António José Ribeiro Neves

By Antonio J. R. Neves and Armando J. Pinho

Part of the book: Signal Processing

By Armando J. Pinho, Antonio J. R. Neves, Daniel A. Martins, Carlos A. C. Bastos and Paulo J. S. G. Ferreira

Part of the book: Signal Processing

By Luís M. O. Matos, António J. R. Neves and Armando J. Pinho

The use of medical imaging has increased in the last years, especially with magnetic resonance imaging (MRI) and computed tomography (CT). Microarray imaging and images that can be extracted from RNA interference (RNAi) experiments also play an important role for large-scale gene sequence and gene expression analysis, allowing the study of gene function, regulation, and interaction across a large number of genes and even across an entire genome. These types of medical image modalities produce huge amounts of data that, for several reasons, need to be stored or transmitted at the highest possible fidelity between various hospitals, medical organizations, or research units.

Part of the book: Applications of Digital Signal Processing through Practical Approach

By Ricardo Ribeiro, Daniel Lopes and António Neves

In the past few years, face recognition has received great attention from both research and commercial communities. Areas such as access control using face verification are dominated by solutions developed by both the government and the industry. In this chapter, a face verification solution is presented using open-source algorithms for access control of large-scale events under unconstrained environments. From the type of camera calibration to the algorithms used for face detection and recognition, every stage has a proposed solution. Tests using the proposed solutions in the entrance of a building were made in order to test and compare each solution proposed.

Part of the book: Intelligent Video Surveillance

By Ronaldo Ferreira, Joaquim José de Castro Ferreira and António José Ribeiro Neves

The objective of this work is to present an object tracking algorithm developed from the combination of random tree techniques and optical flow adapted in terms of Gaussian curvature. This allows you to define a minimum surface limited by the contour of a two-dimensional image, which must or should not contain a minimum amount of optical flow vector associated with the movement of an object. The random tree will have the purpose of verifying the existence of superfluous vectors of optical flow by discarding them, defining a minimum number of vectors that characterizes the movement of the object. The results obtained were compared with those of the Lucas-Kanade algorithms with and without Gaussian filter, Horn and Schunk and Farneback. The items evaluated were precision and processing time, which made it possible to validate the results, despite the distinct nature between the algorithms. They were like those obtained in Lucas and Kanade with or without Gaussian filter, the Horn and Schunk, and better in relation to Farneback. This work allows analyzing the optical flow over small regions in an optimal way in relation to precision (and computational cost), enabling its application to area, such as cardiology, in the prediction of infarction.

Part of the book: Information Extraction and Object Tracking in Digital Video