• Acta Optica Sinica
  • Vol. 37, Issue 2, 215001 (2017)
Wu Peiliang1、2、*, Fu Weixing1, and Kong Lingfu1、2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • show less
    DOI: 10.3788/aos201737.0215001 Cite this Article Set citation alerts
    Wu Peiliang, Fu Weixing, Kong Lingfu. A Fast Algorithm for Affordance Detection of Household Tool Parts Based on Structured Random Forest[J]. Acta Optica Sinica, 2017, 37(2): 215001 Copy Citation Text show less
    References

    [1] Gibson J J. The theory of affordances[M].//Shaw R, Bransford J. Perceiving, acting, and knowing: toward an ecological psychology. Hilldale: Lawrence Erlbaum Associates, 1977: 67-82.

    [2] Zhu Y, Fathi A, Li F F. Reasoning about object affordances in a knowledge base representation[C]. European Conference on Computer Vision, 2014: 408-424.

    [3] Koppula H S, Saxena A. Physically grounded spatio-temporal object affordances[C]. European Conference on Computer Vision, 2014: 831-847.

    [4] Stark L, Bowyer K. Function-based generic recognition for multiple object categories[J]. CVGIP: Image Understanding, 1994, 59(1): 1-21.

    [5] Bohg J, Kragic D. Grasping familiar objects using shape context[C]. International Conference on Advanced Robotics, 2009.

    [6] Saxena A, Driemeyer J, Ng A Y. Robotic grasping of novel objects using vision[J]. The International Journal of Robotics Research, 2008, 27(2): 157-173.

    [7] Stark M, Lies P, Zillich M, et al. Functional object class detection based on learned affordance cues[C]. International Conference on Computer Vision Systems, 2008: 435-444.

    [8] Grabner H, Gall J, Van Gool L. What makes a chair a chair [C]. IEEE Conference on Computer Vision and Pattern Recognition, 2011: 1529-1536.

    [9] Kjellstrm H, Romero J, Kragic′ D. Visual object-action recognition: inferring object affordances from human demonstration[J]. Computer Vision and Image Understanding, 2011, 115(1): 81-90.

    [10] Zhu Y X, Zhao Y B, Zhu S C. Understanding tools: task-oriented object modeling, learning and recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2015: 2855-2864.

    [11] Hassan M, Dharmaratne A. Attribute based affordance detection from human-object interaction images[C]. Proceedings of the Image and Video Technology Workshops, 2015: 220-232.

    [12] Kemp C C, Edsinger A. Robot manipulation of human tools: autonomous detection and control of task relevant features[C]. International Conference on Intelligent Manipulation and Grasping, 2006: 1-8.

    [13] Mar T, Tikhanoff V, Metta G. Multi-model approach based on 3D functional features for tool affordance learning in robotics[C]. IEEE-RAS International Conference on Humanoid Robots, 2015: 482-489.

    [14] Lenz I, Lee H, Saxena A. Deep learning for detecting robotic grasps[J]. International Journal of Robotics Research, 2015, 34(4-5): 705-724.

    [15] Redmon J, Angelova A. Real-time grasp detection using convolutional neural networks[C]. IEEE International Conference on Robotics and Automation, 2015: 26-30.

    [16] Myers A, Teo C L, Fermuller C, et al. Affordance detection of tool parts from geometric features[C]. IEEE Conference on Robotics and Automation, 2015: 1374-1381.

    [17] Ferrari V, Fevrier L, Jurie F, et al. Groups of adjacent contour segments for object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(1): 36-51.

    [18] Ullman S, Basri R. Recognition by linear combinations of models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(10): 992-1006.

    [19] Arbeláez P, Maire M, Fowlkes C, et al. Contour detection and hierarchical image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(5): 898-916.

    [20] Wachinger C, Fritscher K, Sharp G, et al. Contour-driven atlas-based segmentation[J]. IEEE Transactions on Medical Imaging, 2015, 34(12): 2492-2505.

    [21] Dollar P, Zitnick C L. Fast edge detection using structured forests[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 37(8): 1558-1570.

    [22] Pedersoli M, Vedaldi A, Gonzalez J, et al. A coarse-to-fine approach for fast deformable object detection[J]. Pattern Recognition, 2015, 48(5): 1844-1853.

    [23] Ho T K. Random decision forests[C]. International Conference on Document Analysis and Recognition, 1995: 278-282.

    [24] Criminisi A, Shotton J. Decision forests for computer vision and medical image analysis[C]. Advances in Computer Vision and Pattern Recognition, 2013: 201-212.

    [25] Kontschieder P, Bulo S R, Bischof H, et al. Structured class-labels in random forests for semantic image labelling[C]. IEEE International Conference on Computer Vision, 2011: 2190-2197.

    [26] Koenderink J J, Van Doorn A J. Surface shape and curvature scales[J]. Image and vision computing, 1992, 10(8): 557-564.

    [27] Criminisi A, Shotton J, Konukoglu E. Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning[J]. Foundations and Trends in Computer Graphics and Vision, 2012, 7(2-3): 81-227.

    [28] Lei Qin, Shi Chaojian, Chen Tingting. Structured random forests for target detection in sea images[J]. Opto-Electronic Engineering, 2015, 42(7): 31-35.

    [29] Besl P J, Jain R C. Segmentation through variable-order surface fitting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, 10(2): 167-192.

    [30] Margolin R, Zelnik-Manor L, Tal A. How to evaluate foreground maps [J]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2014: 248-255.

    Wu Peiliang, Fu Weixing, Kong Lingfu. A Fast Algorithm for Affordance Detection of Household Tool Parts Based on Structured Random Forest[J]. Acta Optica Sinica, 2017, 37(2): 215001
    Download Citation