Dynamic Weights Equations for Converting Grayscale Image to RGB Image

طريقة تحويل الصور الملونة من نظام الوان العرض إلى الصور الرمادي هو عملية بسيطة باستخدام طريقة الأوزان الثابتة للتحويل، ولكن باستخدام نفس الأوزان لاستعادة اللون من نفس الصور ليست عملية فعالة لجميع أنواع الصور لأن الصورة الرمادية تحتوي على معلومات قليلة، وغير كافية لاجراء عملية التحويل. الفكرة الأساسية في هذا البحث هي استخدام المعادلات الرياضية المستخرجة من الصورة الرمادية في عملية التحويل ،حيث يقدم هذا البحث طريقة تلوين الصورة الرمادية باستخدام الأوزان المستمدة من خصائص الصورة الرمادية. وقد تم استخراج مقياس (الانحراف، المتوسط، والانحراف المعياري) من خصائص الصور الرمادية واعتمادها في تحديد الأوزان اللازمة لنظام الوان العرض. أثبتت هذه الطريقة نجاحها في تلوين الصور مقارنة مع الطريقة التقليدية المعتمدة على الأوزان الثابتة لتلوين الصور لأنها تعتمد على الأوزان الثابتة لتحويل جميع الصور انواع الصور الرمادية.The method of converting color images from the RGB color system to grayscale images is a simple operation by using the fixed weights method of conversion, but using the same weights to restore the color of the same images is not an effective operation of all types of images because the grayscale image contains little information and it isn't worthy of conversion operation. The basic idea in this paper is to employ the mathematics equations which extracted from the grayscale image in conversion operation, this paper presents the method of coloring the grayscale image by using the weights derived from the characteristics of the grayscale image. Skewness, Mean and Standard deviation moments have been extracted from the features of grayscale images and its adoption the determine weights of the RGB color system. This method proved its success in coloring images compared to the traditional method adoption of fixed weights for coloring images because it relies on fixed weights for converting all grayscale images

DIAGNOSE EYES DISEASES USING VARIOUS FEATURES EXTRACTION APPROACHES AND MACHINE LEARNING ALGORITHMS

Ophthalmic diseases like glaucoma, diabetic retinopathy, and cataracts are the main cause of visual impairment worldwide. With the use of the fundus images, it could be difficult for a clinician to detect eye diseases early enough. By other hand, the diagnoses of eye disease are prone to errors, challenging and labor-intensive. Thus, for the purpose of identifying various eye problems with the use of the fundus images, a system of automated ocular disease detection with computer-assisted tools is needed. Due to machine learning (ML) algorithms' advanced skills for image classification, this kind of system is feasible. An essential area of artificial intelligence)AI (is machine learning. Ophthalmologists will soon be able to deliver accurate diagnoses and support individualized healthcare thanks to the general capacity of machine learning to automatically identify, find, and grade pathological aspects in ocular disorders. This work presents a ML-based method for targeted ocular detection. The Ocular Disease Intelligent Recognition (ODIR) dataset, which includes 5,000 images of 8 different fundus types, was classified using machine learning methods. Various ocular diseases are represented by these classes. In this study, the dataset was divided into 70% training data and 30% test data, and preprocessing operations were performed on all images starting from color image conversion to grayscale, histogram equalization, BLUR, and resizing operation. The feature extraction represents the next phase in this study ,two algorithms are applied to perform the extraction of features which includes: SIFT(Scale-invariant feature transform) and GLCM(Gray Level Co-occurrence Matrix), ODIR dataset is then subjected to the classification techniques Naïve Bayes, Decision Tree, Random Forest, and K-nearest Neighbor. This study achieved the highest accuracy for binary classification (abnormal and normal) which is 75% (NB algorithm), 62% (RF algorithm), 53% (KNN algorithm), 51% (DT algorithm) and achieved the highest accuracy for multiclass classification (types of eye diseases) which is 88% (RF algorithm), 61% (KNN algorithm) 42% (NB algorithm), and 39% (DT algorithm)

