End-stopped cells in cortical area V1, which combine outputs of complex cells tuned to different orientations, serve to detect line and edge crossings, singularities and points with large curvature. These cells can be used to construct retinotopic keypoint maps at different spatial scales (Level-of-Detail). The importance of the multi-scale keypoint representation is studied in this paper. It is shown that this representation provides very important information for object recognition and face detection. Different grouping operators can be used for object segregation and automatic scale selection. Saliency maps for Focus-of-Attention can be constructed. Such maps can be employed for face detection by grouping facial landmarks at eyes, nose and mouth. Although a face detector can be based on processing within area V1, it is argued that such an operator must be embedded into dorsal and ventral data streams, to and from higher cortical areas, for obtaining translation-, rotation-and scale-invariant detection.

a b s t r a c tIn this paper we present an improved model for line and edge detection in cortical area V1. This model is based on responses of simple and complex cells, and it is multi-scale with no free parameters. We illustrate the use of the multi-scale line/edge representation in different processes: visual reconstruction or brightness perception, automatic scale selection and object segregation. A two-level object categorization scenario is tested in which pre-categorization is based on coarse scales only and final categorization on coarse plus fine scales. We also present a multi-scale object and face recognition model. Processing schemes are discussed in the framework of a complete cortical architecture. The fact that brightness perception and object recognition may be based on the same symbolic image representation is an indication that the entire (visual) cortex is involved in consciousness.

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Abstract.Over the past few years, there has been a growing interest in using robots in education. The use of these tangible devices in combination with problem-based learning activities results in more motivated students, higher grades and a growing interest in the STEM areas. However, most educational robotics systems still have some restrictions like high cost, long setup time, need of installing software in children's computers, etc. We present a new, Iow-cost, classroom-oriented educational robotics system that does not require the installation of any software. It can be used with computers, tablets or smartphones. It also supports multiple robots and the system can be setup and is ready to be used in under 5 minutes. The robotics system that will be presented has been successfully used by two classes of 3rd and 4th graders. Besides improving mathematical reasoning, the system can be employed as a motivational tool for any subject.

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The application of the most recent technologies is fundamental to add value to tourism experiences, as well as in other economic sectors. Mobile Five Senses Augmented Reality (M5SAR) system is a mobile guide instrument for cultural, historical, and museum events. In order to realize the proclaimed five senses, the system has two main modules: a (i) mobile application which deals mainly with the senses of sight and hearing, using for that the mobile device camera to recognize and track on-the-fly (museum's) objects and give related information about them; and a (ii) portable device capable of enhancing the augmented reality (AR) experience to the full five senses through the stimulus of touch, taste, and smell, by associating itself to the users' smartphone or tablet. This paper briefly presents the system's architecture but, the main focus is on the analysis of the users' acceptance for this technology, namely the AR (software) application, and its integration with the (hardware) device to achieve the five senses AR. Results show that social influence, effort expectancy, and facilitating conditions are the key constructs that drive the users to accept and M5SAR's technology.

Abstract-Depth information using the biological Disparity Energy Model can be obtained by using a population of complex cells. This model explicitly involves cell parameters like their spatial frequency, orientation, binocular phase and position difference. However, this is a mathematical model. Our brain does not have access to such parameters, it can only exploit responses. Therefore, we use a new model for encoding disparity information implicitly by employing a trained binocular neuronal population. This model allows to decode disparity information in a way similar to how our visual system could have developed this ability, during evolution, in order to accurately estimate disparity of entire scenes.