Global Depth Perception from Familiar Scene Structure

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7267

Title:	Global Depth Perception from Familiar Scene Structure
Keywords:	AI depth monocular scale selection natural images scene recognition
Issue Date:	9-Oct-2013
Description:	In the absence of cues for absolute depth measurements as binocular disparity, motion, or defocus, the absolute distance between the observer and a scene cannot be measured. The interpretation of shading, edges and junctions may provide a 3D model of the scene but it will not inform about the actual "size" of the space. One possible source of information for absolute depth estimation is the image size of known objects. However, this is computationally complex due to the difficulty of the object recognition process. Here we propose a source of information for absolute depth estimation that does not rely on specific objects: we introduce a procedure for absolute depth estimation based on the recognition of the whole scene. The shape of the space of the scene and the structures present in the scene are strongly related to the scale of observation. We demonstrate that, by recognizing the properties of the structures present in the image, we can infer the scale of the scene, and therefore its absolute mean depth. We illustrate the interest in computing the mean depth of the scene with application to scene recognition and object detection.
URI:	http://koha.mediu.edu.my:8181/xmlui/handle/1721
Other Identifiers:	AIM-2001-036 CBCL-213 http://hdl.handle.net/1721.1/7267
Appears in Collections:	MIT Items

Files in This Item:

There are no files associated with this item.

DSpace JSPUI