This paper addresses the problem of motion estimation, selective reconstruction, and motion-based segmentation of objects undergoing rotational and translational motion and expansion, contraction, or shear type deformation. The goal for motion estimation and selective reconstruction is to identify the motion parameters of different moving objects in the scene and derive the location of the object with a specific motion parameter at each time instance. The information so obtained can be used in various autonomous tasks that have to be completed visually, for example, visual navigation of a mobile robot or tracking a moving part on a conveyor belt. An important objective of motion based segmentation is to simultaneously estimate the motion parameters of edges, on an image, together with their corresponding locations. Based upon position and velocity information of moving edges on the image, motion based clustering and segmentation can be readily performed. The algorithms that we have developed are based on 2D + T filters that are tuned to a specific angular velocity and translational velocity. Additionally, we propose that the problem of estimating various deformational parameters can be reduced to that of computing the parameters of a translational motion by log-sampling the image. Thus, one computes the deformational parameters and the magnitude and location of an edge undergoing deformation using translational velocity tuned filters. Additionally, the algorithm we propose can be applied equally well to both spinning and orbiting motion, unifying the treatment. A specific advantage of applying tuned filters in motion parameter estimation is that various different estimation problems can be decoupled, viz. rotational motion and translational motion parameters can be computed separately. The other important advantage is that they are not based on point correspondence between one image and the next. The algorithms we propose have been tested on both real image sequences and synthesized image sequences corrupted by noise, and are shown to be accurate and robust against noise and occlusion.
- Motion estimation
- Velocity tuned filters