Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Detecting, Localizing, and Recovering Kinematics of Textured Animals

Deva Ramanan, David A. Forsyth and Kobus Barnard

EECS Department
University of California, Berkeley
Technical Report No. UCB/CSD-04-1361
November 2004

http://www.eecs.berkeley.edu/Pubs/TechRpts/2004/CSD-04-1361.pdf

We develop and demonstrate an object recognition system capable of accurately detecting, localizing, and recovering the kinematic configuration of textured animals in real images. We build a deformation model of shape automatically from videos of animals and an appearance model of texture from a labeled collection of animal images, and combine the two models automatically. We develop a simple texture descriptor that outperforms the state of the art. We test our animal models on two datasets; images taken by professional photographers from the Corel collection, and assorted images from the web returned by Google. We demonstrate quite good performance on both datasets. Comparing our results with simple baselines, we show that for the Google set, we can recognize objects from a collection demonstrably hard for object recognition.


BibTeX citation:

@techreport{Ramanan:CSD-04-1361,
    Author = {Ramanan, Deva and Forsyth, David A. and Barnard, Kobus},
    Title = {Detecting, Localizing, and Recovering Kinematics of Textured Animals},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2004},
    Month = {Nov},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2004/5724.html},
    Number = {UCB/CSD-04-1361},
    Abstract = {We develop and demonstrate an object recognition system capable of accurately detecting, localizing, and recovering the kinematic configuration of textured animals in real images. We build a deformation model of shape automatically from videos of animals and an appearance model of texture from a labeled collection of animal images, and combine the two models automatically. We develop a simple texture descriptor that outperforms the state of the art. We test our animal models on two datasets; images taken by professional photographers from the Corel collection, and assorted images from the web returned by Google. We demonstrate quite good performance on both datasets. Comparing our results with simple baselines, we show that for the Google set, we can recognize objects from a collection demonstrably hard for object recognition.}
}

EndNote citation:

%0 Report
%A Ramanan, Deva
%A Forsyth, David A.
%A Barnard, Kobus
%T Detecting, Localizing, and Recovering Kinematics of Textured Animals
%I EECS Department, University of California, Berkeley
%D 2004
%@ UCB/CSD-04-1361
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2004/5724.html
%F Ramanan:CSD-04-1361