Velocity selective filters recursively implemented in the spatiotemporal domain.

Energy-based methods for motion estimation in image sequences process the input data either in the spatiotemporal or in the frequency domain. In both cases, the algorithms already described in the literature often require a huge number of elementary operations. In this paper, we describe a class of velocity selective filters which yield an accurate detection of the edges moving in the sequence.

Gaussian neural networks for glass bottles inspection: a learning procedure.

In glass bottle inspection, the defects detection is of first importance. For online system detection, high speed and robust detection of faults are highly required. Neural networks have recently, and successfully, been applied to fault detection in many manufacturing processes.

Fully automated screening procedure for early detection of visual field defects.

A major problem in mass screening for glaucoma is the substantial time required to detect areas of depressed visual sensitivity, scotoma. We have developed a novel instrument for testing rapidly and automatically the sensitivity of the visual field. The patient views a large CRT screen on which up to four luminous points are presented in a prearranged pattern.

A fast algorithm for nonparametric probability density estimation.

A fast algorithm for the well-known Parzen window method to estimate density functions from the samples is described. The computational efforts required by the conventional and straightforward implementation of this estimation procedure limit its practical application to data of low dimensionality. The proposed algorithm makes the computation of the same density estimates with a substantial reduction of computer time possible, especially for data of high dimensionality.