Effect of substrates and surfactants over the evolution of crystallographic texture of nanostructured ZnO thin films deposited through microwave irradiation

In spite of intense research on ZnO over the past decade, the detailed investigation about the crystallographic texture of as obtained ZnO thin films/coatings, and its deviation with growth surface is scarce. We report a systematic study about the orientation distribution of nanostructured ZnO thin films fabricated by microwave irradiation with the variation of substrates and surfactants. The nanostructured films comprising of ZnO nanorods are grown on semiconductor substrates such as Si(100), Ge(100)], conducting substrates (ITO-coated glass, Cr coated Si), and polymer coated Si (PMMA/Si) to examine the respective development of crystallographic texture. The ZnO deposited on semiconductor substrates yieldsmixed texture, whereas c-axis oriented ZnO nanostructured films are obtained by conducting substrate, and PMMA coated Si substrates. Among all the surfactants, nanostructured film produced by using the lower molecular weight of polymeric surfactants (polyvinylpyrrolidone) shows a stronger (0002) texture, and that can be tuned to (10 - 10) by increasing the molecular weight of the surfactant. The strongest basal pole is achieved for the ZnO deposited on PMMA coated Si as substrate, and cetyl-trimethyl ammonium bromide as cationic surfactant. The texture analysis is carried out by X-ray pole figure analysis using the Schultz reflection method. (C) 2015 Elsevier B.V. All rights reserved.

An FPGA Based Coprocessor for GLCM and Haralick Texture Features and Their Application in Prostate Cancer Classification

Grey Level Co-occurrence Matrix (GLCM), one of the best known tool for texture analysis, estimates image properties related to second-order statistics. These image properties commonly known as Haralick texture features can be used for image classification, image segmentation, and remote sensing applications. However, their computations are highly intensive especially for very large images such as medical ones. Therefore, methods to accelerate their computations are highly desired. This paper proposes the use of programmable hardware to accelerate the calculation of GLCM and Haralick texture features. Further, as an example of the speedup offered by programmable logic, a multispectral computer vision system for automatic diagnosis of prostatic cancer has been implemented. The performance is then compared against a microprocessor based solution.

Ocular blood flow analysis detects microvascular abnormalities in impaired glucose tolerance.

Objective: Waveform analysis has been used to assess vascular resistance and predict cardiovascular events. We aimed to identify microvascular abnormalities in patients with impaired glucose tolerance (IGT) using ocular waveform analysis. The effects of pioglitazone were also assessed. Methods: Forty patients with IGT and twenty-four controls were studied. Doppler velocity recordings were obtained from the central retinal, ophthalmic and common carotid arteries, and sampled at 200 Hz. A discrete wavelet-based analysis method was employed to quantify waveforms. The resistive index (RI),was also determined. Patients with IGT were randomised to pioglitazone or placebo and measurements repeated after 12 weeks treatment. Results: In the ocular waveforms, significant differences in power spectra were observed in frequency band four (corresponding to frequencies between 6.25 and 12.50 Hz) between groups (p

Automated tumour annotation and analysis for molecular pathology using TissueMark (R)

Objective: Molecular pathology relies on identifying anomalies using PCR or analysis of DNA/RNA. This is important in solid tumours where molecular stratification of patients define targeted treatment. These molecular biomarkers rely on examination of tumour, annotation for possible macro dissection/tumour cell enrichment and the estimation of % tumour. Manually marking up tumour is error prone. Method: We have developed a method for automated tumour mark-up and % cell calculations using image analysis called TissueMark® based on texture analysis for lung, colorectal and breast (cases=245, 100, 100 respectively). Pathologists marked slides for tumour and reviewed the automated analysis. A subset of slides was manually counted for tumour cells to provide a benchmark for automated image analysis. Results: There was a strong concordance between pathological and automated mark-up (100 % acceptance rate for macro-dissection). We also showed a strong concordance between manually/automatic drawn boundaries (median exclusion/inclusion error of 91.70 %/89 %). EGFR mutation analysis was precisely the same for manual and automated annotation-based macrodissection. The annotation accuracy rates in breast and colorectal cancer were 83 and 80 % respectively. Finally, region-based estimations of tumour percentage using image analysis showed significant correlation with actual cell counts. Conclusion: Image analysis can be used for macro-dissection to (i) annotate tissue for tumour and (ii) estimate the % tumour cells and represents an approach to standardising/improving molecular diagnostics.

