A biologically inspired optical flow system for motion detection and object identification

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on April 7, 2008)Includes bibliographical references.Thesis (M.S.) University of Missouri-Columbia 2007.Dissertations, Academic -- University of Missouri--Columbia -- Electrical and computer engineering.Optical flow is possibly the best known method for motion segmentation. However its application is restricted to offline processing as it requires extensive computational resources and time. This thesis explores an optical flow method derived from observation on vision system of diptereous insect. The proposed method , Biological Optical flow (BioOF) was implemented using series of first order filters, and, therefore is much faster than any existing machine coded optical flow algorithm beside being hardware implement able. Like other optical flow methods, the output of proposed BioOF has two components: horizontal optical flow and vertical optical flow; both of them can be combined in order to get a better final result in terms of motion segmentation. Unfortunately, this combined output of the BioOF can be heavily coupled with noise. So, in order to remove the noise, intensive image processing had to be performed. The result was an algorithm that can provide a good contour of the segmented object in an image. Finally the object contour is converted to a Fourier feature space leading to a representation that is rotational and translational invariant. Over this feature space various classification algorithms including SVM, feature subset forward selection, Scatter matrix, and a simple linear classifier using principal component analysis and Mahanabolis distance were investigated

A Local Approach for Robust Optical Flow Estimation under Varying Illumination

The problem of motion estimation, in general, is made difficult by large illumination variations and by motion discontinuities. In recent papers, we and others have proposed global approaches to deal with both problems simultaneously within the regularization framework. A major drawback of such global methods is that several regularization parameters responsible for the integration of the illumination and motion components need to be determined in advance. This has reduced the applicability of global methods. In this paper, a parameter-free local approach, which solves a linear regression problem using a simple parametric model, is presented. To achieve robustness for the linear regression problem, we introduce a modified version of the least median of squares algorithm. We show quantitative error comparisons between the results obtained by our local approach and those produced by several global methods. Our results show that our local method is comparable to the best results obtained by the global approaches yet does not require any manual selection of parameters.

Global optimization methods for full-reference and no-reference motion estimation with applications to atherosclerotic plaque motion and strain imaging

Pixel-based motion estimation using optical flow models has been extensively researched during the last two decades. The driving force of this research field is the amount of applications that can be developed with the motion estimates. Image segmentation, compression, activity detection, object tracking, pattern recognition, and more recently non-invasive biomedical applications like strain imaging make the estimation of accurate velocity fields necessary. The majority of the research in this area is focused on improving the theoretical and numerical framework of the optical flow models. This effort has resulted in increased method complexity with an increasing number of motion parameters. The standard approach of heuristically setting the motion parameters has become a major source of estimation error. This dissertation is focused in the development of reliable motion estimation based on global parameter optimization methods. Two strategies have been developed. In full-reference optimization, the assumption is that a video training set of realistic motion simulations (or ground truth) are available. Global optimization is used to calculate the best motion parameters that can then be used on a separate set of testing videos. This approach helps provide bounds on what motion estimation methods can achieve. In no-reference optimization, the true displacement field is not available. By optimizing for the agreement between different motion estimation techniques, the no-reference approach closely approximates the best (optimal) motion parameters. The results obtained with the newly developed global no-reference optimization approach agree closely with those produced with the full-reference approach. Moreover, the no-reference approach calculates velocity fields of superior quality than published results for benchmark video sequences. Unreliable velocity estimates are identified using new confidence maps that are associated with the disagreement between methods. Thus, the no-reference global optimization method can provide reliable motion estimation without the need for realistic simulations or access to ground truth. The methods developed in this dissertation are applied to ultrasound videos of carotid artery plaques. The velocity estimates are used to analyze plaque motion and produce novel non-invasive elasticity maps that can help in the identification of vulnerable atherosclerotic plaques