Search results for "image processing"
showing 10 items of 3285 documents
Les joueurs africains rêvent d'Angleterre
2008
Multi-focus image fusion using Laplacian Pyramid technique based on Alpha-Stable filter
2019
International audience
A Robust Multi Stage Technique for Image Binarization of Degraded Historical Documents
2017
International audience; Document image binarization is a central problem in many document analysis systems. Indeed, it represents one of the basic challenges, especially in case of historical documents analysis. In this paper, we propose a novel robust multi stage framework that combines different existing document image thresholding methods for the purpose of getting a better binarization result. CLAHE technique is introduced to significantly enhance contrast in some poor images. The proposed method then uses a hybrid algorithm to partition image into foreground and background. A special procedure is finally applied in order to remove small noise and correct characters morphology. Experime…
Construction of quality indicators based on pre-established goals: application to a colombian public university
2020
This study creates indicators of adequacy and excellence based on multiple-criteria decision-making (MCDM) methods and fuzzy logic. The calculation of indicators presents two main difficulties: The nature of the data (numerical, interval, and linguistic values are mixed) and the objective of each criterion (which does not have to reach either the maximum or the minimum). A method is proposed, based on similarity measures with predetermined ideals, that is capable of overcoming these difficulties to provide easy-to-interpret information about the quality of the alternatives. To illustrate the usefulness of this proposed method, it has been applied to data collected from students across nine …
A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series
2021
Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. Automatic sleep scoring is crucial and urgent to help address the increasing unmet need for sleep research. Therefore, this paper aims to develop an end-to-end deep learning architecture using raw polysomnographic recordings to automate sleep scoring. The proposed model adopts two-dimensional convolutional neural networks (2D-CNN) to automatically learn features from multi-modality signals, together with a "squeeze and excitation" block for recalibrating channel-wise feature responses. The learnt representations are finally fed to a softmax classifier to generate predictions for each sleep stage. The model pe…
Estimation of pollutant emissions from road traffic by image processing techniques. A case study in a suburban area
2013
This paper suggests a methodology based on the image processing technique able to automatically calculate the vehicle traffic and its components (light vehicles, heavy vehicles and motorcycles). The method also allows to evaluate instant vehicle speeds and, where necessary, to rebuild vehicle trajectories. Traffic data obtained through the procedure described below (capacities and speeds) can be also usefully applied to estimate pollutant emissions from vehicle traffic per year; therefore, the suggested method employs the algorithms defined with CORINAIR procedures, implemented in Copert 4 software. In order to evaluate how effective is the methodology, an experiment has been carried out in…
A novel pilot study of automatic identification of EMF radiation effect on brain using computer vision and machine learning
2020
Abstract Electromagnetic field (EMF) radiations from mobile phones and cell tower affect brain of humans and other organisms in many ways. Exposure to EMF could lead to neurological changes causing morphological or chemical changes in the brain and other internal organs. Cellular level analysis to measure and identify the effect of mobile radiations is an expensive and long process as it requires preparing the cell suspension for the analysis. This paper presents a novel pilot study to identify changes in brain morphology under EMF exposure considering drosophila melanogaster as a specimen. The brain is automatically segmented, obtaining microscopic images from which discriminatory geometri…
On the Influence of Grammars on Crossover in Grammatical Evolution
2021
Standard grammatical evolution (GE) uses a one-point crossover (“ripple crossover”) that exchanges codons between two genotypes. The two resulting genotypes are then mapped to their respective phenotypes using a Backus-Naur form grammar. This article studies how different types of grammars affect the resulting individuals of a ripple crossover. We distinguish different grammars based on the expected number of non-terminals chosen when mapping genotype codons to phenotypes, \(B_{avg}\). The grammars only differ in \(B_{avg}\) but can express the same phenotypes. We perform crossover operations on the genotypes and find that grammars with \(B_{avg} > 1\) lead to high numbers of either very sm…
Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization
2019
AbstractVisual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality with…
On the suffix automaton with mismatches
2007
International audience; In this paper we focus on the construction of the minimal deterministic finite automaton S_k that recognizes the set of suffixes of a word w up to k errors. We present an algorithm that makes use of S_k in order to accept in an efficient way the language of all suffixes of w up to k errors in every window of size r, where r is the value of the repetition index of w. Moreover, we give some experimental results on some well-known words, like prefixes of Fibonacci and Thue-Morse words, and we make a conjecture on the size of the suffix automaton with mismatches.