The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.01 - Jan. (2013 vol.35)
pp: 130-143
S. Giannarou , Hamlyn Centre for Robotic Surg., Imperial Coll. London, London, UK
M. Visentini-Scarzanella , Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK
Guang-Zhong Yang , Hamlyn Centre for Robotic Surg., Imperial Coll. London, London, UK
ABSTRACT
Despite a wide range of feature detectors developed in the computer vision community over the years, direct application of these techniques to surgical navigation has shown significant difficulties due to the paucity of reliable salient features coupled with free--form tissue deformation and changing visual appearance of surgical scenes. The aim of this paper is to propose a novel probabilistic framework to track affine-invariant anisotropic regions under contrastingly different visual appearances during Minimally Invasive Surgery (MIS). The theoretical background of the affine-invariant anisotropic feature detector is presented and a real-time implementation exploiting the computational power of the GPU is proposed. An Extended Kalman Filter (EKF) parameterization scheme is used to adaptively adjust the optimal templates of the detected regions, enabling accurate identification and matching of the tracked features. For effective tracking verification, spatial context and region similarity have also been incorporated. They are used to boost the prediction of the EKF and recover potential tracking failure due to drift or false positives. The proposed framework is compared to the existing methods and their respective performance is evaluated with in vivo video sequences recorded from robotic-assisted MIS procedures, as well as real-world scenes.
INDEX TERMS
Feature extraction, Detectors, Target tracking, Kalman filters, Visualization, Kernel, Probabilistic logic,image-guided navigation, Salient feature extraction, feature point tracking
CITATION
S. Giannarou, M. Visentini-Scarzanella, Guang-Zhong Yang, "Probabilistic Tracking of Affine-Invariant Anisotropic Regions", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 1, pp. 130-143, Jan. 2013, doi:10.1109/TPAMI.2012.81
REFERENCES
[1] D. Dey, D. Gobbi, P. Slomka, K. Surry, and T.T. Peters, "Automatic Fusion of Freehand Endoscopic Brain Images to Three-Dimensional Surfaces: Creating Stereoscopic Panoramas," IEEE Trans. Medical Imaging, vol. 21, no. 1, pp. 23-30, Jan. 2002.
[2] D. Stoyanov, G.P. Mylonas, M. Lerotic, A.J. Chung, and G.Z. Yang, "Intra-Operative Visualizations: Perceptual Fidelity and Human Factors," J. Display Technology, vol. 4, no. 4, pp. 491-501, 2008.
[3] R. Richa, A. Bo, and P. Poignet, "Robust 3D Visual Tracking for Robotic-Assisted Cardiac Interventions," Medical Image Computing and Computer-Assisted Intervention, vol. 1, pp. 267-274, 2010.
[4] T. Tuytelaars and L. van Gool, "Matching Widely Separated Views Based on Affine Invariant Regions," Int'l J. Computer Vision, vol. 59, no. 1, pp. 61-85, 2004.
[5] F. Schaffalitzky and A. Zisserman, "Multi-View Matching for Unordered Image Sets," Proc. IEEE European Conf. Computer Vision, pp. 414-431, 2002.
[6] T. Kadir, A. Zisserman, and M. Brady, "An Affine Invariant Salient Region Detector," Proc. IEEE European Conf. Computer Vision, pp. 345-457, 2004.
[7] J. Matas, O. Chum, M. Urban, and T. Pajdla, "Robust Wide Baseline Stereo from Maximally Stable Extremal Regions," Proc. British Machine Vision Conf., pp. 384-393, 2002.
[8] K. Mikolajczyk and C. Schmid, "Scale & Affine Invariant Interest Point Detectors," Int'l J. Computer Vision, vol. 60, no. 1, pp. 63-86, 2004.
[9] J. Maver, "Self-Similarity and Points of Interest," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 7, pp. 1211-1226, July 2010.
[10] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. van Gool, "A Comparison of Affine Region Detectors," Int'l J.Computer Vision, vol. 65, nos. 1/2, pp. 43-72, 2005.
[11] B. Lucas and T. Kanade, "An Iterative Image Registration Technique with an Application to Stereo Vision," Proc. Int'l Joint Conf. Artificial Intelligence, pp. 674-679, 1981.
[12] G. Hager and P. Belhumeur, "Real-Time Tracking of Image Regions with Changes in Geometry and Illumination," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 403-410, 1996.
[13] D. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," Int'l J. Computer Vision, vol. 60, pp. 91-110, 2004.
[14] D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-Based Object Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564-577, May 2003.
[15] G. Hager, M. Dewan, and C. Stewart, "Multiple Kernel Tracking with SSD," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 790-797, 2004.
