Contribution: Open Source Computer Vision Library (OpenCV) Basics is an application designed with the purpose of facilitating the initiation of industrial engineering students in the field of Computer Vision, making the learning process easier, more dynamic and more direct. To this end, an application has been developed for the Android operating system with which users can make use of a wide variety of algorithms available in the OpenCV library. Background: Teaching topics related to Computer Vision can rely on the use of new technologies such as mobile applications. With this type of support, students can learn concepts that might otherwise be difficult to understand. Intended Outcomes: The objective is to facilitate the assimilation of concepts related to Computer Vision by taking advantage of the camera and the processing power of a mobile device to observe in real time the effects produced on an image by many of the image processing algorithms included in OpenCV. This application is currently available to be downloaded for free through the Google Play Store so that anyone interested in the field of Computer Vision can make use of it. Application Design: The proposed approach introduces students to concepts related to Computer Vision by making use of the developed application, complementing the theoretical contents taught by the teacher with specific examples. Findings: The degree of satisfaction of OpenCV Basics users has been evaluated within the framework of the course advanced robotized systems, taught in the industrial engineering degree at the University of La Laguna.

An accurate method to detect obstacles and dangerous areas is the key to the safe performance of autonomous robots. Time of flight sensors can report their existence through the emission, reflection, and measurement of wave patterns, but large wavelength light projection is often unreliable in outdoors environments, due to solar radiation contamination. In this paper, a specific Microsoft Kinect arrangement on a robotic vehicle is proposed, such that outdoors detection is possible. The main contribution of this paper is the description of a sequence of filtering techniques, which translate the depth image provided by the sensor into definite obstacle projections in the navigability map used by the vehicle. A series of experiments proves that the Kinect device is more accurate at detecting obstacles using this procedure than a camera pair using two different stereovision techniques.

In this paper, a study of the odometric system for the autonomous cart Verdino, which is an electric vehicle based on a golf cart, is presented. A mathematical model of the odometric system is derived from cart movement equations, and is used to compute the vehicle position and orientation. The inputs of the system are the odometry encoders, and the model uses the wheels diameter and distance between wheels as parameters. With this model, a least square minimization is made in order to get the nominal best parameters. This model is updated, including a real time wheel diameter measurement improving the accuracy of the results. A neural network model is used in order to learn the odometric model from data. Tests are made using this neural network in several configurations and the results are compared to the mathematical model, showing that the neural network can outperform the first proposed model.

A new method for automatic optic disc localization and segmentation is presented. The localization procedure combines vascular and brightness information to provide the best estimate of the optic disc center which is the starting point for the segmentation algorithm. A detection rate of 99.58% and 100% was achieved for the Messidor and ONHSD databases, respectively. A simple circular approximation to the optic disc boundary is proposed based on the maximum average contrast between the inner and outer ring of a circle centered on the estimated location. An average overlap coefficient of 0.890 and 0.865 was achieved for the same datasets, outperforming other state of the art methods. The results obtained confirm the advantages of using a simple circular model under non-ideal conditions as opposed to more complex deformable models.