Browsing by Author "Lobato, D."
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- A fast neural-dynamical approach to scale-invariant object detectionPublication . Terzic, Kasim; Lobato, D.; Saleiro, Mário; du Buf, J. M. H.We present a biologically-inspired method for object detection which is capable of online and one-shot learning of object appearance. We use a computationally efficient model of V1 keypoints to select object parts with the highest information content and model their surroundings by a simple binary descriptor based on responses of cortical cells. We feed these features into a dynamical neural network which binds compatible features together by employing a Bayesian criterion and a set of previously observed object views. We demonstrate the feasibility of our algorithm for cognitive robotic scenarios by evaluating detection performance on a dataset of common household items. © Springer International Publishing Switzerland 2014.
- Biological models for active vision: towards a unified architecturePublication . Terzic, Kasim; Lobato, D.; Saleiro, Mário; Martins, Jaime; Farrajota, Miguel; Rodrigues, J. M. F.; du Buf, J. M. H.Building a general-purpose, real-time active vision system completely based on biological models is a great challenge. We apply a number of biologically plausible algorithms which address different aspects of vision, such as edge and keypoint detection, feature extraction,optical flow and disparity, shape detection, object recognition and scene modelling into a complete system. We present some of the experiments from our ongoing work, where our system leverages a combination of algorithms to solve complex tasks.
- Biologically Inspired Vision for Indoor Robot NavigationPublication . Saleiro, Mário; Terzic, Kasim; Lobato, D.; Rodrigues, J. M. F.; du Buf, J. M. H.Ultrasonic, infrared, laser and other sensors are being applied in robotics. Although combinations of these have allowed robots to navigate, they are only suited for specific scenarios, depending on their limitations. Recent advances in computer vision are turning cameras into useful low-cost sensors that can operate in most types of environments. Cameras enable robots to detect obstacles, recognize objects, obtain visual odometry, detect and recognize people and gestures, among other possibilities. In this paper we present a completely biologically inspired vision system for robot navigation. It comprises stereo vision for obstacle detection, and object recognition for landmark-based navigation. We employ a novel keypoint descriptor which codes responses of cortical complex cells. We also present a biologically inspired saliency component, based on disparity and colour.