This paper presents MOCT, a multi-object chromatic tracking technique for real-time natural video processing. Its main step is the MOCT localization algorithm, that performs local data evaluations in order to apply a multiple output parallel reduction operator to the image. The reduction operator is used to localize the positions of the object centroids, to compute the number of pixels occupied by an object and its bounding boxes, and to update object trajectories in image space. The operator is analyzed using three different computation layouts and tested over several reduction factors. 1

We propose a novel approach for generating video interpolating frames from optical flow information. Our approach is based on solving an affine image-matching equation via the Green's function method. This method provides a pair of filters that, when applied on each frame of an input video signal, allows the generation of a virtual sequence that creates extra frames. Our technique provides smooth reconstruction of video, specifically of sequences created through camera translation or translation of objects in a otherwise static scene.

We describe a new technique that uses active scene illumination to perform foreground-background segmentation and recover partial HDR information. We explore the fact that relative tones can be recovered by varying illumination intensity, without knowing the camera response function. In our approach, the scene is illuminated with an uncalibrated projector and two images of the scene are captured under different illumination conditions. By taking advantage of the fact that the projector can be set up to illuminate only the foreground, we are able to distinguish the foreground from the background. The output of our system is a segmentation mask, together with a image with additional tonal information for the foreground pixels. As an application, we show how to produce spatially variant tone mapped images, where background and foreground receive different treatments. The segmentation and the visualization algorithms are implemented in real-time, and can be used to produce range-enhanced video sequences.

We propose a new technique that provides a unified framework for modeling the collision dynamics of objects having arbitrary shape and rugosity. The technique assumes that boundary of the object is expressed by elementary segments with some random distribution of perturbation. Collision detection and treatment is done considering this conceptual representation. In order to efficiently detect collisions between irregular objects, we introduce the augmented oriented bounded box tree.