Texture recognition under varying imaging geometries

La visió és probablement el nostre sentit més dominant a partir del qual derivem la majoria d'informació del món que ens envolta. A través de la visió podem percebre com són les coses, on són i com es mouen. En les imatges que percebem amb el nostre sistema de visió podem extreure'n característiques com el color, la textura i la forma, i gràcies a aquesta informació som capaços de reconèixer objectes fins i tot quan s'observen sota unes condicions totalment diferents. Per exemple, som capaços de distingir un mateix objecte si l'observem des de diferents punts de vista, distància, condicions d'il·luminació, etc. La Visió per Computador intenta emular el sistema de visió humà mitjançant un sistema de captura d'imatges, un ordinador, i un conjunt de programes. L'objectiu desitjat no és altre que desenvolupar un sistema que pugui entendre una imatge d'una manera similar com ho realitzaria una persona. Aquesta tesi es centra en l'anàlisi de la textura per tal de realitzar el reconeixement de superfícies. La motivació principal és resoldre el problema de la classificació de superfícies texturades quan han estat capturades sota diferents condicions, com ara distància de la càmera o direcció de la il·luminació. D'aquesta forma s'aconsegueix reduir els errors de classificació provocats per aquests canvis en les condicions de captura. En aquest treball es presenta detalladament un sistema de reconeixement de textures que ens permet classificar imatges de diferents superfícies capturades en diferents condicions. El sistema proposat es basa en un model 3D de la superfície (que inclou informació de color i forma) obtingut mitjançant la tècnica coneguda com a 4-Source Colour Photometric Stereo (CPS). Aquesta informació és utilitzada posteriorment per un mètode de predicció de textures amb l'objectiu de generar noves imatges 2D de les textures sota unes noves condicions. Aquestes imatges virtuals que es generen seran la base del nostre sistema de reconeixement, ja que seran utilitzades com a models de referència per al nostre classificador de textures. El sistema de reconeixement proposat combina les Matrius de Co-ocurrència per a l'extracció de característiques de textura, amb la utilització del Classificador del veí més proper. Aquest classificador ens permet al mateix temps aproximar la direcció d'il·luminació present en les imatges que s'utilitzen per testejar el sistema de reconeixement. És a dir, serem capaços de predir l'angle d'il·luminació sota el qual han estat capturades les imatges de test. Els resultats obtinguts en els diferents experiments que s'han realitzat demostren la viabilitat del sistema de predicció de textures, així com del sistema de reconeixement.

Plant Leaf Identification Using Gabor Wavelets

This article presents a novel method of plant classification using Gabor wavelet filters to extract texture filters in a foliar surface. The aim of this promising method is to add to the results obtained by other leaf attributes (such as shape, contour, color, among others), increasing, therefore, the percentage of classification of plant species. To corroborate the efficiency of the technique, an experiment using 20 species from Brazilian flora was done and discussed. The results are also compared with texture Fourier descriptors and cooccurrence matrices. (C) 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 236-243, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20201

Estudo da transformada rápida wavelet e sua conexão com banco de filtros

In this work we presented an exhibition of the mathematical theory of orthogonal compact support wavelets in the context of multiresoluction analysis. These are particularly attractive wavelets because they lead to a stable and very efficient algorithm, that is Fast Transform Wavelet (FWT). One of our objectives is to develop efficient algorithms for calculating the coefficients wavelet (FWT) through the pyramid algorithm of Mallat and to discuss his connection with filters Banks. We also studied the concept of multiresoluction analysis, that is the context in that wavelets can be understood and built naturally, taking an important step in the change from the Mathematical universe (Continuous Domain) for the Universe of the representation (Discret Domain)

