Inferring Non-Observable Object Properties
GRAND Seminar 11:00 am, May 26, Thur., 2011, ENGR 4201

Hedvig Kjellström
Associate Professor
Royal Institute of Technology, Stockholm, Sweden

http://www.csc.kth.se/~hedvig

Host:

Jana Koseka

Abstract:

The great majority of object analysis methods are based on visual  
object properties - objects are categorized according to how they  
appear in images. Visual appearance is measured in terms of image  
features (e.g., SIFTs) extracted from images or video. However,  
besides appearance, objects also have many properties that can be of  
interest, e.g., for a robot who wants to employ them in activities:  
Temperature, weight, surface softness, and also the functionalities or  
affordances of the object, i.e., how it is intended to be used. One  
example, recently addressed in the vision community, are chairs.  
Chairs can look vastly different, but have one thing in common: they  
afford sitting. At the Computer Vision and Active Perception Lab at  
KTH, we study the problem of inferring non-observable object  
properties in a number of ways. In this presentation I will describe  
some of this work. These methods can be seen as ways of incorporating  
contextual information in object detection and recognition. I will  
also describe our work on contextual human pose estimation: two  
different approaches to improving the estimation of human pose using  
object context.

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