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001
Publications

2018

Automatic identification of pollen in microscopic images

Authors
Santos, EMDS; Marcal, ARS;

Publication
Lecture Notes in Computational Vision and Biomechanics

Abstract
A system for the identification of pollen grains in bright-field microscopic images is presented in this work. The system is based on segmentation of raw images and binary classification for 3 types of pollen grain. The segmentation method developed tackles a major difficulty of the problem: the existence of clustered pollen grains in the initial binary images. Two different SVM classification kernels are compared to identify the 3 pollen types. The method presented in this paper is able to provide a good estimate of the number of pollen grains of Olea Europea (relative error of 1.3%) in microscopic images. For the two others pollen types tested (Corylus and Quercus), the results were not as good (relative errors of 14.5% and 20.3%, respectively). © 2018, Springer International Publishing AG.

2018

Towards Automatic Calibration of Dotblot Images

Authors
Marcal, ARS; Martins, J; Selaru, E; Tavares, F;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
This paper addresses the issue of calibration (or normalization) of macroarray (dotblot) images. It proposes 3 parameters for the evaluation of the impact on the recorded markers of under- and over-exposure during the experimental acquisition of dotblot images – volume (V), saturation (S) and apparent radius (R). These parameters were evaluated using 101 dotblot images obtained from 16 different experiments, with 404 control markers in total. A procedure to simulate the changes on markers by increasing and decreasing exposure times is also presented. This can be the basis of a normalization procedure for dot blot images, which would be an important improvement in the current laboratory image acquisition protocol, reducing the subjectivity both at the acquisition level and at the subsequent image analysis stage. © 2018, Springer International Publishing AG, part of Springer Nature.

2018

Robust Detection of Water Sensitive Papers

Authors
Marcal, ARS;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
The automatic analysis of water-sensitive papers (WSP) is of great relevance in agriculture. SprayImageMobile is a software tool developed for mobile devices (iOS) that provides full processing of WSP, from image acquisition to the final reporting. One of the initial processing tasks on SprayImageMobile is the detection (or segmentation) of the WSP on the image acquired by the device. This paper presents the method developed for the detection of the WSP that was implemented in SprayImageMobile. The method is based on the identification of reference points along the WSP margins, and the modeling of a quadrilateral that takes into account possible false positive and negative identifications. The method was tested on a set of 360 images, failing to detect the WSP in only 1 case (detection accuracy of 99.7%). The segmentation accuracy was evaluated using references obtained by a semi-automatic method. The average values obtained for the 359 images tested were: 0.9980 (precision), 0.9940 (recall) and 0.9921 (Hammoude metric). © 2018, Springer International Publishing AG, part of Springer Nature.

2016

PhenoSat – a tool for remote sensing based analysis of vegetation dynamics

Authors
Rodrigues, A; Marcal, ARS; Cunha, M;

Publication
Remote Sensing and Digital Image Processing

Abstract
PhenoSat is a software tool that extracts phenological information from satellite based vegetation index time-series. This chapter presents PhenoSat and tests its main characteristics and functionalities using a multi-year experiment and different vegetation types – vineyard and semi-natural meadows. Three important features were analyzed: (1) the extraction of phenological information for the main growing season, (2) detection and estimation of double growth season parameters, and (3) the advantages of selecting a sub-temporal region of interest. Temporal NDVI satellite data from SPOT VEGETATION and NOAA AVHRR were used. Six fitting methods were applied to filter the satellite noise data: cubic splines, piecewise-logistic, Gaussian models, Fourier series, polynomial curve-fitting and Savitzky-Golay. PhenoSat showed to be capable to extract phenological information consistent with reference measurements, presenting in some cases correlations above 70% (n=10; p=0.012). The start of in-season regrowth in semi-natural meadows was detected with a precision lower than 10-days. The selection of a temporal region of interest, improve the fitting process (R-square increased from 0.596 to 0.997). This improvement detected more accurately the maximum vegetation development and provided more reliable results. PhenoSat showed to be capable to adapt to different vegetation types, and different satellite data sources, proving to be a useful tool to extract metrics related with vegetation dynamics. © Springer International Publishing AG 2016.

2015

A quantitative hybridization approach using 17 DNA markers for identification and clustering analysis of Ralstonia solanacearum

Authors
Albuquerque, P; Marcal, ARS; Caridade, C; Costa, R; Mendes, MV; Tavares, F;

Publication
PLANT PATHOLOGY

Abstract
Ralstonia solanacearum (Rs) is a quarantine phytopathogenic bacterium accountable for heavy economic losses worldwide. Monitoring and eradication programmes required for this pathogen are dependent on the availability of time-and cost-efficient detection and typing methods. However, members of the Rs species complex are characterized by a high phenotypic and genetic diversity, which requires improved diagnostics methods. The currently available full genome sequences of several Rs strains allow for the selection of novel specific DNA markers using comparative genomics tools. In this work, 17 novel markers were selected based on Rs-specific protein domains and thoroughly validated for specificity and stability, both in silico and using 'wet lab' assays. Polymerase chain reaction-and hybridization-based validation assays revealed that the DNA regions selected as markers were unevenly distributed amongst the tested strains, with nine markers present throughout the species complex. The distribution of the remaining eight markers was highly variable between the different analysed strains and enabled the attainment of strain-specific dot blot hybridization patterns, particularly informative for typing. The average probability value of each strain being positive for each of the 17 markers was calculated by an algorithm and used to obtain a dendrogram representing hierarchical clustering analysis of Rs, according to the similarity of their hybridization patterns. This method should prove to be a robust and straightforward procedure for genotyping members of the Rs species complex. Furthermore, this quantitative hybridization approach will allow the construction of informative databases to determine new Rs genotypes and infer epidemiological patterns.

Supervised
thesis

2017

Reconhecimento Automático de Sinais de Trânsito em Imagens Digitais

Author
Moisés Vungo

Institution
UP-FCUP

2017

Análise Automática de Imagens Pulmonares de Tomografia Computorizada

Author
Susana Augusta Cardoso Leal Lopes

Institution
UP-FCUP

2015

Visão Computacional para veículos aéreos não tripulados (UAV)

Author
Mónica Cristina Gandra da Rocha Salgado

Institution
UP-FCUP

2015

Elaboração de um método semiautomático para detecção do tecido adiposo epicárdico em Imagens de Ressonância Magnética

Author
Cristiana Sofia dos Santos Machado de Araújo

Institution
UP-FCUP