NDLI: A self-organization based optical flow estimator with GPU implementation

Content Provider	Springer Nature Link
Author	Shiralkar, Manish P. Schalkoff, Robert J.
Copyright Year	2011
Abstract	This work describes a parallelizable optical flow field estimator based upon a modified batch version of the Self-Organizing Map (SOM). This estimator handles the ill-posedness in gradient-based motion estimation via a novel combination of regression and self-organization. The aperture problem is treated using an algebraic framework that partitions motion estimates obtained from regression into two sets: one (set H c ) containing motion estimates with high confidence and another (set H p ) with low confidence. The self-organization step uses a uniquely designed pair of sets: training set (Q = H c ) and the initial weights set ( $${W=H_c \cup H_p}$$ ). It is shown that with this specific choice of training and initial weights sets, the interpolation of flow vectors is achieved primarily due to the regularization property of the SOM. Moreover, the computationally involved step of finding the winner unit in SOM simplifies to indexing into a 2D array making the algorithm highly scalable. To preserve flow discontinuities at occlusion boundaries, we have designed an anisotropic neighborhood function for SOM that uses a novel optical flow constraint equation residual-based distance measure. A multi-resolution or pyramidal approach is used to estimate large motion. As self-organization based motion estimation is computationally intense, parallel processing on graphics processing units is used for speedup. As the algorithm is data (rather datum) parallel, with sufficient number of computing cores, the implementation of the estimator can be made real time. With the available ground truth from Middlebury database, error metrics like average angular error and average end point error are computed and are shown to be comparable with other leading techniques.
Starting Page	1229
Ending Page	1242
Page Count	14
File Format	PDF
ISSN	09328092
Journal	Machine Vision and Applications
Volume Number	23
Issue Number	6
e-ISSN	14321769
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-07-16
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Optical flow Parallel implementation Batch SOM Anisotropic neighborhood Graphics processing unit (GPU) Image Processing and Computer Vision Communications Engineering, Networks Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Computer Science Applications Software Hardware and Architecture

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