A Novel Algorithm for Dehazing an Image

Image dehazing plays a vital role in the field of image processing. Previously, many researchers have suggested many techniques like histogram equalization and gamma transformation in order to achieve the target. But these techniques have many limitations like different degree of polarization, different kind of weather conditions or depth information of pixel in image. The proposed work has tried to develop a more effective and reliable image quality assessment method that can evaluate the quality of the proposed dehazing algorithms. It proposed a robust method that is capable enough to improve the detection quality of hazed image by minimizing atmospheric haze effect by using local min operator to reduce time complexity. As compared to previous work it provides better results

Platform image processing to study the structural properties of retinal vessel

This paper presents a technological platform specialized in assessing retinal vessel caliber and describing the relationship of the results obtained to cardiovascular risk. Retinal circulation is an area of active research by numerous groups, and there is general experimental agreement on the analysis of the patterns of the retinal blood vessels in the normal human retina. The development of automated tools designed to improve performance and decrease interobserver variability, therefore, appears necessary

Altair: Automatic Image Analyzer to Assess Retinal Vessel Caliber

The scope of this work is to develop a technological platform specialized in assessing retinal vessel caliber and describing the relationship of the results obtained to cardiovascular risk. Population studies conducted have found retinal vessel caliber to be related to the risk of hypertension, left ventricular hypertrophy, metabolic syndrome, stroke, and coronary artery disease. The vascular system in the human retina has a unique property: it is easily observed in its natural living state in the human retina by the use of a retinal camera. Retinal circulation is an area of active research by numerous groups, and there is general experimental agreement on the analysis of the patterns of the retinal blood vessels in the normal human retina. The development of automated tools designed to improve performance and decrease interobserver variability, therefore, appears necessary

A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms.

Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autism is the small sample sizes of available imaging datasets with mixed sex. Thus, the majority of the investigations have involved male samples, with females somewhat overlooked. This paper deploys machine learning on partial least squares feature extraction to reveal differences in regional brain structure between individuals with autism and typically developing participants. A four-class classification problem (sex and condition) is specified, with theoretical restrictions based on the evaluation of a novel upper bound in the resubstitution estimate. These conditions were imposed on the classifier complexity and feature space dimension to assure generalizable results from the training set to test samples. Accuracies above 80% on gray and white matter tissues estimated from voxel-based morphometry (VBM) features are obtained in a sample of equal-sized high-functioning male and female adults with and without autism (N = 120, n = 30/group). The proposed learning machine revealed how autism is modulated by biological sex using a low-dimensional feature space extracted from VBM. In addition, a spatial overlap analysis on reference maps partially corroborated predictions of the "extreme male brain" theory of autism, in sexual dimorphic areas.This work was partly supported by the MINECO under the TEC2015-64718-R project, the Salvador de Madariaga Mobility Grants 2017 and the Consejer´ıa de Econom´ıa, Innovaci´on, Ciencia y Empleo (Junta de Andaluc´ıa, Spain) under the Excellence Project P11-TIC-7103. The study was conducted in association with the National Institute for Health Research Collaborations for Leadership in Applied Health Research and Care (NIHR CLAHRC) East of England (EoE). The project was supported by the UK Medical Research Council (grant number GO 400061) and European Autism Interventions—a Multicentre Study for Developing New Medications (EU-AIMS); EU-AIMS has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n◦ 115300, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007 - 2013) and EFPIA companies’ in-kind contribution. During the period of this work M-CL was supported by the O’Brien Scholars Program in the Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health (CAMH) and The Hospital for Sick Children, Toronto, the Academic Scholar Award from the Department of Psychiatry, University of Toronto, the Slaight Family Child and Youth Mental Health Innovation Fund, CAMH Foundation, and the Ontario Brain Institute via the Province of Ontario Neurodevelopmental Disorders (POND) Network; MVL was supported by the British Academy, Jesus College Cambridge,Wellcome Trust, and an ERC Starting Grant (ERC-2017- STG; 755816); SB-C was supported by the Autism Research Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health, U

Extracção automática de dados georreferenciados a partir dos planos cadastrais portugueses

Tese dout., Engenharia Electrónica e Computação, Universidade do Algarve, 2009Image recognition algorithms are used to extract information from digitized images automatically. Systems designed to convert paper documents into meaningful vectorial representations are numerous nowadays, and have been constantly improved over the two last decades. However, none of these systems seems to be able to provide satisfying results when it comes to convert complex documents such as technical drawings, usually semantic of the problem is not considered and post-processing costs remain high. This dissertation presents a set of techniques that greatly simplifies the automatic extraction of cadastral entities from the portuguese cadastral maps. The validity of the approach is illustrated designing a prototype system, joining all recognition algorithms and validating all information.Fundação para a Ciência e Tecnologia (FCT