104]F. Jurie and B. Triggs, "Creating efficient codebooks for visual recognition," In Proceedings of the 10th International Conference on Computer Vision, Beijing, China, vol.1, pp. 604-610,2005.
[105]B. Leibe and B. Schiele, "Interleaved object categorization and segmentation," In Proceedings of the 14th British Machine Vision Conference, Norwich, vol.2, pp.264-271, 2003.
[106]T. Leung and J. Malik, "Recognizing surfaces using three-dimensional textons," In Proceedings of the 7th International Conference on Computer Vision, Kerkyra, Greece, pp. 1010-1017,1999.
[107]M. Varma and A. Zisserman, "Texture classification:Are filter banks necessary?" In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, vol.2, pp.691-698, June 2003.
[108]J. Winn, Criminisi, A., and T. Minka, "Object Categorization by Learned Universal Visual Dictionary," In Proceedings of the 10th International Conference on Computer Vision, Beijing, China,2005.
[109]C. P. Papageorgiou, M. Oren, and T. Poggio, "A general framework for object detection," In Proceedings of the 6th International Conference on Computer Vision, Bombay, India, pp. 555, Washington, DC, USA,1998.
[110]P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, pp.511-518,2001.
[111]N. Dalal and B Triggs, "Histogram of Oriented Gradients for Human Detection," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, vol.2, pp.886-893,2005.
[112]M. Varma and A. Zisserman, "Classifying materials from images:to cluster or not to cluster?" In Proceedings of the 2nd International Workshop on Texture Analysis and Synthesis, Copenhagen, Denmark, pp.139-144, May 2002.
[113]M. A. Fischler and R. C. Bolles, "Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, vol.24(6), pp.381-395,1981.
[114]D. Scharstein and R. Szelisk, "A taxonomy and evaluation of dense two-frame stereo correspondence algorithms," International Journal of Computer Vision, vol.47(1), pp.7-42, 2002.
[115]S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, "A comparison and evaluation of multi-view stereo reconstruction algorithms," Computer Vision and Pattern Recognition,2006.
[116]A. Saxena, S. Chung, and A. Ng, "Learning depth from single monocular images," Advances in Neural Information Processing Systems,2006.
[117]A. Saxena, S. Chung, and A. Ng, "3-d depth reconstruction from a single still image," International Journal of Computer Vision, vol.76(1), pp.53-69,2008.
[118]J. Shotton, T. Sharp, A. Kipman, A. Fitzgibbon, M. Finocchio, A. Blake, M. Cook, and R. Moore, "Real-time human pose recognition in parts from single depth images," Communications of the ACM, vol.56(1), pp.116-124,2013.
[119]G. Fanelli, J. Gall, and L. Van Gool, "Real time head pose estimation with random regression forests," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
[120]D. A. Forsyth and J. Ponce, "Computer vision:a modern approach," Person Education, 2012.
[121]C. Carson, S. Belongie, H. Greenspan, and J. Malik, "Blobworld:Image Segmentaion Using Expectation-Maximization and Its Application to Image Querying," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24(8), pp.1026-1038, 2002.
[122]Z. Wang, Z. Zheng, "A Region Based Stereo Matching Algorithm Using Cooperative Optimization," IEEE Conference on Computer Vision and Pattern Recognition,2008.
[123]D. Greig, B. Porteous, and A. Seheult, "Exact maximum a posteriori estimation for binary images," Journal of the Royal Statistical Society, Series B, Vol.51(2), pp.271-279,1989.
[124]Y. Boykov and O. Veksler, "Graph cuts in vision and graphics:Theories and applications. Handbook of mathematical models in computer vision," Springer, pp.79-96,2006.
[125]L. Ford and D. Fulkerson, "Flows in Networks," Princeton University Press,1962.
[126]W. Cook, W. Cunningham, W. Pulleyblank, and A. Schrijver, "Combinatorial Optimization," John Wiley & Sons,1998.
[127]M. Grundmann, V. Kwatra, M. Han, and I. Essa, "Efficient Hierarchical Graph-Based Video Segmentation," Computer Vision and Pattern Recognition,2010.
[128]D. Hoiem, A. Efros, and M. Hebert, "Recovering occlusion boundaries from a single image.' International Conference on Computer Vision,2007.
