Evaluation of attentional control in active systems using a 3D simulation framework

In active vision systems, attentional control is used to determine the relevant parts of a scene and to direct perception towards these parts. To test and evaluate active vision systems, we have implemented a 3D simulation framework capable of simulating a broad scope of environments from simple block worlds to complex photorealistic scenes. The simulator allows full control of all aspects of the simulation, acting and moving inside virtual environments. In this paper, we demonstrate its use for evaluating our attentional control system. The attention model is based on a novel two-stage selection mechanism and especially focuses on the dynamic and three-dimensional aspects of its environment

Perceiving spatially inseparable objects : evidence for feature-based object selection not mediated by location

In 4 experiments, stimulus elements were arranged into an LED-like array, and letters were defined within the array by feature similarity between the elements with respect to color and form. These stimuli allowed the display of a target and a distractor letter simultaneously at the same location. They were spatially inseparable but could be separated in feature space. Participants had to identify the letter on a prespecified feature dimension (color or form). As a result, the distractors produced specific compatibility effects. This showed that non-target features could not be ignored at early stage (i. e., that color and form were processed automatically and in parallel up to a high stage). The target was selected from the resulting objects according to the prespecified feature dimension. Results demonstrate that object selection is possible without selecting absolute spatial arrays

Selecting what is Important: Training Visual Attention

Abstract. We present a new, sophisticated algorithm to select suitable training images for our biologically motivated attention system VOCUS. The system detects regions of interest depending on bottom-up (scenedependent) and top-down (target-specific) cues. The top-down cues are learned by VOCUS from one or several training images. We show that our algorithm chooses a subset of the training set that outperforms both the selection of one single image as well as simply using all available images for learning. With this algorithm, VOCUS is able to quickly and robustly detect targets in numerous real-world scenes. 1 Introduction and State of the Art Human visual perception is based on a separation of object recognition into two subtasks [11]: first, a fast parallel pre-selection of scene regions detects object candidates and second, complex recognition restricted to these regions verifies or falsifies the hypothesis. This dichotomy of fast localization processes an