2007 IEEE International Conference on Image Processing - San Antonio, Texas, U.S.A. - September 16-19, 2007

Technical Program

Paper Detail

Paper:WP-P5.2
Session:Image and Video Modeling III / Distributed Coding
Time:Wednesday, September 19, 14:30 - 17:10
Presentation: Poster
Title: FINDING FAMILIAR OBJECTS AND THEIR DEPTH FROM A SINGLE IMAGE
Authors: Hwann-Tzong Chen; National Tsing Hua University 
 Tyng-Luh Liu; Academia Sinica 
Abstract: We present a classification-based method to identify objects of interest, and judge their depth in a single image. Our approach is motivated by a postulate of human depth perception that people can give a credible depth estimation for an object whose familiar size is known, even without using stereo vision. To emulate the mechanism, we categorize objects into the same class if they have similar sizes and shapes, and model the sense of discovering a familiar object by applying {\em multiple kernel logistic regression} to the conditional probability of feature types. The depth of a detected target can then be obtained by referencing its corresponding object category. Overall, the proposed algorithm is efficient in both the training and testing phases, and does not require a large amount of training images for good performances.



©2016 Conference Management Services, Inc. -||- email: webmaster@icip2007.com -||- Last updated Friday, August 17, 2012