[129]L. Ladicky, P. Sturgess, C. Russell, S. Sengupta, Y. Bastanlar, W. Clocksin, and P. H. S. Torr, "Joint optimization for object class segmentation and dense stereo reconstruction," International Journal of Computer Vision, vol.100(2), pp.1-12,2012.
[130]M. Bleyer, C. Rother, P. Kohli, D. Scharstein, and S. Sinha, "Object stereo—oint stereo matching and object segmentation," CVPR,2011.
[131]C. Zhang, L. Wang, R. Yang, "Semantic segmentation of urban scenes using dense depth maps," European Conference on Computer Vision,2010.
[132]N. Silberman N, D. Hoiem, K. Pushmeet, and R. Fergus, "Indoor Segmentation and Support Inference from RGBD Images," European Conference on Computer Vision,2012.
[133]D. Hoiem, A. Efros, and M. Hebert, "Recovering surface layout from an image," International Journal of Computer Vision," vol.75(1), pp.151-172,2002.
[134]D. Hoiem, A. Efros, and M. Hebert, "Geometric context from a single image," Tenth IEEE International Conference on Computer Vision,2005.
[135]S. Gould, "Multiclass pixel labeling with non-local matching constraints," In IEEE Conference on Computer Vision and Pattern Recognition, pp.2783-2790,2012.
[136]X. He, R. S. Zemel, and M. A. Carreira-Perpin'an, "Multi-scale conditional random fields for image labeling. In IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp.11-695,2004.
[137]P. Kohli, P. H. Torr, L. Ladicky, "Robust higher order potentials for enforcing label consistency," International Journal of Computer Vision, vol.82(3), pp.302-324,2009.
[138]I. Blthoff, H. Blthoff, and P. Sinha, "Top-down influences on stereoscopic depthperception," Nature Neuroscience, vol.1, pp.254-257,1998.
[139]J. Michels, A. Saxena, and A.Y. Ng, "High speed obstacle avoidance using monocular vision and reinforcement learning," In International Conference on Machine Learning (ICML),2005.
[140]R. Fleming and B. L. Anderson, "The Perceptual Organization of Depth," The visual neurosciences:1119-1128, MIT,2004.
[141]T. Leung and J. Malik, "Representing and recognizing the visual appearance of materials using three-dimensional textons," IJCV, Vol.43(1), pp.29-44,2001.
[142]B. Liu, S. Gould, D. Koller, "Single Image Depth Estimation From Predicted Semantic Labels," CVPR,2010.
[2]D.G. Treicher, Are you missing the boat in training aid? Film and AV Communication 1 (1967) 14-16.
[3]A. Saxena, L. Wong, M. Quigley, and A. Y. Ng, "A vision-based system for grasping novel objects in cluttered environments," In International Symposium on Robotics Research (ISRR),2007.
[4]D. Thirde, M. Borg, J. Ferryman, F. Fusier, V. Valentin, F. Bremond, and M. Thon-nat, "A real-time scene understanding system for airport apron monitoring," In Proc. of the International Conference on Computer Vision Systems (ICCVS),2006.
[5]D. Mirota, R. H. Taylor, M. Ishii, and G. D. Hager. Direct endoscopic video reg-istration for sinus surgery. In Medical Imaging 2009:Visualization, Image-guided Procedures and Modeling. Proceedings of the SPIE,2009.
[6]D. Ritendra, J. Dhiraj, Li Jia, and Wang, James Z, "Image retrieval:Ideas, influences, and trends of the new age," ACM Computing Surveys (CSUR), 40(2),2008.
[7]Lowe, D. G, "Distinctive image features from scale-invariant keypoints," Springer,60:91-110,2004.
[8]L. Fei-Fei and P. Perona, "A Bayesian Hierarchical Model for Learning Natural Scene Categories," Proc. of IEEE Computer Vision and Pattern Recognition, pp. 524-531,2005
[9]L. Fei-Fei, R. Fergus, and A. Torralba. "Recognizing and Learning Object Categories," CVPR short course,2007.
[10]G. Qiu, "Indexing chromatic and achromatic patterns for content-based colour image retrieval," Pattern Recognition, vol.35, pp.1675-1686,2002.
[11]Z. Tu, X. Chen, Y. Alan, and S. Zhu, "Image parsing:Unifying segmentation, detection, and recognition," International Journal of Computer Vision 63(2): 113-140,2005.
