Visual search in depth

Visual search in depth

The accuracy of saccade localisation during visual search was examined for a search target defined by the single features of orientation or depth or by a conjunction of the two features. Subjects were required to move their eyes to the target which appeared in one of eight possible locations, arranged circularly around fixation, with non-targets filling the remaining seven positions. Search for...

Synchronized Audio-Visual Transients Drive Efficient Visual Search for Motion-in-Depth

In natural audio-visual environments, a change in depth is usually correlated with a change in loudness. In the present study, we investigated whether correlating changes in disparity and loudness would provide a functional advantage in binding disparity and sound amplitude in a visual search paradigm. To test this hypothesis, we used a method similar to that used by van der Burg et al. to show...

Depth First Search in Graphs

Interleaved Depth-First Search

In tree search, depth-first search (DFS) often uses ordering successor heuristics. If the heuristic makes a mistake ordering a bad successor (without goals in its subtree) before good ones (wi th goals in their subtrees), DFS has to unsuccessfully traverse the whole bad subtree before f inding a goal. To prevent this useless work, we present a new strategy called interleaved depthfirst search (...

The visual contour in depth.

We asked subjects to match points on the surface of a smooth three-dimensional (3-D) shape with points on the surface of another object that was geometrically identical to the first object but was placed in a different pose, was differently textured, and was differently shaded. In all cases, the fiducial point was on the rim of one of the objects (i.e., the boundary of the visible region of the...