Recently, a neuroscience inspired set of visual features was introduced. It was shown that this representation facilitates better performance than state-of-the-art vision systems for object recognition in cluttered and unsegmented images. In this paper, we investigate the utility of these features in other common scene-understanding tasks. We show that this outstanding performance extends to shape-based object detection in the usual windowing framework, to amorphous object detection as a texture classification task, and finally to context understanding These tasks are performed on a large set of images which were collected as a benchmark for the problem of scene understanding. The final system is able to reliably identify cars, pedestrians, bicycles, sky, road, buildings and trees in a diverse set of images.
|State||Published - 2005|
|Event||2005 16th British Machine Vision Conference, BMVC 2005 - Oxford, United Kingdom|
Duration: 5 Sep 2005 → 8 Sep 2005
|Conference||2005 16th British Machine Vision Conference, BMVC 2005|
|Period||5/09/05 → 8/09/05|