HUMAN IDENTIFICATION SYSTEM BASED ON BRAINPRINT USING MACHINE LEARNING ALGORITHMS

In the medical field, due to the development of neuroimaging, several new methods of the biometric     field have been attending and favorable candidates for the identification of people. These methods are part of "covert biometrics" that involve the use of measures of clinical and medical images to identify them. The prime motivation to use an invisible (Hidden biometric) is the fact that attacks of a system can be very hard to deal with. This privacy strongly contributes to the increased strongest in the topic of person's verification and identification. In this article, he extracted a brain signature, called a "brain fingerprint" from brain (MRI) Magnetic Resonance Image, obtained from 30 healthy subjects as images (1739), these real data sets from Yarmok Medical Hospital. These brainprint in this work are considered to be a hallmark of the brain. The objective of this proposed work which is design a robust, accurate human identification using human brain print, the brain classification based on several phases, included Data acquisition, Feature extraction processing depend on linear discrimination analysis (LDA) to gain important and interesting features of every image calculated by (number of features in the class). The proposed system shows rise detection precision with the features extracted based on LDA with automatical classifier learning by K nearest neighbor (K-NN) and logistic regression (LR) from the LDA method gained with the LR algorithm of (93%) while LDA method gained (91%) with K-NN

Color2Hatch: conversion of color to hatching for low-cost printing

In this paper, we propose Color2Hatch, a decolorization method for business/presentation graphics. In Color2Hatch, each region represented as a closed path and uniformly colored in scalable vector graphics (SVG) is converted to a region hatched in black and white. From the characteristics of business graphics, the hatching patterns are designed to represent mainly the hue in the region; additionally, lightness and saturation can also be reflected. To discriminate subtle differences between colors, attached short line segments, zigzag lines, and wave lines are used in hatching by analogy to a clock. Compared with the existing decolorization methods, for example, grayscale conversion and texturing, our method is superior in the discrimination of regions, suitable for low-cost black and white printing that meets real-world needs

wEscore: quality assessment method of multichannel image visualization with regard to angular resolution

This work considers the problem of quality assessment of multichannel image visualization methods. One approach to such an assessment, the Escore quality measure, is studied. This measure, initially proposed for decolorization methods evaluation, can be generalized for the assessment of hyperspectral image visualization methods. It is shown that Escore does not account for the loss of local contrast at the supra-pixel scale. The sensitivity to the latter in humans depends on the observation conditions, so we propose a modified wEscore measure which includes the parameters allowing for the adjustment of the local contrast scale based on the angular resolution of the images. We also describe the adjustment of wEscore parameters for the evaluation of known decolorization algorithms applied to the images from the COLOR250 and the Cadik datasets with given observational conditions. When ranking the results of these algorithms and comparing it to the ranking based on human perception, wEscore turned out to be more accurate than Escore.This work was supported by Russian Science Foundation (Project No. 20-61-47089)

Recoloração de imagens para dicromatas baseada em mapas elásticos

TCC(graduação) - Universidade Federal de Santa Catarina. Campus Araranguá. Engenharia da Computação.A deficiência na percepção de cores (DPC) afeta 8% da população caucasiana masculina, causada pela falha ou ausência de células fotorreceptoras do tipo cone na retina, e proveniente de causa genética, alguma lesão no olho, ou também devido a outras doenças, como diabetes, leucemia, etc. O indivíduo com DPC tem dificuldades na percepção de cores, que variam dependendo do tipo de deficiência. Dicromatas são os indivíduos com DPC causada pela ausência de um dos tipos de fotorreceptores cone, causando dificuldades na percepção das cores. A DPC causa dificuldades na realização de tarefas que necessitam da distinção de cores, o que pode prejudicar o indivíduo tanto na vida pessoal quanto profissional. Este trabalho propõe uma técnica de recoloração de imagens para dicromatas baseada na técnica de redução de dimensionalidade Mapas Elásticos, onde o objetivo é proporcionar aos indivíduos imagens que preservam detalhes da imagem original, como contrastes entre cores, os quais, os dicromatas não percebem. A técnica foi implementada tanto para CPU como para GPU, apresentando bons tempos de execução, além de apresentar bons resultados no aspecto da preservação de contrastes após a recoloração, a técnica também se propõe a preservar o aspecto de naturalidade da imagem, escolhendo o mapeamento final que minimiza a soma total das distância entre a cor original e o mapeamento dela no plano de percepção dos dicromatas.Color Vision Deficiency (CVD) affects 8% of caucasian male populations, caused by failure or absence of cone-like photorreceptor cells in the retina. CVD may be from genetic cause, some eye injury, or from other diseases such as diabetes, leukemia, etc. Individuals with CVD have difficulty in color perception, whose variation depends on the type of disability. Dichromats are individuals with CVD caused by the abscence of one of the types of cone photoreceptors, causing difficulties in the perception of colors. CVD causes difficulties in performing tasks that require color distinction, which can harm the individual in both personal and professional life. This work proposes an image recoloring technique for dichromats based on the Elastic Maps dimensionality reduction technique, where the objective is to provide images that preserve details of the original image, such as color contrasts. The technique was implemented both CPU and GPU, presenting good execution times, and good results in the aspect of preservation of contrasts after recoloring, the technique also proposes to preserve the aspect of naturality of image, choosing the final mapping that minimizes the total sum of the distance between the original color and the mapping of it in the plane of dichromat perception