[12]L. Bastian, L. Ales, and S. Bernt, "An implicit shape model for combined object categorization and segmentation", Springer,2006.
[13]J. Winn, N. Joijic, "Locus:Learning object classes with unsupervised segmentation," In Proceedings of the IEEE International Conference on Computer Vision, Beijing,2005.
[14]G. Humphrey, "The psychology of the gestalt. Journal of Educational Psychology," 15(7),401-412,1924.
[15]R. Shapley, N. Rubin, and D. Ringach, "Visual segmentation and illusory contours." The visual neurosciences:1119-1128, MIT,2004.
[16]R. V. D. HEYDT, "Image parsing mechanisms of the visual cortex," The visual neurosciences:1139-1150, MIT,2004.
[17]N. R. Pal and S. K. Pal, "A review on image segmentation techniques." Pattern Recognition 26(9):1277-1294,1993.
[18]H. Zhang, J. E. Fritts, and S. A. Goldman, "Image segmentation evaluation:A survey of unsupervised methods," Computer Vision and Image Understanding 110(2):260-280,2008.
[19]D. Marr, Vision:A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman and Company, New York, 1996.
[20]L. G. Shapiro and G. C. Stockman, "Computer Vision", pp.279-325, New Jersey, Prentice-Hall,2001.
[21]Stan Z. Li, "Markov Random Field Modeling in Image Analysis," springer, 2001.
[22]J. M. Morel and S. Solimini, "Variational methods in image segmentation," Birkhauser Boston Inc,1995.
[23]D. Mumford and J. Shah, "Optimal approximations by piecewise smooth functions and associated variational problems," Communications on pure and applied mathematics 42(5), pp.577-685,1989.
[24]D. Cremers, M. Rousson, and R. Deriche, "A Review of Statistical Approaches to Level Set Segmentation:Integrating Color, Texture, Motion and Shape," International Journal of Computer Vision vol.72(2), pp.195-215,2007.
[25]D. Comaniciu, and P. Meer, "Mean shift:a robust approach toward feature space analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):603-619,2002.
[26]S. Belongie, C. Carson, H. Greenspan, and J. Malik, "Color-and texture-based image segmentation using EM and its application to content-based image retrieval," cvpr, pp.657-682,1998.
[27]A. Y. Yang, J. Wright, and Y. Ma, S. Sastry, "Unsupervised segmentation of natural images via lossy data compression," CVIU, vol.110(2), pp.212-225, 2008.
[28]P. Felzenszwalb and D. Huttenlocher, "Efficient Graph-Based Image Segmentation," International Journal of Computer Vision, vol.59(2), pp.167-181,2004.
[29]J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22(8), pp. 888-905,2000.
[30]J. Malik, S. Belongie, T. Leung, and J. Shi, "Contour and texture analysis for image segmentation," International Journal of Computer Vision, vol.43(1), pp.7-27,2001.
[31]D. A. Tolliver and G. L. Miller, "Graph partitioning by spectral rounding: Applications in image segmentation and clustering," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.1053-1060,2006.
[32]T. Cour, F. Benezit, and J. Shi, "Spectral segmentation with multiscale graph decomposition," IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005.
[33]J. Canny, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.8(6), pp.679-698,1986.
[34]P. Perona and J. Malik, "Detecting and localizing edges composed of steps, peaks and roofs," Third International Conference on Computer Vision,1990.
[35]D. R. Martin, C. C. Fowlkes, and J. Malik, "Learning to detect natural image boundaries using local brightness, color, and texture cues," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26(5), pp.530-549,2004.
[36]P. Felzenszwalb and D. McAllester, "A min-cover approach for finding salient curves," IEEE Conference on Computer Vision and Pattern Recognition Workshop,2006.
[37]X. Ren, C. C. Fowlkes, and J. Malik, "Scale-invariant contour completion using conditional random fields," Tenth IEEE International Conference on Computer Vision,2005.
[38]P. Dollar, Z. Tu, and S. Belongie, "Supervised learning of edges and object boundaries," IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2006.
[39]Q. Zhu, G. Song, and J. Shi, "Untangling cycles for contour grouping," IEEE 11th International Conference on Computer Vision,2007.
