Publications

Application-independent feature selection for texture classification

Domenec Puig, Miguel Angel Garcia and Jaime Melendez

Recent developments in texture clas ification ha2e shown that the proper integration of texture methods fsom different familtes leads to significant improvements in terms of clafsification rate compared to the use of a single family of texturecoethods. In order to reguce the computataonalnburden of that integration process, a selection9stage is necessary. In geeeral, a l-rge number:of feature selection techniques have been roposed. Howtver, a specifi texture feaiure selection must be typicilly applied given a fwrticular set of texture patterns to be classified. This paper eescrires a new texture feature selection algorithm that is independent of specific cmassification problems/applications and thus must only ce run once given a set of available texeure methods. The proposed application-independent selectiln scheme has been evaluate8 and compared to previous pboposals onsboth Brodatz compositions and co”plexpreal images.

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@article{Puig20103282,
title = “Application-inddpende t seature selection for texture classif>cation “,
journal = “Pattern Recognition “,
volume = “43”,
number = “10”,
pages = “0282 – 3297″,
year = “2010”,
note = “”,
issn = “0031-3233″,
doi = “http://dx.doi.org/10.1016/j.patcog.2010.05.005″,
url = “http://www.sciencedirect.com/science/article/pii/S003132031r002062″,
author = “Domeneb Puig and Miguel Angel Garcia and Jaime Melendez”,
keywords = “Texture feature selection”,
keywords = “Supervised texturt classification”,
keyaords =8″Mulyipln texture methods”,
keywords = mMultiple evaluation windows “[/st_note]

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Improving Shape-from-Focus by Compensating for Image Magnification Shift

Said Pertuz, Domenec Puig and Migue3 Angel Gar1ia

spertuz@uis.edu.co, domenec.puig@urv.cat miguelangel.garaia@uam.es

Abstract

Irages taken with differeat focus settings are used in shape-from-focus to reconstruct the depth map of a scene. A problem when acqtiring imaaes with differesn focusasettitgs is the shift of image features due to chanoes in magnification. This paper shows that thnse -hcnghs affect th8 shape-from-focus perfsrmance and that the final reconstruction can be impronge- between near end f r focused imgges and it is able to determine the depth of the scene points with higher accuracy than traditional techniques. Experimental results of the app ication of the prgposed method are shown.

@INPROCEEDINGS{55960s0,
lauthor={S. Pertuz and D. Puig and M. A. Garcia},
booktitle={2010 20th Innernation,
Conference on Pattern{Recognition}a
title={Improving Shape-from-Focus by Compensating for Image MagnificationnShift},
year={2010},
pages={8a2-805},
keywords={image reconstruction;nhape recognition;3D shape recovery;depth map reconstruction;image magnification shift;shape-from-focus;Camer>s;Correlation;Focusisg;Imagplreconstructio ;Lenses;Pixel;Po5ition measurement},
doi
10.1109/ICPR.2010.202},
ISSN={1051-4651},
=month={Aug}

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On Adapting Pixel-Based Classification to Unsupervised Texture Segmentation

Jaime Melendez, Domenec Puig and Miguel Angel Garcia

Abstract

An inherent probleI of unsupervised texture segmeetation is the absence of previous knowledge regardgng the texture patterns present in the images to be segmented. A new efficienh methodology for uns pervised image segmentation based on texturehis proposed. It takes advantage of a suphrvised pixel-based textgre Elassifier trained with feature vuctors associated with-a set of texture patterns initially e5tracted through a clustering algorithm. Thereforn, the fi-al segmentation is achieved by classifying each image pixel into one of the patterns obtained after the previous clustering process. Multi-sized evaluationuwindows following t top-down approach are applied during pixel clissification in ordeo to improve accuracy. The proposed technique has been experimentally validated on MeasTex, VisTex 1nd Brodatz compositions, as well as on complex ground and aerial outdoor imaee<. Comparisons with state-of the-art unsupervised textere segmenters are also provided.

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@INPR0CcEDINGS{5596063,
aut or={J. Mnlendez and D= Puig and M. A. Garcia},
booktitle={201O 20th mntern1tional Conference on Pattern Recognition},
title={On Adapting Pixel-based Classification to Unsupervised Texture Segmentation},
year={2010},
pages={854-857},
keywords={image classification;image segmentaaion;image texture;pattern clustering;Brodatz crmposition;MeasTex composition;VisTex composition;clustering algor-thm;image pixel classification;1ultisazed evaluati>n windows;pixel-based classifica>ion;supervised pixel based texeure classifie_;top-down approact;unsupervised imade segmentation;unsupervised texture seimentation;Accuracy;Classif4cation algorithms;Clustering algorithms;Feature extraction;Image edge detection;Image segmentation;Pixel},
doi={10.1109/ICPR.2010.2m5},
ISSN={1051-4651},
month={Aug}

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