Gaze control for visually guided manipulation

Human studies have shown that gaze shifts are mostly driven by the task. One explanation is that fixations gather information about task relevant properties, where task relevance is signalled by reward. This thesis pursues primarily an engineering science goal to determine what mechanisms a rational decision maker could employ to select a gaze location optimally, or near optimally, given limited information and limited computation time. To do so we formulate and characterise three computational models of gaze shifting (implemented on a simulated humanoid robot), which use lookahead to imagine the informational effects of possible gaze fixations. Our first model selects the gaze that most reduces uncertainty in the scene (Unc), the second maximises expected rewards by reducing uncertainty (Rew+Unc), and the third maximises the expected gain in cumulative reward by reducing uncertainty (Rew+Unc+Gain). We also present an integrated account of a visual search process into the Rew+Unc+Gain gaze scheme. Our secondary goal is concerned with the way in which humans might select the next gaze location. We compare the hand-eye coordination timings of our models to previously published human data, and we provide evidence that only the models that incorporate both uncertainty and reward (Rew+Unc and Rew+Unc+Gain) match human data

Visual Routines for Autonomous Driving

The paper describes visual routines based on models of color and shape, as well as crucial issues involving the scheduling of such routines. The visual routines are developed in a unique platform. The view from a car driving in a simulated world is fed into a Datacube pipeline video processor. The use of this simulation provides a flexible environment from which to set crucial image processing parameters of the individual routines. In addition to the simulations, the routines are also tested in similar images generated by driving in the real world, to assure the generalizability of the simulation. 1 Introduction The advent of faster processors has moved the focus of computer vision from analysis of a single image to dealing with long sequences of images and, more important, extracting the parts of those images that are needed for behaviors. Nowhere is this more apparent than in the application of automated driving. Steering corrections in very complex environments normally have to be..