Abstract
The present paper describes the Portuguese National Brain Imaging Network designed to join R&D efforts of four Portuguese universities (Aveiro, Coimbra, Minho and Porto) in this emergent scientific area. This is an open initiative, already funded in 81.3% of its predicted investment (similar to 4.3 million E) for the first 5 years of operation, opened to the participation of other national institutions. This area of neuroscience uses several types of datasets from different medical imaging modalities and biosignals. MRI/MRS and fMRI volumes along with high-resolution EEG are our main targets for the first 5 years of operation and can easily reach the GByte size for a patient study. The Brain Imaging Network Grid (BING) will provide the support to a "neuroscientist-friendly" web portal where neuroscientists can submit brain imaging datasets for different analysis protocols. We will focus the present paper on the description of the consortium, its objectives and the network and Grid services architecture designs that will provide both the computational resources and the federated large data repository for the Portuguese national wide neuroscience scientific community.

Abstract
Automatic biometric identification based on fingerprints is still one of the most reliable identification method in criminal and forensic applications. A critical step in fingerprint analysis without human intervention is to automatically and reliably extract singular points from the input fingerprint images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. Poincaré Index-based methods are one of the most common for singular points detection. However, these methods usually result in many spurious detections. Therefore, we propose an enhanced version of the method presented by Zhou et al. [13] that introduced a feature called DORIC to improve the detection. Our principal contribution lies in the adoption of a smoothed orientation field and in the formulation of a new algorithm to analyze the DORIC feature. Experimental results show that the proposed algorithm is accurate and robust, giving better results than the best reported results so far, with improvements in the range of 5% to 7%. © 2009 IEEE.

Abstract
The Cellular Neural Networks (CNN) model is now a paradigm of cellular analogue programmable multidimensional processor array with distributed local logic and memory. CNNs consist of many parallel analogue processors computing in real time. One desirable feature is that these processors arranged in a two dimensional grid only have local connections, which lend themselves easily to VLSI implementations. In this paper, we present a new algorithm for motion estimation using 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
This paper presents the implementation of an optical flow algorithm on a pipeline image processor The overall optical flow computation method is presented and evaluated on a common set of image sequences. Results are compared to other implementations according to two different error measures. Due to its deterministic architecture, this implementation achieves very low computation delays that allow it to operate at standard video frame-rate and resolutions. It compares favorably to recent implementations in parallel hardware.

Abstract
In this paper we present an evaluation of optical flow techniques applied to a case study in the perception of visual motion. This case study is being conducted in a project for the evaluation of human factors in road traffic, specifically, concerning the processing of visual information. We present the goals of the case study, discuss the need to apply optical flow techniques to synthesized image sequences and evaluate some limitations encountered in their use. © 1996 IEEE.

Abstract
Optical flow algorithms generally demand for high computational power and huge storage capacities. This paper is a contribution for real-time implementation of an optical flow algorithm on a pipeline machine. This overall optical flow computation methodology is presented and evaluated on a set of synthetic and real image sequences. Results are compared to other implementations using as measures the average angular error, the optical flow density and the root mean square error. The proposed implementation achieves very low computation delays, allowing operation at standard video frame-rate and resolution. It compares favorably to recent implementations in standard microprocessors and in parallel hardware.