We investigate the application of genetic algorithms (GAs) for recognizing real two-dimensional (2-D) or three-dimensional (3-D) objects from 2-D intensity images, assuming that th...
George Bebis, Evangelos A. Yfantis, Sushil J. Loui...
Abstract. We describe a system for autonomous learning of visual object representations and their grasp affordances on a robot-vision system. It segments objects by grasping and mo...
Dirk Kraft, Renaud Detry, Nicolas Pugeault, Emre B...
Recognition of specular objects is particularly difficult because their appearance is much more sensitive to lighting changes than that of Lambertian objects. We consider an appr...
Probabilistic relational models are an efficient way to learn and represent the dynamics in realistic environments consisting of many objects. Autonomous intelligent agents that gr...
We present a probabilistic framework for recognizing objects in images of cluttered scenes. Hundreds of objects may be considered and searched in parallel. Each object is learned f...