Particle Filter with Affine Transformation for Multiple Key Points Tracking

This paper proposes an accurate method for multiple key points tracking in long microscopic sequences. Tracking in normal-scale image sequences is proved to be a valuable fundamental technology in computer vision, while tracking in microscopic sequences is a more challenging work due to its poor image quality resulted from the complexity of microscopic imaging process. The micro stereo imaging process can be implemented in a tilting rotation of the stage which produces an affine geometric transformation on the projection of rigid spatial micro structure. This paper finds that the projection's affine invariance leads tracking of point templates to be a feasible solution, due to the fixed spatial relationship among the composed of simple fundamental components such as points, lines and planes. At the same time, we apply an adaptive particle filter (PF) of points tracking algorithm to sample and calculate the weights from those multiple point templates, which can resolve the visual distortion, illumination variability and irregular motion estimation. The experimental results are precise and robust for rigid multiple key points tracking in long micro image sequences.

[1]  A. Barth,et al.  Where will the oncoming vehicle be the next second? , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[2]  Duy-Dinh Le,et al.  Robust Face Track Finding in Video Using Tracked Points , 2008, 2008 IEEE International Conference on Signal Image Technology and Internet Based Systems.

[3]  Andrew W. Fitzgibbon,et al.  Combining local and global motion models for feature point tracking , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Baihua Li,et al.  Reconstruction of segmentally articulated structure in freeform movement with low density feature points , 2004, Image Vis. Comput..

[5]  P. Perona,et al.  Motion estimation via dynamic vision , 1996, IEEE Trans. Autom. Control..

[6]  Fatih Murat Porikli,et al.  Covariance Tracking using Model Update Based on Lie Algebra , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[7]  Kyoung Mu Lee,et al.  Visual tracking via geometric particle filtering on the affine group with optimal importance functions , 2009, CVPR.

[8]  Mubarak Shah,et al.  Target tracking in airborne forward looking infrared imagery , 2003, Image Vis. Comput..

[9]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[10]  Xiaobo Zhou,et al.  Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy , 2006, IEEE Transactions on Biomedical Engineering.

[11]  Carme Torras,et al.  Qualitative vision for the guidance of legged robots in unstructured environments , 2001, Pattern Recognit..

[12]  Lexiao Ye,et al.  Real-Time Tracking of the Shoot Point from Light Pen Based on Camshift , 2008, 2008 First International Conference on Intelligent Networks and Intelligent Systems.

[13]  Frank Chongwoo Park,et al.  Visual Tracking via Particle Filtering on the Affine Group , 2008, 2008 International Conference on Information and Automation.

[14]  X. Zhuang,et al.  Virus trafficking – learning from single-virus tracking , 2007, Nature Reviews Microbiology.

[15]  Rama Chellappa,et al.  Dynamic feature point tracking in an image sequence , 1994, Proceedings of 12th International Conference on Pattern Recognition.