[40]J. Mairal, M. Leordeanu, F. Bach, M. Hebert and J. Ponce, "Discriminative sparse image models for class-specific edge detection and image interpretation," ECCV, Springer, pp.43-56,2008.
[41]X. Ren, "Multi-scale improves boundary detection in natural images," ECCV 2008.
[42]J. Malik, S. Belongie, T. Leung, and J. shi, "Contour and texture analysis for image segmentation," International Journal of Computer Vision vol.43(1), pp. 7-27.
[43]P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, "From contours to regions:An empirical evaluation," Computer Vision and Pattern Recognition,2009.
[44]M. P. Kumar, P. H. S. Ton, and A. Zisserman, "OBJ CUT," IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005.
[45]J. Shotton, A. Blake, and R. Cipolla, "Contour-based learning for object detection," Tenth IEEE International Conference on Computer Vision,2005.
[46]J. Shotton, J. Winn, C. Rother, and A. Criminisi, "TextonBoost:Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation," ECCV,2006.
[47]J. Winn and J. Shotton, "The Layout Consistent Random Field for Recognizing and Segmenting Partially ccluded Objects," CVPR,2006.
[48]J. Shotton, M. Johnson, and R. Cipolla, "Semantic Texton Forests for Image Categorization and Segmentation," CVPR,2008.
[49]S. Gould, R. Fulton, and D. Koller, "Decomposing a scene into geometric and semantically consistent regions," IEEE 12th International Conference on Computer Vision,2009.
[50]Florian Schroff, "Semantic Image Segmentation and Web-Supervised Visual Learning," Doctor of Philosophy Thesis, University of Oxford,2009.
[51]C. Rother, V. Kolmogorov, and A. Blake, "Grabcut:Interactive foreground extraction using iterated graph cuts," ACM SIGGRAPH,2004.
[52]V. Lempitsky, P. Kohli, C. Rother, and T. Sharp, "Image Segmentation with A Bounding Box Prior," ICCV,2009.
[53]J. Wang, and M. F. Cohen, "An iterative optimization approach for unified image segmentation and matting," Tenth IEEE International Conference on Computer Vision,2005.
[54]A. Levin, D. Lischinski, and Y. Weiss, "A Closed-Form Solution to Natural Image Matting," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30(2) pp.228-242,2008.
[55]V. Kolmogorov and R. Zabin, "What energy functions can be minimized via graph cuts?" IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26(2) pp.147-159,2004.
[56]Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts." IEEE Transactions on Pattern Analysis and Machine Intelligence:1222-1239,2001.
[57]Boykov, Y. and O. Veksler, "Graph cuts in vision and graphics:Theories and applications," Handbook of mathematical models in computer vision, Springer, pp.79-96,2006.
[58]F. Estrada, A. Jepson, and C. Chennubhotla, "Spectral embedding and min-cut for image segmentation," British Machine Vision Conference,2004.
[59]A. Hoover, G. Jean-Baptiste, X. Jiang, P. J. Flynn, H. Bunke, D. B. Goldgof, K. Bowyer, D. W. Eggert, A. Fitzgibbon, and R. B. Fisher, "An experimental comparison of range image segmentation algorithms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18(7), pp.673-689,1996.
[60]M. W. Powell, K. W. Bowyer, X. Jiang, and H. Bunke, "Comparing curved-surface range image segmenters," IEEE Sixth International Conference on Computer Vision,1998.
[61]X. Wang and H. Wang, "Markov random field modeled range image segmentation," Pattern Recognition Letters, vol.25(3), pp.367-375,2004.
[62]N. Yokoya and M. D. Levine, "Range image segmentation based on differential geometry:A hybrid approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11(6), pp.643-649,1989.
[63]Y. Ding, X. Ping, M. Hu, and D. Wang, "Range image segmentation based on randomized hough transform," Pattern Recognition Letters, vol.26(13), pp. 2033-2041,2005.
[64]A. Bab-Hadiashar and D. Suter, "Robust range segmentation," IEEE Fourteenth International Conference on Pattern Recognition,1998.
[65]H. Feng, Z. Tu, and S. Zhu, "Range image segmentation by an effective jump-diffusion method," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26(9), pp.1138-1153,2004.
[66]A. D. Sappa and M. Devy, "Fast range image segmentation by an edge detection strategy," Third International Conference on 3-D Digital Imaging and Modeling, 2001.