Texture profile and correlation between sensory and instrumental analyses on extruded snacks

Instrumental texture analysis on extruded snacks is widely applied, however there is no scientific consensus about the test and probe types that can be correlated with the sensory texture of snacks. Eleven commercial extruded snacks of different shapes were evaluated instrumentally using different probes and sensorially through descriptive analysis. The snack texture was described using the attributes of hardness, crispness, adhesiveness, fracturability and chewiness. Cylindrical snacks were described through crispness and fracturability, pelleted and shell-shaped snacks by chewiness and ring-shaped snacks by adhesiveness and hardness. Hardness and adhesiveness were correlated with a Warner-Bratzler test using a V shape probe (r = 0.718 and r = 0.763, respectively), while fracturability and chewiness were correlated with a Warner-Bratzler test using a guillotine (r = 0.776 and r = 0.662, respectively). The fairly strong good correlations enable application of these instrumental tests as an indication of the sensory texture of extruded snacks. © 2013 Elsevier Ltd. All rights reserved.

Análise e síntese de um processador digital wavelet

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Segmentação de texturas em imagens digitais utilizando wavelets redundantes

Pós-graduação em Matematica Aplicada e Computacional - FCT

Análise de variância WAVELET de séries temporais com dados faltantes: avaliação do índice de cintilação ionosférica S4 em sinais de satélite GPS

Pós-graduação em Matematica Aplicada e Computacional - FCT

Análise de variância WAVELET de séries temporais com dados faltantes: avaliação do índice de cintilação ionosférica S4 em sinais de satélite GPS

Pós-graduação em Matematica Aplicada e Computacional - FCT

Texture descriptor based on partially self-avoiding deterministic walker on networks

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation. (C) 2012 Elsevier Ltd. All rights reserved.

Analysis of morphological and functional heterogeneity in ct perfusion images of lung tumours

Questa tesi si propone di innovare lo stato dell’arte dei metodi di analisi dell’eterogeneità in lesioni polmonari attualmente utilizzati, affiancando l’analisi funzionale (emodinamica) a quella morfologica, grazie allo sviluppo di nuove feature specifiche. Grazie alla collaborazione tra il Computer Vision Group (CVG) dell’Università di Bologna e l’Unità Operativa di Radiologia dell’IRCCS-IRST di Meldola (Istituto di Ricovero e Cura a Carattere Scientifico – Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori), è stato possibile analizzare un adeguato numero di casi reali di pazienti affetti da lesioni polmonari primitive, effettuando un’analisi dell’eterogeneità sia su sequenze di immagini TC baseline sia contrast-enhanced, consentendo quindi un confronto tra eterogeneità morfologica e funzionale. I risultati ottenuti sono infine discussi sulla base del confronto con le considerazioni di natura clinica effettuate in cieco da due esperti radiologi dell’IRCCS-IRST.

Utilização da transformada de wavelet para detecção de variações anormais de tensão em sistemas elétricos com geração distribuída

The increasing demand in electricity and decrease forecast, increasingly, of fossil fuel reserves, as well as increasing environmental concern in the use of these have generated a concern about the quality of electricity generation, making it well welcome new investments in generation through alternative, clean and renewable sources. Distributed generation is one of the main solutions for the independent and selfsufficient generating systems, such as the sugarcane industry. This sector has grown considerably, contributing expressively in the production of electricity to the distribution networks. Faced with this situation, one of the main objectives of this study is to propose the implementation of an algorithm to detect islanding disturbances in the electrical system, characterized by situations of under- or overvoltage. The algorithm should also commonly quantize the time that the system was operating in these conditions, to check the possible consequences that will be caused in the electric power system. In order to achieve this it used the technique of wavelet multiresolution analysis (AMR) for detecting the generated disorders. The data obtained can be processed so as to be used for a possible predictive maintenance in the protection equipment of electrical network, since they are prone to damage on prolonged operation under abnormal conditions of frequency and voltage.