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W.E.L. Grimson, D.P. Huttenlocher, "On the Sensitivity of the Hough Transform for Object Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 3, pp. 255274, March, 1990.  
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@article{ 10.1109/34.49052, author = {W.E.L. Grimson and D.P. Huttenlocher}, title = {On the Sensitivity of the Hough Transform for Object Recognition}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {12}, number = {3}, issn = {01628828}, year = {1990}, pages = {255274}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.49052}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  On the Sensitivity of the Hough Transform for Object Recognition IS  3 SN  01628828 SP255 EP274 EPD  255274 A1  W.E.L. Grimson, A1  D.P. Huttenlocher, PY  1990 KW  pattern recognition; picture processing; false peak likelihood bound; sensitivity; Hough transform; object recognition; sensory data; coordinate transformations; noise; occlusion; tessellation effects; false positives; pattern recognition; picture processing; sensitivity; transforms VL  12 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Object recognition from sensory data involves, in part, determining the pose of a model with respect to a scene. A common method for finding an object's pose is the generalized Hough transform, which accumulates evidence for possible coordinate transformations in a parameter space whose axes are the quantized transformation parameters. Large clusters of similar transformations in that space are taken as evidence of a correct match. A theoretical analysis of the behavior of such methods is presented. The authors derive bounds on the set of transformations consistent with each pairing of data and model features, in the presence of noise and occlusion in the image. Bounds are provided on the likelihood of false peaks in the parameter space, as a function of noise, occlusion, and tessellation effects. It is argued that haphazardly applying such methods to complex recognition tasks is risky, as the probability of false positives can be very high.
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