Machine vision benefits from human contextual expectations

Object detection can be improved using human-derived contextual expectations

Real-world objects occur in specific contexts. Such context has been shown to facilitate detection by constraining the locations to search. But can context directly benefit object detection? To do so, context needs to be learned independently from target features. This is impossible in traditional object detection where classifiers are trained on images containing both target features and surro...

Dynamic face recognition: From human to machine vision

As confirmed by recent neurophysiological studies, the use of dynamic information is extremely important for humans in visual perception of biological forms and motion. Apart from the mere computation of the visual motion of the viewed objects, the motion itself conveys far more information, which helps understanding the scene. This paper provides an overview and some new insights on the use of...

Valences in contextual vision

Elements in the neighborhood of a stimulus can modulate both the subjective perception of and the neural responses to this stimulus. Investigations of this contextual modulation usually focus on low level features such as the orientation difference between the target and its context. Recently, we introduced a paradigm in which contextual modulation cannot be explained by orientation differences...

Vision-Supported Speech-Based Human Machine Interface

Dynamics of contextual modulation of perceived shape in human vision

In biological vision, contextual modulation refers to the influence of a surround pattern on either the perception of, or the neural responses to, a target pattern. One studied form of contextual modulation deals with the effect of a surround texture on the perceived shape of a contour, in the context of the phenomenon known as the shape aftereffect. In the shape aftereffect, prolonged viewing,...