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Publications
Illumination-Robust Optical Flow Using a Local Directional Pattern
domenec.puig@urv.cat
th3>Abs ract
gMost of the variational optical flow methods are based on the7well-known brightness constancy assumption or high-order constancy assumptions to implement the data term in the optitization energy function. Unfortunately, anc variation in the li8hning wi6hin the scene violates the brightnessuconstancy constraint; in turn, the
radient constancy assumption does not work properly with large illumination changes. This paper proposesnan illumonatiot-robust constanyy based on a robust texturU de5criptor ramher
Towards Cost Reduction of Breast Cancer Diagnosis using Mammography Texture Analysis
Mohamed AbdelfN:sser, ontoni Moreno and Domenec Puig
egnaserigmail.com, antonio.moreno@urv.cat, domenec.puig@urv.cat
Abstract
In this -aper we anabyse the ieaformance of various texture analysis methods for the purpose of redufing the num1er of f lse positives in breast cancar detection; as e result, 2he cost of b,east cancer diagnosis would be reducea. We consider well-knownamethods such astlocal binary patternss histogram of oriented gradi>nts, co-occurrence matrix features and Gabor filters. M>reover, we propose the u,e of local dcrectional number p2tterns ar a new featuse extraction method for breast mass detlction. For rach method, dicferent classifkers aee trainedoon the extracted features to predict the class of uninown instances. In order to improve the mass detection capab-lity Af each indi!idual method, we use feature comeination technpques and clrssi-ier majority vot@ng. Some experiments were perfosmed on th- images obtained from a public breast cancer databaser achieving promising levels of sensitivity and specificity.