publication . Preprint . 2013

Constrained Parametric Proposals and Pooling Methods for Semantic Segmentation in RGB-D Images

Banica, Dan; Sminchisescu, Cristian;
Open Access English
  • Published: 30 Dec 2013
Abstract
We focus on the problem of semantic segmentation based on RGB-D data, with emphasis on analyzing cluttered indoor scenes containing many instances from many visual categories. Our approach is based on a parametric figure-ground intensity and depth-constrained proposal process that generates spatial layout hypotheses at multiple locations and scales in the image followed by a sequential inference algorithm that integrates the proposals into a complete scene estimate. Our contributions can be summarized as proposing the following: (1) a generalization of parametric max flow figure-ground proposal methodology to take advantage of intensity and depth information, in...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, I.2.10, I.4.6, I.4.8
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[1] D. Banica, A. Agape, A. Ion, and C. Sminchisescu. Video object segmentation by salient segment chain composition. In ICCVwks, 2013.

[2] J. Carreira, R. Caseiro, J. Batista, and C. Sminchisescu. Semantic segmentation with second-order pooling. In Computer Vision-ECCV 2012. Springer, 2012. [OpenAIRE]

[3] J. Carreira and C. Sminchisescu. Cpmc: Automatic object segmentation using constrained parametric min-cuts. PAMI, 34(7), 2012.

[4] S. Gupta, P. Arbelaez, and J. Malik. Perceptual organization and recognition of indoor scenes from rgb-d images. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 564-571. IEEE, 2013.

[5] A. Ion, J. Carreira, and C. Sminchisescu. Probabilistic Joint Image Segmentation and Labeling. In Advances in Neural Information Processing Systems, December 2011.

[6] A. E. Johnson and M. Hebert. Using spin images for efficient object recognition in cluttered 3d scenes. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 21(5):433- 449, 1999.

[7] M. Leordeanu, R. Sukthankar, and C. Sminchisescu. Efficient Closed-Form Solution to Generalized Boundary Detection. In ECCV, 2012. [OpenAIRE]

[8] F. Li, J. Carreira, G. Lebanon, and C. Sminchisescu. Composite statistical inference for semantic segmentation. Technical report, Technical report, Georgia Institute of Technology, 2013. [OpenAIRE]

[9] D. Lin, S. Fidler, and R. Urtasun. Holistic scene understanding for 3d object detection with rgbd cameras. In ICCV, December 2013.

[10] D. G. Lowe. Distinctive image features from scaleinvariant keypoints. International journal of computer vision, 60(2):91-110, 2004.

[11] M. Maire, P. Arbelaez, C. Fowlkes, and J. Malik. Using contours to detect and localize junctions in natural images. In CVPR, 2008. [OpenAIRE]

[12] X. Ren, L. Bo, and D. Fox. Rgb-(d) scene labeling: Features and algorithms. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 2759-2766. IEEE, 2012.

[13] N. Silberman and R. Fergus. Indoor scene segmentation using a structured light sensor. In Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, pages 601-608. IEEE, 2011.

[14] N. Silberman, D. Hoiem, P. Kohli, and R. Fergus. Indoor segmentation and support inference from rgbd images. In ECCV, 2012. [OpenAIRE]

Abstract
We focus on the problem of semantic segmentation based on RGB-D data, with emphasis on analyzing cluttered indoor scenes containing many instances from many visual categories. Our approach is based on a parametric figure-ground intensity and depth-constrained proposal process that generates spatial layout hypotheses at multiple locations and scales in the image followed by a sequential inference algorithm that integrates the proposals into a complete scene estimate. Our contributions can be summarized as proposing the following: (1) a generalization of parametric max flow figure-ground proposal methodology to take advantage of intensity and depth information, in...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, I.2.10, I.4.6, I.4.8
Download from

[1] D. Banica, A. Agape, A. Ion, and C. Sminchisescu. Video object segmentation by salient segment chain composition. In ICCVwks, 2013.

[2] J. Carreira, R. Caseiro, J. Batista, and C. Sminchisescu. Semantic segmentation with second-order pooling. In Computer Vision-ECCV 2012. Springer, 2012. [OpenAIRE]

[3] J. Carreira and C. Sminchisescu. Cpmc: Automatic object segmentation using constrained parametric min-cuts. PAMI, 34(7), 2012.

[4] S. Gupta, P. Arbelaez, and J. Malik. Perceptual organization and recognition of indoor scenes from rgb-d images. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 564-571. IEEE, 2013.

[5] A. Ion, J. Carreira, and C. Sminchisescu. Probabilistic Joint Image Segmentation and Labeling. In Advances in Neural Information Processing Systems, December 2011.

[6] A. E. Johnson and M. Hebert. Using spin images for efficient object recognition in cluttered 3d scenes. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 21(5):433- 449, 1999.

[7] M. Leordeanu, R. Sukthankar, and C. Sminchisescu. Efficient Closed-Form Solution to Generalized Boundary Detection. In ECCV, 2012. [OpenAIRE]

[8] F. Li, J. Carreira, G. Lebanon, and C. Sminchisescu. Composite statistical inference for semantic segmentation. Technical report, Technical report, Georgia Institute of Technology, 2013. [OpenAIRE]

[9] D. Lin, S. Fidler, and R. Urtasun. Holistic scene understanding for 3d object detection with rgbd cameras. In ICCV, December 2013.

[10] D. G. Lowe. Distinctive image features from scaleinvariant keypoints. International journal of computer vision, 60(2):91-110, 2004.

[11] M. Maire, P. Arbelaez, C. Fowlkes, and J. Malik. Using contours to detect and localize junctions in natural images. In CVPR, 2008. [OpenAIRE]

[12] X. Ren, L. Bo, and D. Fox. Rgb-(d) scene labeling: Features and algorithms. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 2759-2766. IEEE, 2012.

[13] N. Silberman and R. Fergus. Indoor scene segmentation using a structured light sensor. In Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, pages 601-608. IEEE, 2011.

[14] N. Silberman, D. Hoiem, P. Kohli, and R. Fergus. Indoor segmentation and support inference from rgbd images. In ECCV, 2012. [OpenAIRE]

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