Recovery of motion parameters from distortions in scanned images

Scanned images, such as those produced by the scanning-laser ophthalmoscope (SLO), show distortions when there is target motion. This is because pixels corresponding to different image regions are acquired sequentially, and so, in essence, are slices of different snapshots. While these distortions create problems for image registration algorithms, they are potentially useful for recovering target motion parameters at temporal frequencies above the frame rate. Stetter, Sendtner and Timberlake measured large distortions in SLO images to recover the time course of rapid horizontal saccadic eye movements. Here, this work is extended with the goal of automatically recovering small eye movements in two dimensions. Eye position during the frame interval is modeled using a low dimensional parametric description, which in turn is used to generate predicted distortions of a reference template. The input image is then registered to the distorted template using normalized cross correlation. The motion parameters are then varied, and the correlation recomputed, to find the motion which maximizes the peak value of the correlation. The location and value of the correlation maximum are determined with sub-pixel precision using biquadratic interpolation, yielding eye position resolution better than 1 arc minute. This method of motion parameter estimation is tested using actual SLO images as well as simulated images. Motion parameter estimation might also be applied to individual video lines in order to reduce pipeline delays for a near real-time system.

[1]  B. Troost,et al.  Velocity characteristics of normal human saccades. , 1974, Investigative ophthalmology.

[2]  J. V. Van Gisbergen,et al.  Skewness of saccadic velocity profiles: a unifying parameter for normal and slow saccades. , 1987, Vision research.

[3]  H. Collewijn,et al.  Instability of ocular torsion during fixation: Cyclovergence is more stable than cycloversion , 1994, Vision Research.

[4]  Eileen Kowler The role of visual and cognitive processes in the control of eye movement. , 1990, Reviews of oculomotor research.

[5]  A. V. Loughren Recommendations of the National Television System Committee for a Color Television Signal , 1953 .

[6]  W. J. Daunicht,et al.  Eye movement measurement with the scanning laser ophthalmoscope , 1992 .

[7]  Michal Irani,et al.  Improving resolution by image registration , 1991, CVGIP Graph. Model. Image Process..

[8]  D Marr,et al.  A computational theory of human stereo vision. , 1979, Proceedings of the Royal Society of London. Series B, Biological sciences.

[9]  G. Timberlake,et al.  A Novel Method for Measuring Saccade Profiles Using the Scanning Laser Ophthalmoscope , 1996, Vision Research.

[10]  Colin J. R. Sheppard,et al.  Information capacity and resolution in an optical system , 1986 .

[11]  D. Noton,et al.  Eye movements and visual perception. , 1971, Scientific American.

[12]  J B Mulligan,et al.  Image processing for improved eye-tracking accuracy , 1997, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[13]  R. Webb,et al.  Confocal scanning laser ophthalmoscope. , 1987, Applied optics.

[14]  J.E. Bos,et al.  Ocular torsion quantification with video images , 1994, IEEE Transactions on Biomedical Engineering.

[15]  Steve Mann,et al.  Virtual bellows: constructing high quality stills from video , 1994, Proceedings of 1st International Conference on Image Processing.

[16]  I S Curthoys,et al.  VTM—a New Method of Measuring Ocular Torsion Using Image‐Processing Techniques , 1992, Annals of the New York Academy of Sciences.

[17]  D Ott,et al.  Ocular torsion measured by TV- and scanning laser ophthalmoscopy during horizontal pursuit in humans and monkeys. , 1989, Investigative ophthalmology & visual science.

[18]  Brent R. Beutter,et al.  Eye-Movement Tracking Using Compressed Video Images , 1994 .

[19]  G. Timberlake,et al.  Feature-Based Registration of Retinal Images , 1987, IEEE Transactions on Medical Imaging.

[20]  D. Robinson The mechanics of human saccadic eye movement , 1964, The Journal of physiology.

[21]  E. Groen,et al.  Determination of ocular torsion by means of automatic pattern recognition , 1996, IEEE Transactions on Biomedical Engineering.

[22]  K. W. Cattermole The Fourier Transform and its Applications , 1965 .

[23]  Charles A. Poynton,et al.  A technical introduction to digital video , 1996 .

[24]  L. Stark,et al.  The trajectories of saccadic eye movements. , 1979, Scientific American.

[25]  Peter Cheeseman,et al.  Super-Resolved Surface Reconstruction from Multiple Images , 1996 .

[26]  K Nakayama A New Method of Determining the Primary Position of the Eye Using Listing's Law , 1978, American journal of optometry and physiological optics.

[27]  William Rucklidge Efficient guaranteed search for gray-level patterns , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[28]  Robert H. Webb,et al.  Fundus tracking with the scanning laser ophthalmoscope. , 1987, Applied optics.

[29]  A. Mahurkar,et al.  Constructing Retinal Fundus Photomontages , 1996 .