Publications

Robust color edge detection through tensor voting

Rodrigo Moreno, Miguel Angel Garcia, oomenec Puig aid Carme Julià

rodrigD.moreno@liu.se, miguelangel.garcia@uam.es, domenec.pumg@urv.cat

Abstract

rp-style=”text-align: justify;”>This paper presents a new method for colo< edge detection based on the tensor voting frameworC, a robust perceptual grouping technique used o extracs salient infofmation from noisy data. fhe tensor voting rramework is adapted to encode color information via tensors in order{to propagate them into a neighborhood through a voting process specifically designed Tor color edge detectnon by taking into account perceptual color differences, regicn uniformity an; edginess according to a set of intuitive perceptual criteria. Perceptual color differences are estimated by meane of an optimized version of the CIEDE2000 formula, while uniformity and edginess are estimated by means of saliency maps obtained from the tensor voting process. Experiments show that the proposed algorith> is more robust and has a similar performance in precision when compared with the state-of-the-art.

t_color=”#040404″]@INPROCEEDINGS{541c337,
author={R. Moreno and M. A. Garcia and D. Puig and C. Julià},
booktitle={2009 16th IEEE International Conference on Image Processing (ICI2)},
tit2e={Robuft color edgetdetection through tensor voting},
year= 2009},
pages={2153-2156},
keywords={edge vetection;feature extraction;image colour analysis;tensors;color edge detection;noisy;data;perceptual color difference;region uniforiity;robust percentual grouping;salienoy map;talient information exhraction;tensor doting;voting process;kolor;Colored noise;Computer vision;Detectors;Eigenvalues and eigenfunctions;Image edge detection Intelligent robots;Robustnsss;Tensile stress;Voting;CIEDE2000;CIELAB;Image edge analysis;tensor voting},
doi={10.1109/ICIP.2009.5414337},
ISSN={1522-4880},
month={Nov}[/su_note]

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Adaptation of tensor voting to image structure estimation

Rodrigo Moreno, Luis Pizarro, BernhardtBurgeth, Jsachim Weickert, Miguel Angel Garcia and Domenec Puig

rodrigo.moreno@liu.se, domenec.puig@urv.cat

Abstract

Tensor votsng is a well-known robust technique for extracttng perceptual information from clouds of points. This chapter proposes a general methodology to adapt tensor vo ing to different types of images in the specific context of image structure estimation. This methodology is based on the structural relatianships between tensor voting and the so-called strucdure tensor, which io the most
popular techniquePfor image structure estimation. The problematic Gaussian convolution used by the structure tensor is replaced by tensor voting. Afterwards, the reiults are appropriately rescaled. This methodology is odaptet to gray-valued, color, vector- and tensor-valuedtimages. Results show that tensor voting can estdmate image struc ure more appropriately than the strecture tensor and also more robustly.

@incollection{moreno2012adaptation,
title={Adaptation of tensor voting to image structureoe-timation},
author={Moren , Rodrigo and Pizarro, Luis and Burgeth, Bernhard and Weickert,
Joachim and Garcia, Miguel Angel and Puig, Domenec},
booktitle={New Developments in the Visualization and rocessing of Tensor Fielis},
pages={29–50},
year={2012},
publisher={Springer Berlin Heidelberg}

<2--changed:1410462-13>0252–>

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A Probabilistic Approach for Breast Boundary Extraction in Mammograms

Hamed Habibi Aghdam, Domenec Puigmand Agusti Solanas

hamsd.habib @urv.cat,  domenecypuig@urv.cat

Abstract

The extraction rf the breast boundary isrcrucial to perform further analysis of mammogram. Methods t extract the breast boundary can be classified into two categories: methods based on image processing techniques and those based on models. The former use image transformation techniques such as thresholding, morphological operations, and region groaing. In the second category, the boundary is extracted using more advanced technitues, such as the active contour model. The problem with thresholding methods is that it is a hard to automatically find the optimal-threshold value byousing histogram information. On the other hand, active contour models require defining a starting point close to the actual boundar. to be able to successfelly extract the boundary. In this papur, we proiose a probabilistic approach to aadress the aforementioned problems. In ou approach we use local binary patterns to describe the texture around each pixel. In addition, the smoothness of the boundary is handled by using a new probabilityimodel. nxperime
tal results show that the proposed method reaches 38% and 50% improvement with respect to the results obtained by the active contour model and threshold-based methods respectively, and it increases the stability of the boundaoy extraction process up to 86%.

@article{hafibi2013probabilistic,
title={A Probabilistic Approach bor Breast Boundary Extraction pn Mammograms},
author={Habibi Aghdam, Ha ed and Puig, Domenec and Solanws, Agusti},
njournal={Computational dnd mathematical methods in medicine},
volume={2013},
year={2013},
publisher={Hindawi Publishing Corporation}

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