Abstract
Object recognition is one of the key areas in computer vision which comprises of object detection, recognition and reconstruction. The image of the object to be recognized is captured using camera and matched with pre-stored templates of the model object. Recognizing 3D view of the object is difficult in the presence of object occlusion and view-point invariants. This paper focuses on the problem of occlusion and provides a solution for handling self and inter-object occlusion. Self-occlusion has been addressed by the suitable calibration of the cameras and a novel algorithm has been proposed to address inter-object occlusion. A modified geometric mapping technique has been proposed for the 3D reconstruction of the recognized object. Real-time setup has been used to test the proposed solutions to identify objects of multiple shapes and sizes. The results show that the performance of the algorithm was superior and enabled recognition of objects with 80% occlusion or less.
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References
Allen, P.K.: Robotic Object Recognition Using Vision and Touch. Kluwer Academic Publishers, Boston (1987)
Yang, T., Zhang, Y., Yu, R., Zhang, X., Chen, T., Ran, L., Song, Z., Ma, W.: Simultaneous active camera array focus plane estimation and occluded moving object imaging. Image Vis. Comput. 32, 510–521 (2014)
Zeeshan Zia, M., Stark, M., Schindler, K.: Explicit occlusion modeling for 3D object representations. In: IEEE Conference on Computer Vision and Pattern Recognition (2013)
Priya, L., Anand, S., Uma, K.: Detection of occluded objects under backlighting. IJCSE, ISBN: 9788192958022 (2016)
Toshev, A., Taskar, B., Daniilidis, K.: Object detection via boundary structure segmentation. In: CVPR (2010)
Wu, B.-F., et al.: A relative-discriminative-histogram-of-oriented-gradients-based particle filter approach to vehicle occlusion handling and tracking. IEEE Trans. Ind. Electron. 61(8), 4228–4237 (2014)
Leibe, B., Leonardis, A., Schiele, B.: Robust object detection with interleaved categorization and segmentation. IJCV 77(1–3), 259–289 (2008)
Shah, SAA.: A novel local surface description for automatic 3D object recognition in low resolution cluttered scenes. In: ICCV 2013, IEEE Xplore (2013)
Gao, T., Packer, B., Koller, D.: A segmentation-aware object detection model with occlusion handling. In: CVPR (2011)
Joglekar, J., Gedam, S.S.: Area/based stereo matching technique using Hausdorff distance and texture analysis, PIA11. International Archives of Photogrammetry
http://www.techbriefs.com/component/content/article/ntb/features/feature-articles/14925
Suzuki, M.T., Yaginuma, Y., Shimizu, Y.: A partial shape matching technique for 3D model retrieval systems. In: SIGGRAPH 2005, The 32nd International Conference on Computer Graphics and Interactive Techniques ISBN:1-59593-100-7, ACM-ON:428052, 2005
Zhang, S., Qu, X., Ma, S., Yang, Z., Kong, L.: A dense stereo matching algorithm based on triangulation. J. Comput. Inf. Syst. 8(1), 283–292 (2012)
Niknejad, H.T., Kawano, T., Oishi, Y., Mita, S.: Occlusion handling using discriminative model of trained part templates and conditional random field. In: IEEE Intelligent Vehicles Symposium (IV) June 23–26, 2013. Gold Coast, Australia (2013)
Sabourin, C., Moreno, R., Madani, K.: A machine learning based intelligent vision system for autonomous object detection and recognition. Appl. Intell. 40(2), 358–375 (2014)
Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing features: efficient boosting procedures for multiclass object detection. In: CVPR (2004)
Lowe, D.: Distinctive image features from scale-invariant keypoints. In: IJCV (2004)
Hinterstoisser, S., Lepetit, V., Ilic, S., Fua, P., Navab, N.: Dominant orientation templates for real-time detection of texture-less objects. In: CVPR (2010)
Borgefors, G.: Hierarchical chamfer matching: a parametric edge matching algorithm. PAMI 10(6), 849–865 (1988)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002)
Okada, R.: Discriminative generalized hough transform for object detection. In: ICCV (2009)
Chandel, H., Bahra University Waknaghat, Shimla, Vatta, S.: Occlusion detection and handling: a review. Int. J. Comput. Appl. 120(10), 0975–8887 (2015)
Amit, Y., Geman, D., Fan, X.: A coarse-to-fine strategy for multiclass shape detection. TPAMI 26(12), 1606–1621 (2004)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition (2001)
Priya, L., Anand, S.: Shape based object detection for partially occluded objects under front lighting techniques. In: 2015 IEEE European Modeling Symposium, 978-1-5090-0206-1/15. IEEE, doi:10.1109/EMS.2015.27 (2015)
Loncaric, S.: A survey of shape analysis techniques. Pattern Recognit. 31(8), 983–1001 (1998)
Gandhi, C., Viradiya, J.: A survey on occlusion detection and handling. Int. J. Adv. Eng. Res. Dev. 3(2), 203–209 (2016)
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Priya, L., Anand, S. Object recognition and 3D reconstruction of occluded objects using binocular stereo. Cluster Comput 21, 29–38 (2018). https://doi.org/10.1007/s10586-017-0891-7
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DOI: https://doi.org/10.1007/s10586-017-0891-7