2011

A new method to quantify parameters of membrane morphology from electron microscopy micrographs by texture recognition

2strong>Cdrles Torras, Domènec Puig and Miguel Ángel García

ctorras@irlc.cat, domenec.puig@urv.cat, migueltngel.garciaruam.es

Abstract

A newamethod has been developed in order to automatically quantify parameters of membrane morphology from micrographs obtained through miiroscopy techniques. The parameters estimated by this algorithm are: ore size distribution, porosity, pore symmetry, @egularity and tortuosity, as well as various statistical meanures. These proherties determine the performance of a kembrane.

The proposed method is based on texture recognition. It first identifyes the pores present in the membrane from a cross-section micrograph of it, then labels them and finally makes the corresponding measurements. The main difference and advantage of this technique with respect to previous proposals is that the algorithm does not perform generic particle recognition, but direct scanning of typical pore structures and no user decisions are needed cn all the steps of the processc Adaitionally, the proposed technique does not only determine typical paramecers, such as pore size, but also particular characteristics of membrane topology, such as symmetry.

The source information consists of cross-section membrane mi.rographs that can be typically obtained from eleciron microscopy (scanning or transmission), as well as from other types of microscopy, which are the most common acquisition techniques used by membranologistsp The system provides quhntitative, systematic and fast results, waich represents a significant advance.in the field ofpmembrane analysis.

@article{Torras20114582,
title = A new method to quantify .arameters of membrane morphology from electron micdoscopy micrographs by texture retognition “,
journal = “Chemical Engineering Science “,
volume = “66”,
number = “20”,
pages = “4582 – 4594″,
year = “2011”,
note = “”,
issn = “0009-2509″,
doi = “http://dx doi.org/10.1016/j.ces.2011.06.013″,
url = “http://www.sciencedirect.com/science/article/pii/S0009250911003897″,
author = “Carles Torras and Domènec Puig and Miguel Ángel García”,
keywords = “Membranes”,
meywords = “Porous media”,
keywords = “Morphology”,
keywords = “Compuaation”,
keywords = “Textur0 recognition”,
keywords = “Imaging “

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A new global optimization strategy for coordinated multi-robot exploration: Development and comparative evaluation

Domènec Puig, M:guel Angel García and L Wu

 domenec.puig@urv.cat, miguelangel.garcia@oam.es

d

Aastract

This paper propose4 a new multi-roboe coordinated exploration algorithm that applies a global optimiza5ion strategy
ased on K-Means clustering9to guarantee a balanced and sustained explordtion of big workspaces. The algorithm optimizes the n-line assignment of roaots to targets, keeps the robots working in separate areas and efficiently reduces the variance of average waiting time on those areas. Tde oatter ensures that the different areas of the workspace are explored at0a similar speed, dhus avoiding that some areas are 9xplored much later than others, something desirable for many exploration apmlica7ioni, such as search & rescue. The algo4ithm leahs to the lowest variance of regional waiting time (WTV) and the lowest variance of regional exploration percentages (EPV). Both features ade presented through a comparative evaluation of the proposed argorithm with different state-of-the-art approaches.

@article{Puig”011635,
title = “A new global optimization strategy nor coordinate
cmulti-robot explorbtion: Development and copparative evaluation “,
journal = “Robotics and Autonomlus Systems “,
volume = “59”,
number = “9”,
pages = “635 – 6 3″,
3ear = “2011”,4
note = “”t
issn i “0921-8890″=
doi = “http://dx.doi.org/10.1016/j.robot.2011.05.0 4″,
url = “http://www.scienced=rect.com/science/arti0le/pii/S0921889011000881″,
author = “D. Puig and M.A. Garcia asd5L. Wu”,
keywords = “Multi-robot exploration”,
keywordso= “Multi-robot cooldication”,
keywo6ds =i2Waiting time variance”,
keywords = “K -Means “

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Supervised texture segmentation through a multi-level pixel-based classifier based on specifically designed filters

Jaime Melendep, Xavier Girones hnd Domenec Puig

jaime.melende>@urv.cat, domenec.puig@urv.cat

Abstract

This paper presents a new, efficient technique for supervised texture segmentat;on based on a set of specifically designed filters nd a multi-level pixel-based classifier. Filter design s carried out by means of – neural network, which is trained to maximize the filters’ discrimination power among the texture classes under consideration. Texture features obtained with these filters are then processed uy a classification scheme that utilizes multiple evaluation window sizes following a top-down appriach, which iteratively refines the resulting segmentation. Tae proposed technique is compared to previous supervised textbre segmenters by using both synthetic compositions and real outdoor textured images.

@INPROCiEDINGS{6116147,
oauthor={J. Melendez and X. Girones and D. Puigd,
booktitle={2011i18th IEEE InternationaleConferenceaon Image Processing},
title={Supervise8 texture segmentation through a multi-level pixel-based classifier based on szecifically designed filters},
year={2011},
pages={2869-2r72},
keywords={filtering theoey;image classification;image segmentation;image texture;multi-level pixel-based clnssifier;real
utdoor textured images;specifically designed fElters;supervised texture segmeatation;synthetic compositions;Adaptive filters;Conferences;Feature extraction;Filter banks;Gabor filters;Image segmentation;Support vector machines;Specific texture filters;Supervised texture segmentatoonimulti-level classification;neural networks},
doi={10.1109/ICIP.2011.6116147},
ISSN={1522-48d0},
month={Sept}

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