Abstract
The two-dimensional (2D) kinematic approach is by far the most popular technique in rat gait analysis. This is a simple inexpensive procedure, which requires only one camera to record the movement. However, maximal precision and accuracy of the kinematic values are expected when the experimental protocol includes a three-dimensional (3D) motion analysis methodology: Locomotor speed is a basic kinematic parameter that is often neglected in most studies of movement disorders and neurological diseases. Because locomotor speed c:an act as confounder for the interpretation of the obtained results we also focused our attention on the relation between speed and 3D hindlimb kinematics. Our experimental set-up consisted of a motion capture system with four CMOS cameras which allowed a non-invasive estimation of the instantaneous position of color markers in a 3D measurement volume. Data were recorded while rats walked at different treadmill speeds (30 vs 60 cm/s). For the first time we reported detailed kinematic data for the sagittal, coronal and transverse plane during treadmill locomotion in rats. Despite the overall time course patterns of the curves were identical, we found significant differences between values of joint angular motion at 30 and 60 cm/s at selected points of the step cycle. The adaptation to higher treadmill walking included greater joint angular excursions. The present report highlights the importance of walking speed when evaluating rat hindlimb kinematics during gait. Hopefully, this study will be useful in experimental data assessment when multiple gait abnormalities are expected to occur in all planes. of motion.

Abstract

Abstract
The Department of Biology of the University of Tras-os-Montes e Alto Douro analyses every year a large number of chestnut-tree leaves, in order to measure their biometric characteristics, namely the leaf area, dimensions of the enclosing rectangle, number of teeth and secondary veins. Because for a human operator this is a time consuming and error prone task, a computer vision system has been set up to improve the process. The task of measuring the leaf presents no major problems, while counting the number of teeth and secondary veins has proved to be complex at the resolutions used. This paper describes the state of the project, going into some detail on the algorithms. A complete system has been assembled, based on an PC connected to an imaging system. A windows-based application has been developed, which integrates the control of the operations to grab, store and analyse images of different varieties of chestnut-tree leaves in an organised way. Because the accuracy of the computer vision algorithms used is not sufficient for the system to be completely autonomous, a semi-automatic solution has been adopted. The operator validates or corrects the results of the automatic analysis. This solution leads to a significant improvement in the performance of the human operator, both in terms of speed and quality of the results.

Abstract
This study presents a method to measure the displacement fields on the surface of planar objects with sub-pixel resolution, by combining image correlation with a differential technique. First, a coarse approximation of the pixel level displacement is obtained by cross-correlation (CC). Two consecutive images, taken before and after the application of a given deformation, are recursively split in sub-images, and the CC coefficient is used as the similarity measure. Secondly, a fine approximation is performed to assess the sub-pixel displacements by means of an optical flow method based on a differential technique. To validate the effectiveness and robustness of the proposed method, several numerical tests were carried out on computer-generated images. Moreover, real images from a static test were also processed for estimating the displacement resolution. The results were compared with those obtained by a commercial digital image correlation code. Both methods showed similar and reliable results according to the proposed tests.

Abstract
Objectives: To characterize and compare dynamic joint stiffness (DJS) of the ankle in the sagittal plane during natural cadence walking in both genders. Design: Observation, cross-sectional and matched pairs. Participants: Twenty-one males (mean age = 27 +/- 4.2 years) and 18 females (mean age = 22.9 +/- 4.1 years). Setting: Laboratory at the University de Tras-os-Montes and Alto Douro. The DJS of the ankle was assessed while participants performed barefoot walking at a natural cadence, was measured using a force platform (1000 Hz), and synchronized with a high-speed camera (200 Hz). Main outcome measures: DJS of the ankle in males and females was calculated in two sub-phases (the second and the fourth sub-phases) of the stance phase. The Wilcoxon non-parametric matched-pairs test and the Mann-Whitney U non-parametric test were used to analyse difference of DJS of the ankle within and between the groups with p <= 0.05 regarded as significant. Results: Male and female participants demonstrated significantly (p<0.005) less DJS during the second sub-phase. DJS was not significantly (p>0.063) different between females (0.0511 N m/kg/degrees) and males (0.0596 N m/kg/degrees) during the second sub-phase. DJS was significantly (p<0.001) higher in males (0.0844 N m/kg/degrees) than females (0.0691 N m/kg/degrees) during the fourth sub-phase. Conclusions: Gender-specific DJS characteristics demonstrated by females in this study may be used in preventative training programs designed to promote a greater ability to use contractile components to produce mechanical energy through the ankle.

Abstract
This paper presents a system which extends the use of the traditional white cane by the blind for navigation purposes in indoor environments. Depth data of the scene in front of the user is acquired using the Microsoft Kinect sensor which is then mapped into a pattern representation. Using neural networks, the proposed system uses this information to extract relevant features from the scene, enabling the detection of possible obstacles along the way. The results show that the neural network is able to correctly classify the type of pattern presented as input.