Reconhecimento de placas de trânsito em ciclovias por meio de redes neurais

Trabalho de Conclusão Curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2019.Controle e Automação é uma parte vital de sistemas Eletroeletrônicos com o recente avanço da tecno- logia. O presente trabalho apresenta uma contribuição para a proposta de automatização de veículos, focando mais no estudo de formas de filtragem e simplificação de dados para reconhecimento de placas de trânsito Brasileiras usando Redes Neurais de Aprendizagem Profundas para uma possível aplicação em automatização motora em ciclovias do DF. Para tanto, foram usado algoritmos, predo- minantemente feitos na linguagem de programação Python e fazendo uso de Servidores em Nuvem da Google, que fazem reconhecimento de placas alemãs, devido as suas semelhanças com as placas brasileiras, com testes voltados para o uso em ciclovias do Distrito Federal.Control and Automation is a vital part of the recent Technological Advance that happened in recent years. This paper presents a contribution to a proposal of vehicle automation, focusing in studies on filtering and data simplification for Brazilian traffic signs recognition using Deep Learning Neural Networks for a potential application on motor automation on bicycle paths at Distrito Federal. For this matter, Python based algorithms and Google cloud servers were used, performing recognition of German traffic signs, due to its similarities to Brazilian ones, with testing facing bicycle paths in Distrito Federal

Contribuição para as técnicas de detecção de falhas em placas de circuito impresso utilizando a transformada rápida de wavelet

Orientador: Yuzo IanoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Muitos trabalhos foram desenvolvidos na área de visão computacional aplicados à detecção de falhas em placas de circuito impresso (PCI's), visando reduzir a possibilidade de ocorrência de defeitos de fabricação. Nesse trabalho, a partir de modelos de layouts de referência e de teste de PCI's - sem componentes, estudou-se a aplicação de uma técnica de subtração de imagem para a detecção de falhas desses layouts de placas de circuito impresso utilizando a Transformada Rápida de Wavelet (FWT) durante o processamento de imagem. Assim, desenvolvendo as equações da Transformada de Wavelet Discreta (DWT), pode-se comparar a eficácia dessa técnica de processamento de imagem utilizando simulações lineares em MATLAB. Foram obtidos resultados significativos na redução do tempo de processamento e eficácia de classificação de imagem, indicando vantagens no uso desse tipo de técnica de processamento de imagem nos casos simuladosAbstract: Various concentrated work has been developed in the area of computer vision applied to detection of failures on printed circuit boards (PC's), aiming at reducing the possibility of the occurrence of the fabrication defects. In this research, based on PCI's - without mounting reference and test layout models, the objective is to study is the application of an image ubtraction technique to the failure detection of those bare printed circuit boards layouts using the Fast Wavelet Transform (FWT) during the image processing. By developing the Discrete Wavelet Transform (DWT) equations, one may compare the efficiency of this image processing technique using linear simulations developed in MATLAB. Significative results were obtained regarding the reduction of the image processing time and image classification efficiency, thus indicating advantages in using this technique in the simulated casesMestradoTelecomunicações e TelemáticaMestre em Engenharia Elétric