Contour Tracking with a Spatio-Temporal Intensity Moment

Standard edge detection operators such as the Laplacian of Gaussian and the gradient of Gaussian can be used to track contours in image sequences. When using edge operators, a contour, which is determined on a frame of the sequence, is simply used as a starting contour to locate the nearest contour on the subsequent frame. However, the strategy used to look for the nearest edge points may not work when tracking contours of non isolated gray level discontinuities. In these cases, strategies derived from the optical flow equation, which look for similar gray level distributions, appear to be more appropriate since these can work with a lower frame rate than that needed for strategies based on pure edge detection operators. However, an optical flow strategy tends to propagate the localization errors through the sequence and an additional edge detection procedure is essential to compensate for such a drawback. In this paper a spatio-temporal intensity moment is proposed which integrates the two basic functions of edge detection and tracking.

[1]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[2]  James D. Thomas,et al.  Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates , 1998, IEEE Transactions on Medical Imaging.

[3]  William B. Thompson,et al.  Exploiting Discontinuities in Optical Flow , 1998, International Journal of Computer Vision.

[4]  J. Weickert,et al.  Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods , 2005 .

[5]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[6]  Tomaso A. Poggio,et al.  On Edge Detection , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Jerry L Prince,et al.  Image Segmentation Using Deformable Models , 2000 .

[8]  Anil K. Jain Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[9]  James S. Duncan,et al.  Non-Rigid Motion Models for Tracking the Left Ventricular Wall , 1991, IPMI.

[10]  Joseph K. Kearney,et al.  Optical Flow Estimation: An Error Analysis of Gradient-Based Methods with Local Optimization , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Dorin Comaniciu,et al.  Mean shift analysis and applications , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[12]  Dimitris N. Metaxas,et al.  Optical Flow Constraints on Deformable Models with Applications to Face Tracking , 2000, International Journal of Computer Vision.

[13]  Daniel Cremers,et al.  Integral Invariants for Shape Matching , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[15]  W. Eric L. Grimson,et al.  A Unifying Approach to Registration, Segmentation, and Intensity Correction , 2005, MICCAI.

[16]  Daniel Cremers,et al.  Large displacement optical flow computation withoutwarping , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[17]  Michael Brady,et al.  Velocity Estimation in Ultrasound Images: A Block Matching Approach , 2003, IPMI.

[18]  Jonathan W. Brandt,et al.  Improved Accuracy in Gradient-Based Optical Flow Estimation , 1997, International Journal of Computer Vision.

[19]  M Demi New approach to automatic contour detection from image sequences: an application to ventriculographic images. , 1994, Computers and biomedical research, an international journal.

[20]  Daniel Cremers,et al.  Globally optimal shape-based tracking in real-time , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Marcello Demi,et al.  Contour Tracking When Two Gray-Level Discontinuities Are Close to Each Other , 2008, CIARP.

[22]  Yongmin Kim,et al.  A multiple active contour model for cardiac boundary detection on echocardiographic sequences , 1996, IEEE Trans. Medical Imaging.

[23]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

[24]  Max Mignotte,et al.  Optical-Flow Based on an Edge-Avoidance Procedure , 2006, 2006 International Conference on Image Processing.

[25]  Ellen C. Hildreth,et al.  Computations Underlying the Measurement of Visual Motion , 1984, Artif. Intell..

[26]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Michael Elad,et al.  On the Design of Filters for Gradient-Based Motion Estimation , 2005, Journal of Mathematical Imaging and Vision.

[28]  Thomas Brox,et al.  High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.

[29]  Anthony J. Yezzi,et al.  A variational framework for integrating segmentation and registration through active contours , 2003, Medical Image Anal..

[30]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[31]  J. Alison Noble,et al.  Ultrasound image segmentation: a survey , 2006, IEEE Transactions on Medical Imaging.

[32]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[33]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[34]  Milan Sonka,et al.  Automatic segmentation of echocardiographic sequences by active appearance motion models , 2002, IEEE Transactions on Medical Imaging.