[67]D. Han, B. Lee, J. Cho, and D. Hwang, "Real-time object segmentation using disparity map of stereo matching," Applied Mathematics and Computation, vol. 205(2), pp.770-777,2008.
[68]A. Gupta, "Range image segmenation for 3-d object recognition," MS-CIS, Univerity of Pennsylvania,1988.
[69]T.-J. Fan, G. Medioni, and R. Nevatial, "Recognizing 3-D objects using surface descriptions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11(11), pp.1140-1157,1989.
[70]A. Guzman-Arenas and A. Guzmaan, "Computer recognition of three-dimensional objects in a visual scene," Massachusetts Institute of Technology,1968.
[71]J. B. Paul and C. J. Ramesh, "Three-dimensional object recognition," Computing Surveys, Vol.17, No.1, March 1985.
[72]G. Gordon, T. Darrell, M. Harville, and J. Woodfill, "Background estimation and removal based on range and color," IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1999.
[73]V. Kolmogorov, A. Criminisi, A. Blake, G. Cross, and C. Rother, "Probabilistic Fusion of Stereo with Color and Contrast for Bi-Layer Segmentation," International Journal of Computer Vision, vol.76(2), pp.107-107,2008.
[74]N. D. Doulamis, A. D. Doulamis, Y. S. Avrithis, K. S. Ntalianis, and S. D. Kollias, "Efficient summarization of stereoscopic video sequences, " IEEE Transactions on Circuits and Systems for Video Technology, vol.10(4), pp. 501-517,2000.
[75]A. D. Doulamis, N. D. Doulamis, K. S. Ntalianis, and S. D. Kollias, "Unsupervised semantic object segmentation of stereoscopic video sequences," IEEE INT. CONF. ON INTELLIGENCE, INFORMATION AND SYSTEMS, 1999.
[76]C. Cigla and A. Aydin Alatan, "Depth Assisted Object Segmentation in Multi-View Video," 3DTV Conference:The True Vision-Capture, Transmission and Display of 3D Video,2008.
[77]C. Zhang, L. Wang, and R. Yang, "Semantic segmentation of urban scenes using dense depth maps," ECCV, Springer, pp.708-721,2010.
[78]N. Silberman and R. Fergus, "Indoor scene segmentation using a structured light sensor," IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona,2011.
[79]X. Ren, I. P. Computing, L. Bo, and D. Fox, "RGB-(D) Scene Labeling: Features and Algorithms," IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2012.
[80]K. Fukunaga and L. Hostetler, "The estimation of the gradient of a density function, with applications in pattern recognition," IEEE Transactions on Information Theory, vol.21(1), pp.32-40,1975.
[81]Y. Cheng, "Mean shift, mode seeking, and clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.17(8), pp.790-799,1995.
[82]R. O. Duda and P. E. Hart, "Pattern classification and scene analysis," Wiley, 1973.
[83]M. Tabb and N. Ahuja, "Multiscale image segmentation by integrated edge and region detection," IEEE Transactions on Image Processing, vol.6(5), pp. 642-655,1997.
[84]T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, "Introduction to algorithms," The MIT press,2009.
[85]Z. Wu and R. Leahy, "An optimal graph theoretic approach to data clustering: theory and its application to image segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15(11), pp.1101-1113,1993.
[86]G. H. Golub and C. F. Van Loan, "Matrix computations," John Hopkins Press, 1989.
[87]M. R. Everingham, H. Muller, and B. Thomas, "Evaluating image segmentation algorithms using the pareto front," In European Conference on Computer Vision (ECCV), volume 4, pp.34-48,2002.
[88]C. W. Shaffrey, I. H. Jermyn, and N. G. Kingsbury, "Psychovisual evaluation of image segmentation algorithms," In Advanced Concepts for Intelligent Vision Systems,2002.
[89]F. J. Estrada and A. D. Jepson, "Quantitative evaluation of a novel image segmentation algorithm," CVPR,2005.
[90]J. Freixenet, X. Munoz, D. Raba, J. Marti, and X. Cuff. "Yet another survey on image segmentation:Region and boundary information integration," In European Conference on Computer Vision (ECCV), pp.408-422,2002.
