Abstract
Face detection is a problem in image processing that has been extensively studied in the last decades. This fact is justified due to the innumerous applications this subject has in computer vision. In this paper we present a technique to detect a face in an image where several persons may be. The images were recorded from news broadcasts from different networks. First, a model based on normalized RGB color space was built to define a skin color model: skin regions were selected from several face images and extracted statistical characteristics. Second, each pixel of the image was classified in two categories: skin and not skin. Third, the face region was identified. A MATLAB function was created to detect faces of any size, achieving high detection rates.

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
An increasing ageing society and consequently rising number of post-stroke related neurological dysfunction patients are forcing the rehabilitation field to adapt to ever-growing demands. In parallel, an unprecedented number of research efforts and technological solutions meant for human monitoring are continuously influencing traditional methodologies, causing paradigm shifts; extending the therapist patient dynamics. Compensatory movements can be observed in post-stroke patient when performing functional tasks. Although some controversy remains regarding the functional benefits of compensatory movement as a way of accomplish a given task, even in the presence of a motor deficit; studies suggest that such maladaptive strategies may limit the plasticity of the nervous system to enhance neuro-motor recovery. This preliminary study intends to aid in the development of a system for compensatory movement detection in stroke patients through the use of accelerometry data. A post-stroke patients group is presented and discussed, instructed to perform reach and press movements while sensors were positioned at different location on the arm, forearm and trunk, in order to assess sensor positioning influence. Results suggest that P1 is advantageous for compensatory elevation movement detection at the shoulder; P4 seems the most appropriate for detecting the abduction; and P5 presents a reasonable sensitivity for detection of anteriorization and rotation of the trunk.

Abstract
Advances in sensor technology, electronic textile integration, and integrated circuits have introduced a paradigm shift in the way most researchers approach signal monitoring. In recent years, devices such as body sensor networks (BSN) allow for direct on-body physiological and biomechanical parameters measurements. Such technology allows for a more in depth analysis of an athlete's performance, without affecting the results due to awkward wires or uncomfortable carry-on devices. Miniaturization and other achievements allow a more seamless interaction with the individual, permitting a more natural behaviour during the monitoring session. The project BIOSWIM (Body Interface System based on Wearable Integration Monitorization) is a joint multidisciplinary effort of a number of Portuguese universities which seeks a pervasive monitoring solution for performance, physiological and biomechanical signals from a swimmer under normal training conditions. In order to achieve such an undertaking a swimsuit prototype was developed with truly integrated EKG textile sensors; which will work in conjunction with a wearable inertial monitoring unit (WIMU) and a wearable chemical monitoring unit. This article focuses on the WIMU, which serves as the biomechanical data processing unit of the system.

Abstract
In this paper we will describe our experiments with x-ray image analysis for vertebra detection in juvenile/adolescent patients with idiopathic scoliotic spines. We will focus on detecting vertebrae location in a anterior-posterior x-ray image in a fully automatic way. For accomplishing this, we propose a set of techniques for (i) isolating the spine by removing other bone structures (e.g. ribs), (ii) detecting vertebrae location along the spine using an hierarchical and progressive threshold analysis, and (iii) detecting vertebrae lateral boundaries.