Spatio-temporal Image Derivatives Research Articles

Design and Use of Linear Models for Image Motion Analysis

Linear parameterized models of optical flow, particularly affine models, have become widespread in image motion analysis. The linear model coefficients are straightforward to estimate, and they provide reliable estimates of the optical flow of smooth surfaces. Here we explore the use of parameterized motion models that represent much more varied and complex motions. Our goals are threefold: to construct linear bases for complex motion phenomenas to estimate the coefficients of these linear modelss and to recognize or classify image motions from the estimated coefficients. We consider two broad classes of motions: i) generic “motion features” such as motion discontinuities and moving barss and ii) non-rigid, object-specific, motions such as the motion of human mouths. For motion features we construct a basis of steerable flow fields that approximate the motion features. For object-specific motions we construct basis flow fields from example motions using principal component analysis. In both cases, the model coefficients can be estimated directly from spatiotemporal image derivatives with a robust, multi-resolution scheme. Finally, we show how these model coefficients can be use to detect and recognize specific motions such as occlusion boundaries and facial expressions.

Real-Time Tracking of Moving Objects with an Active Camera

This article is concerned with the design and implementation of a system for real-time monocular tracking of a moving object using the two degrees of freedom of a camera platform. Figure-ground segregation is based on motion without making anya prioriassumptions about the object form. Using only the first spatiotemporal image derivatives, substraction of the normal optical flow induced by camera motion yields the object image motion. Closed-loop control is achieved by combining a stationary Kalman estimator with an optimal Linear Quadratic Regulator. The implementation on a pipeline architecture enables a servo rate of 25 Hz. We study the effects of time-recursive filtering and fixed-point arithmetic in image processing and we test the performance of the control algorithm on controlled motion of objects.

Robust egomotion estimation from the normal flow using search subspaces

We address the problem of egomotion estimation for a monocular observer moving under arbitrary translation and rotation, in an unknown environment. The method we propose is uniquely based on the spatio-temporal image derivatives, or the normal flow. We introduce a search paradigm which is based on geometric properties of the normal flow field, and consists in considering a family of search subspaces to estimate the egomotion parameters. Various algorithms are proposed within this framework. In order to decrease the noise sensitivity of the estimation methods, we use statistical tools, based on robust regression theory. Finally, we present and discuss a wide variety of experiments with synthetic and real images, for various kinds of camera motion.