[35]  Michael J. Black,et al.  Secrets of optical flow estimation and their principles , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[36]  Stephen M. Pizer,et al.  Image geometry through multiscale statistics , 1996 .

[37]  C. Bregler,et al.  Large displacement optical flow , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  Edward H. Adelson,et al.  Human-assisted motion annotation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[39]  Keith Langley,et al.  Recursive Filters for Optical Flow , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  Seth J. Teller,et al.  Particle Video: Long-Range Motion Estimation Using Point Trajectories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[41]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[42]  F. Faita,et al.  Real‐time Measurement System for Evaluation of the Carotid Intima‐Media Thickness With a Robust Edge Operator , 2008, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[43]  Michael Isard,et al.  Active Contours , 2000, Springer London.

[44]  Marcello Demi,et al.  Real time contour tracking with a new edge detector , 2004, Real Time Imaging.

[45]  Michael Unser,et al.  Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation , 2005, IEEE Transactions on Medical Imaging.

[46]  Marcello Demi,et al.  The First Absolute Central Moment in Low-Level Image Processing , 2000, Comput. Vis. Image Underst..

[47]  Alessandro C Rossi,et al.  Automatic localization of intimal and adventitial carotid artery layers with noninvasive ultrasound: a novel algorithm providing scan quality control. , 2010, Ultrasound in medicine & biology.

[48]  Frederic Fol Leymarie,et al.  Tracking Deformable Objects in the Plane Using an Active Contour Model , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[49]  J. Alison Noble,et al.  A shape-space-based approach to tracking myocardial borders and quantifying regional left-ventricular function applied in echocardiography , 2002, IEEE Transactions on Medical Imaging.

[50]  William J. Christmas,et al.  Filtering requirements for gradient-based optical flow measurement , 2000, IEEE Trans. Image Process..

[51]  Petia Radeva,et al.  ECOC Random Fields for Lumen Segmentation in Radial Artery IVUS Sequences , 2009, MICCAI.

[52]  Gregory G. Slabaugh,et al.  Coupled PDEs for non-rigid registration and segmentation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[53]  Demetri Terzopoulos,et al.  Regularization of Inverse Visual Problems Involving Discontinuities , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[54]  Ce Liu,et al.  Exploring new representations and applications for motion analysis , 2009 .

[55]  Marcello Demi,et al.  A System for Real-Time Measurement of the Brachial Artery Diameter in B-Mode Ultrasound Images , 2007, IEEE Transactions on Medical Imaging.

[56]  Chao Lu,et al.  A coupled segmentation and registration framework for medical image analysis using robust point matching and active shape model , 2012, 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis.

[57]  Shang-Hong Lai,et al.  Reliable and Efficient Computation of Optical Flow , 1998, International Journal of Computer Vision.

[58]  Peter J. Huber,et al.  Robust Statistics , 2005, Wiley Series in Probability and Statistics.

[59]  M. Demi,et al.  On the gray-level central and absolute central moments and the mass center of the gray-level variability in low-level image processing , 2005, Comput. Vis. Image Underst..

[60]  Alan L. Yuille,et al.  A regularized solution to edge detection , 1985, J. Complex..

[61]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[62]  Abbas El Gamal,et al.  Optical flow estimation using temporally oversampled video , 2005, IEEE Transactions on Image Processing.

[63]  Michael Hoch,et al.  A semi-automatic system for edge tracking with snakes , 1996, The Visual Computer.

[64]  Marcello Demi,et al.  Contour Tracking by Enhancing Corners and Junctions , 1996, Comput. Vis. Image Underst..

[65]  Eero P. Simoncelli,et al.  Differentiation of discrete multidimensional signals , 2004, IEEE Transactions on Image Processing.

[66]  Michael J. Black,et al.  The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields , 1996, Comput. Vis. Image Underst..

[67]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.