Abstract. Most biological approaches to disparity extraction rely on the disparity energy model (DEM). In this paper we present an alternative approach which can complement the DEM model. This approach is based on the multiscale coding of lines and edges, because surface structures are composed of lines and edges and contours of objects often cause edges against their background. We show that the line/edge approach can be used to create a 3D wireframe representation of a scene and the objects therein. It can also significantly improve the accuracy of the DEM model, such that our biological models can compete with some state-of-the-art algorithms from computer vision.
The communication between people with normal hearing with those having hearing or speech impairment is difficult. Learning a new alphabet is not always easy, especially when it is a sign language alphabet, which requires both hand skills and practice. This paper presents the GyGSLA system, standing as a completely portable setup created to help inexperienced people in the process of learning a new sign language alphabet. To achieve it, a computer/mobile game-interface and an hardware device, a wearable glove, were developed. When interacting with the computer or mobile device, using the wearable glove, the user is asked to represent alphabet letters and digits, by replicating the hand and fingers positions shown in a screen. The glove then sends the hand and fingers positions to the computer/mobile device using a wireless interface, which interprets the letter or digit that is being done by the user, and gives it a corresponding score. The system was tested with three completely inexperience sign language subjects, achieving a 76 % average recognition ratio for the Portuguese sign language alphabet.
Empirical studies concerning face recognition suggest that faces may be stored in memory by a few canonical representations. In cortical area V1 exist double-opponent colour blobs, also simple, complex and end-stopped cells which provide input for a multiscale line/edge representation, keypoints for dynamic feature routing, and saliency maps for Focus-of-Attention. All these combined allow faces to be segregated. Events of different facial views are stored in memory and combined to identify the view and recognise a face, including its expression. In this paper, the authors show that with five 2D views and their cortical representations it is possible to determine the left-right and frontal-lateral-profile views, achieving a view-invariant recognition rate of 91%. The authors also show that the same principle with eight views can be applied to 3D object recognition when they are mainly rotated about the vertical axis.
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