publication . Other literature type . Part of book or chapter of book . Preprint . 2014

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

Gupta, Saurabh; Girshick, Ross; Arbeláez, Pablo; Malik, Jitendra;
  • Published: 22 Jul 2014
  • Publisher: Springer International Publishing
Abstract
In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves an average precision of 37.3%, which is a 56% relative improvement over existing methods. We then focus on the task of instance segmentation where we label pixels belonging to ob...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Segmentation, Binary number, Machine learning, computer.software_genre, computer, RGB color model, Embedding, Object detection, Random forest, Computer science, Pattern recognition, Computer vision, Convolutional neural network, Artificial intelligence, business.industry, business, Pixel, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
39 references, page 1 of 3

1. Arbelaez, P., Pont-Tuset, J., Barron, J., Marques, F., Malik, J.: Multiscale combinatorial grouping. In: CVPR (2014)

2. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. TPAMI (2011)

3. Banica, D., Sminchisescu, C.: CPMC-3D-O2P: Semantic segmentation of RGB-D images using CPMC and second order pooling. CoRR abs/1312.7715 (2013) [OpenAIRE]

4. Bo, L., Ren, X., Fox, D.: Unsupervised Feature Learning for RGB-D Based Object Recognition. In: ISER (2012)

5. Breiman, L.: Random forests. Machine Learning (2001)

6. Couprie, C., Farabet, C., Najman, L., LeCun, Y.: Indoor semantic segmentation using depth information. CoRR abs/1301.3572 (2013) [OpenAIRE]

7. Deng, J., Berg, A., Satheesh, S., Su, H., Khosla, A., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Competition 2012 (ILSVRC2012). http://www. image-net.org/challenges/LSVRC/2012/

8. Dollar, P.: Piotr's Image and Video Matlab Toolbox (PMT). http://vision.ucsd. edu/~pdollar/toolbox/doc/index.html

9. Dollar, P., Zitnick, C.L.: Structured forests for fast edge detection. In: ICCV (2013) [OpenAIRE]

10. Dollar, P., Zitnick, C.L.: Fast edge detection using structured forests. CoRR abs/1406.5549 (2014)

11. Donahue, J., Jia, Y., Vinyals, O., Ho man, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: A deep convolutional activation feature for generic visual recognition. In: ICML (2014)

12. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: A library for large linear classi cation. JMRL (2008)

13. Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. TPAMI (2013) [OpenAIRE]

14. Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. TPAMI (2010) [OpenAIRE]

15. Geman, D., Amit, Y., Wilder, K.: Joint induction of shape features and tree classi ers. TPAMI (1997)

39 references, page 1 of 3
Abstract
In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves an average precision of 37.3%, which is a 56% relative improvement over existing methods. We then focus on the task of instance segmentation where we label pixels belonging to ob...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Segmentation, Binary number, Machine learning, computer.software_genre, computer, RGB color model, Embedding, Object detection, Random forest, Computer science, Pattern recognition, Computer vision, Convolutional neural network, Artificial intelligence, business.industry, business, Pixel, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
39 references, page 1 of 3

1. Arbelaez, P., Pont-Tuset, J., Barron, J., Marques, F., Malik, J.: Multiscale combinatorial grouping. In: CVPR (2014)

2. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. TPAMI (2011)

3. Banica, D., Sminchisescu, C.: CPMC-3D-O2P: Semantic segmentation of RGB-D images using CPMC and second order pooling. CoRR abs/1312.7715 (2013) [OpenAIRE]

4. Bo, L., Ren, X., Fox, D.: Unsupervised Feature Learning for RGB-D Based Object Recognition. In: ISER (2012)

5. Breiman, L.: Random forests. Machine Learning (2001)

6. Couprie, C., Farabet, C., Najman, L., LeCun, Y.: Indoor semantic segmentation using depth information. CoRR abs/1301.3572 (2013) [OpenAIRE]

7. Deng, J., Berg, A., Satheesh, S., Su, H., Khosla, A., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Competition 2012 (ILSVRC2012). http://www. image-net.org/challenges/LSVRC/2012/

8. Dollar, P.: Piotr's Image and Video Matlab Toolbox (PMT). http://vision.ucsd. edu/~pdollar/toolbox/doc/index.html

9. Dollar, P., Zitnick, C.L.: Structured forests for fast edge detection. In: ICCV (2013) [OpenAIRE]

10. Dollar, P., Zitnick, C.L.: Fast edge detection using structured forests. CoRR abs/1406.5549 (2014)

11. Donahue, J., Jia, Y., Vinyals, O., Ho man, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: A deep convolutional activation feature for generic visual recognition. In: ICML (2014)

12. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: A library for large linear classi cation. JMRL (2008)

13. Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. TPAMI (2013) [OpenAIRE]

14. Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. TPAMI (2010) [OpenAIRE]

15. Geman, D., Amit, Y., Wilder, K.: Joint induction of shape features and tree classi ers. TPAMI (1997)

39 references, page 1 of 3
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