[91]Q. Huang and B. Dom, "Quantitative methods of evaluating image segmentation," In IEEE Intl. Conf. on Image Processing (ICIP), pp.53-56, 1995.
[92]D. Martin, "An Empirical Approach to Grouping and Segmentation," PhD thesis, Univ. of California, Berkeley,2002.
[93]H. I. Christensen and P. J. Phillips, "Empirical Evaluation methods in Computer Vision," World Scientific Publishing Company, July 2002.
[94]T. Malisiewicz and A. A. Efros, "Improving spatial support for objects via multiple segmentations," In BMVC, September 2007.
[95]D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics," In ICCV,2001.
[96]C. Harris and M. Stephens, "A combined corner and edge detector," Proceedings of the 4th AIvey Vision Conference, pp.147-15],1988.
[97]K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir and L. Van Gool, "A comparison of affine region detectors," In IJCV, vol.65(1/2), pp. 43-72,2005.
[98]H. Bay, A. Ess, T. Tuytelaars, and Luc Van Gool, "SURF:Speeded Up Robust Features," Computer Vision and Image Understanding (CVIU), Vol.110, No.3, pp.346-359,2008.
[99]D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol.60(2), pp.91-110,2004.
[100]J. Sivic and A. Zisserman, "Video Google:a text retrieval approach to object matching in videos," Ninth IEEE International Conference on Computer Vision,2003.
[101]Y. Rubner, C. Tomasi, L. Guibas, "A metric for distributions with applications to image databases," Sixth International Conference on Computer Vision,1998.
[102]F. L. Hitchcock, "The distribution of a product from several sources to numerous localities," J. Math. Phys., vol.20, pp.224-230,1941.
[103]J. A. Bilmes, "A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models," Technical Report TR-97-021, International Computer Science Institute and Computer Science Division, University of California at Berkeley, April 1998.
[104]F. Jurie and B. Triggs, "Creating efficient codebooks for visual recognition," In Proceedings of the 10th International Conference on Computer Vision, Beijing, China, vol.1, pp. 604-610,2005.
[105]B. Leibe and B. Schiele, "Interleaved object categorization and segmentation," In Proceedings of the 14th British Machine Vision Conference, Norwich, vol.2, pp.264-271, 2003.
[106]T. Leung and J. Malik, "Recognizing surfaces using three-dimensional textons," In Proceedings of the 7th International Conference on Computer Vision, Kerkyra, Greece, pp. 1010-1017,1999.
[107]M. Varma and A. Zisserman, "Texture classification:Are filter banks necessary?" In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, vol.2, pp.691-698, June 2003.
[108]J. Winn, Criminisi, A., and T. Minka, "Object Categorization by Learned Universal Visual Dictionary," In Proceedings of the 10th International Conference on Computer Vision, Beijing, China,2005.
[109]C. P. Papageorgiou, M. Oren, and T. Poggio, "A general framework for object detection," In Proceedings of the 6th International Conference on Computer Vision, Bombay, India, pp. 555, Washington, DC, USA,1998.
[110]P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, pp.511-518,2001.
[111]N. Dalal and B Triggs, "Histogram of Oriented Gradients for Human Detection," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, vol.2, pp.886-893,2005.
[112]M. Varma and A. Zisserman, "Classifying materials from images:to cluster or not to cluster?" In Proceedings of the 2nd International Workshop on Texture Analysis and Synthesis, Copenhagen, Denmark, pp.139-144, May 2002.
[113]M. A. Fischler and R. C. Bolles, "Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, vol.24(6), pp.381-395,1981.
[114]D. Scharstein and R. Szelisk, "A taxonomy and evaluation of dense two-frame stereo correspondence algorithms," International Journal of Computer Vision, vol.47(1), pp.7-42, 2002.
[115]S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, "A comparison and evaluation of multi-view stereo reconstruction algorithms," Computer Vision and Pattern Recognition,2006.
[116]A. Saxena, S. Chung, and A. Ng, "Learning depth from single monocular images," Advances in Neural Information Processing Systems,2006.
[117]A. Saxena, S. Chung, and A. Ng, "3-d depth reconstruction from a single still image," International Journal of Computer Vision, vol.76(1), pp.53-69,2008.
