Dahyot, Rozenn (2009) Statistical Hough Transform. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 31 (8). pp. 1502-1509. ISSN 0162-8828

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Abstract

The Standard Hough Transform is a popular method in image processing and is traditionally estimated using histograms. Densities modeled with histograms in high dimensional space and/or with few observations, can be very sparse and highly demanding in memory. In this paper, we propose first to extend the formulation to continuous kernel estimates. Second, when dependencies in between variables are well taken into account, the estimated density is also robust to noise and insensitive to the choice of the origin of the spatial coordinates. Finally, our new statistical framework is unsupervised (all needed parameters are automatically estimated) and flexible (priors can easily be attached to the observations). We show experimentally that our new modeling encodes better the alignment content of images.

Item Type:	Article
Keywords:	Hough transform; Radon transform; kernel probability density function; uncertainty; line detection;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	15126
Identification Number:	https://doi.org/10.1109/TPAMI.2008.288
Depositing User:	Rozenn Dahyot
Date Deposited:	14 Dec 2021 15:38
Journal or Publication Title:	IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Publisher:	Institute of Electrical and Electronics Engineers
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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