[16] S. Birchfield and S. Rangarajan, "Spatiograms versus Histograms for Region-Based Tracking," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 1158-1163, 2005.
[17] A. Adam, E. Rivlin, and I. Shimshoni, "Robust Fragments-Based Tracking Using the Integral Histogram," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 798-805, 2006.
[18] D. Ross, J. Lim, R.-S. Lin, and M.-H. Yang, "Incremental Learning for Robust Visual Tracking," Int'l J. Computer Vision, vol. 77, pp. 125-141, 2008.
[19] S. Avidan, "Support Vector Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 8, pp. 1064-1072, Aug. 2004.
[20] H. Grabner, M. Grabner, and H. Bischof, "Real-Time Tracking via On-Line Boosting," Proc. British Machine Vision Conf., vol. 1, pp. 47-56, 2006.
[21] S. Avidan, "Ensemble Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 2, pp. 261-271, Feb. 2007.
[22] B. Babenko, M.-H. Yang, and S. Belongie, "Visual Tracking with Online Multiple Instance Learning," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 983-990, 2009.
[23] V. Mahadevan and N. Vasconcelos, "Saliency-Based Discriminant Tracking," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1007 -1013, 2009.
[24] R. Kalman, "A New Approach to Linear Filtering and Prediction Problems," J. Basic Eng., vol. 82, pp. 35-45, 1960.
[25] M. Isard and A. Blake, "CONDENSATION Conditional Density Propagation for Visual Tracking," Int'l J. Computer Vision, vol. 29, pp. 5-28, 1998.
[26] C. Rasmussen and G. Hager, "Probabilistic Data Association Methods for Tracking Complex Visual Objects," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 560-576, June 2001.
[27] A. Jepson, D. Fleet, and T. El-Maraghi, "Robust Online Appearance Models for Visual Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1296-1311, Oct. 2003.
[28] P. Mountney and G.-Z. Yang, "Soft Tissue Tracking for Minimally Invasive Surgery: Learning Local Deformation Online," Medical Image Computing and Computer-Assisted Intervention, vol. 2, pp. 364-372, 2008.
[29] Y. Wu and T. Huang, "Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning," Int'l J. Computer Vision, vol. 58, no. 1, pp. 55-71, 2004.
[30] R. Collins, L. Yanxi, and M. Leordeanu, "Online Selection of Discriminative Tracking Features," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1631-1643, Oct. 2005.
[31] Y. Wu and J. Fan, "Contextual Flow," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 33-40, 2009.
[32] M. Yang, Y. Wu, and G. Hua, "Context-Aware Visual Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 7, pp. 1195-1209, July 2009.
[33] S. Giannarou, M. Visentini-Scarzanella, and G.-Z. Yang, "Affine-Invariant Anisotropic Detector for Soft Tissue Tracking In Minimally Invasive Surgery," Proc. Int'l Symp. Biomedical Imaging, pp. 1059-1062, 2009.
[34] G. Yang, P. Burger, D. Firmin, and S. Underwood, "Structure Adaptive Anisotropic Image Filtering," Image and Vision Computing J., vol. 14, no. 2, pp. 135-145, 1996.
[35] T. Lindeberg, "Feature Detection with Automatic Scale Selection," Int'l J. Computer Vision, vol. 30, no. 2, pp. 79-116, 1998.
[36] D.G. Lowe, "Object Recognition from Local Scale-Invariant Features," Proc. IEEE Int'l Conf. Computer Vision, pp. 1150-1157, 1999.
[37] T. Lindeberg and J. Garding, "Shape-Adapted Smoothing in Estimation of 3-D Shape Cues from Affine Deformations of Local 2-D Brightness Structure," Image and Vision Computing, vol. 15, no. 6, pp. 415-434, 1997.
[38] H. Liu, L. Zhang, Z.Y.H. Zha, and Y. Shi, "Collaborative Mean Shift Tracking Based on Multi-Cue Integration and Auxiliary Objects," Proc. IEEE Int'l Conf. Image Processing, vol. 3, pp. 217-220, 2007.
[39] http://www.robots.ox.ac.uk / vgg/research affine/, 2012.
[40] J.-Y. Bouguet, "Pyramidal Implementation of the Lucas Kanade Feature Tracker: Description of the Algorithm," Intel Corp. Microprocessor Research Labs, 2002.
[41] D. Noonan, C. Payne, J. Shang, V. Sauvage, R. Newton, D. Elson, A. Darzi, and G.-Z. Yang, "Force Adaptive Multi-Spectral Imaging with an Articulated Robotic Endoscope," Medical Image Computing and Computer-Assisted Intervention, vol. 3, 245-252, 2010.
54 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool