Abstract

Abstract

Abstract

Abstract
Important aspects of the implementation and calibration of a 3-D data acquisition system based on the ratio of two intensity images are described. These two images are obtained by illuminating the scene with a projector whose light is filtered, successively, by a graded and a constant neutral density filter. Calibration steps, involving the determination of camera and light projector geometric parameters and ratio calibration data, are analyzed. A new ratio calibration procedure is proposed. Results of two depth calculation methods are presented, and system accuracy is evaluated for several test scenes.

Abstract
In this paper, we present a new algorithm for motion flow field estimation using Cellular Neural Networks (CNN). We start from a mathematical viewpoint (i.e. statistical regularisation based on Markov Random Field (MRF) and proceed by mapping the algorithm onto a cellular neural network. Because of the temporal dynamics inherent in the cells of the CNN it is well suited to processing time-varying images. A robust motion estimation algorithm is achieved by using a spatio-temporal neighbourhood for modelling pixel interactions.

Abstract
In this paper we propose an image segmentation algorithm that combines region merging with spectral-based techniques. An initial partitioning of the image into primitive regions is produced by applying a region merging approach which produces a chunk graph that takes in attention the image gradient magnitude. This initial partition is the input to a computationally efficient region segmentation process that produces the final segmentation. The latter process uses a multi-class partition that minimizes the normalized cut value for the region graph. We have efficiently applied the proposed approach with good visual and objective segmentation quality results.