This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Finding Waldo, or Focus of Attention Using Local Color Information
August 1995 (vol. 17 no. 8)
pp. 805-809

Abstract—We present a method to locate an “object” in a color image, or more precisely, to select a set of likely locations for the object. The model is assumed to be of known color distribution, which permits the use color-space processing. A new method is presented, which exploits more information than the previous Backprojection Algorithm of Swain and Ballard at a competitive complexity. Precisely, the new algorithm is based on matching local histograms with the model, instead of directly replacing pixels with a confidence that they belong to the object. We prove that a simple version of this algorithm degenerates into Backprojection in the worst case. In addition, we show how to estimate the scale of the model.

Results are shown on pictures digitized from the famous “Where is Waldo” books. Issues concerning the optimal choice of a color space and its quantization are carefully considered and studied in this application. We also propose to use co-occurrence histograms to deal with cases where important color variations can be expected.

[1] M.J. Swain and D.H. Ballard,“Indexing via color histograms,” Proc. ICCV 90, pp. 390–393.
[2] W.K. Pratt, Digital Image Processing, John Wiley&Sons, New York, 1978.
[3] S.C. Shapiro, Encyclopedia of Artificial Intelligence, John Wiley and Sons, 1990.
[4] T.F. Syeda-Mahmood, “Data and Model-Driven Selection Using Color Regions,” Proc. European Conf. Computer Vision, pp. 321-327, 1992.
[5] G.E. Healey,“Segmenting images using normalized colors,” Physics-Based Vision: Principles and Practice—Color, p. 166, 1992.
[6] W.E.L. Grimson, Object Recognition by Computer. MIT Press, 1990.
[7] F. Stein and G. Medioni,“Recognition of 3D objects from 2D groupings,” Image Understanding Workshop,San Diego, Calif., Jan. 1992.
[8] T. Pavlidis, Algorithms for Graphics and Image Processing, pp. 199-201 Rockville, Md.: Computer Science Press, 1982.
[9] R. Duda, P. Hart, and D. Stork, Pattern Classification. New York: John Wiley&Sons, 2001.
[10] A.K. Jain, Fundamentals of Digital Image Processing. Prentice Hall, 1989.
[11] G. Finlayson,M. Drew,, and B. Funt,“Diagonal transforms suffice for color constancy,” Proc. IEEE Int’l Conf. Computer Vision,Berlin, 1993.
[12] D.H. Ballard and C.M. Brown, Computer Vision, Prentice Hall, Upper Saddle River, N.J., 1982.
[13] Y. Ohta,T. Kanade,, and T. Sakai,“Color information for region segmentation,” CGIP, vol. 13, p. 222.
[14] G.H. Bell and D.J. Hall,“ISODATA, A novel method of data analysis and pattern classification,” NTIS Report AD 699616, 1965.

Index Terms:
Object recognition, focus of attention, color images, color quantization, color histograms.
Citation:
François Ennesser, Gérard Medioni, "Finding Waldo, or Focus of Attention Using Local Color Information," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 805-809, Aug. 1995, doi:10.1109/34.400571
Usage of this product signifies your acceptance of the Terms of Use.