[118]J. Shotton, T. Sharp, A. Kipman, A. Fitzgibbon, M. Finocchio, A. Blake, M. Cook, and R. Moore, "Real-time human pose recognition in parts from single depth images," Communications of the ACM, vol.56(1), pp.116-124,2013.
[119]G. Fanelli, J. Gall, and L. Van Gool, "Real time head pose estimation with random regression forests," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
[120]D. A. Forsyth and J. Ponce, "Computer vision:a modern approach," Person Education, 2012.
[121]C. Carson, S. Belongie, H. Greenspan, and J. Malik, "Blobworld:Image Segmentaion Using Expectation-Maximization and Its Application to Image Querying," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24(8), pp.1026-1038, 2002.
[122]Z. Wang, Z. Zheng, "A Region Based Stereo Matching Algorithm Using Cooperative Optimization," IEEE Conference on Computer Vision and Pattern Recognition,2008.
[123]D. Greig, B. Porteous, and A. Seheult, "Exact maximum a posteriori estimation for binary images," Journal of the Royal Statistical Society, Series B, Vol.51(2), pp.271-279,1989.
[124]Y. Boykov and O. Veksler, "Graph cuts in vision and graphics:Theories and applications. Handbook of mathematical models in computer vision," Springer, pp.79-96,2006.
[125]L. Ford and D. Fulkerson, "Flows in Networks," Princeton University Press,1962.
[126]W. Cook, W. Cunningham, W. Pulleyblank, and A. Schrijver, "Combinatorial Optimization," John Wiley & Sons,1998.
[127]M. Grundmann, V. Kwatra, M. Han, and I. Essa, "Efficient Hierarchical Graph-Based Video Segmentation," Computer Vision and Pattern Recognition,2010.
[128]D. Hoiem, A. Efros, and M. Hebert, "Recovering occlusion boundaries from a single image.' International Conference on Computer Vision,2007.
[129]L. Ladicky, P. Sturgess, C. Russell, S. Sengupta, Y. Bastanlar, W. Clocksin, and P. H. S. Torr, "Joint optimization for object class segmentation and dense stereo reconstruction," International Journal of Computer Vision, vol.100(2), pp.1-12,2012.
[130]M. Bleyer, C. Rother, P. Kohli, D. Scharstein, and S. Sinha, "Object stereo—oint stereo matching and object segmentation," CVPR,2011.
[131]C. Zhang, L. Wang, R. Yang, "Semantic segmentation of urban scenes using dense depth maps," European Conference on Computer Vision,2010.
[132]N. Silberman N, D. Hoiem, K. Pushmeet, and R. Fergus, "Indoor Segmentation and Support Inference from RGBD Images," European Conference on Computer Vision,2012.
[133]D. Hoiem, A. Efros, and M. Hebert, "Recovering surface layout from an image," International Journal of Computer Vision," vol.75(1), pp.151-172,2002.
[134]D. Hoiem, A. Efros, and M. Hebert, "Geometric context from a single image," Tenth IEEE International Conference on Computer Vision,2005.
[135]S. Gould, "Multiclass pixel labeling with non-local matching constraints," In IEEE Conference on Computer Vision and Pattern Recognition, pp.2783-2790,2012.
[136]X. He, R. S. Zemel, and M. A. Carreira-Perpin'an, "Multi-scale conditional random fields for image labeling. In IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp.11-695,2004.
[137]P. Kohli, P. H. Torr, L. Ladicky, "Robust higher order potentials for enforcing label consistency," International Journal of Computer Vision, vol.82(3), pp.302-324,2009.
[138]I. Blthoff, H. Blthoff, and P. Sinha, "Top-down influences on stereoscopic depthperception," Nature Neuroscience, vol.1, pp.254-257,1998.
[139]J. Michels, A. Saxena, and A.Y. Ng, "High speed obstacle avoidance using monocular vision and reinforcement learning," In International Conference on Machine Learning (ICML),2005.
[140]R. Fleming and B. L. Anderson, "The Perceptual Organization of Depth," The visual neurosciences:1119-1128, MIT,2004.
[141]T. Leung and J. Malik, "Representing and recognizing the visual appearance of materials using three-dimensional textons," IJCV, Vol.43(1), pp.29-44,2001.
[142]B. Liu, S. Gould, D. Koller, "Single Image Depth Estimation From Predicted Semantic Labels," CVPR,2010.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |