scholarly journals Silhouette index for determining optimal k-means clustering on images in different color models

2018 ◽  
Vol 7 (2.14) ◽  
pp. 105 ◽  
Author(s):  
Abd Rasid Mamat ◽  
Fatma Susilawati Mohamed ◽  
Mohamad Afendee Mohamed ◽  
Norkhairani Mohd Rawi ◽  
Mohd Isa Awang
Keyword(s):  
Image Processing ◽  
Color Model ◽  
Cluster Validation ◽  
Validation Process ◽  
Color Models ◽  
Silhouette Index ◽  
Hsv Color Model ◽  
Different Color ◽  
Lab Color Model ◽  
Cie Lab

Clustering process is an essential part of the image processing. Its aim to group the data according to having the same attributes or similarities of the images. Consequently, determining the number of the optimum clusters or the best (well-clustered) for the image in different color models is very crucial. This is because the cluster validation is fundamental in the process of clustering and it reflects the split between clusters. In this study, the k-means algorithm was used on three colors model: CIE Lab, RGB and HSV and the clustering process made up to k clusters. Next, the Silhouette Index (SI) is used to the cluster validation process, and this value is range between 0 to 1 and the greater value of SI illustrates the best of cluster separation. The results from several experiments show that the best cluster separation occurs when k=2 and the value of average SI is inversely proportional to the number of k cluster for all color model. The result shows in HSV color model the average SI decreased 14.11% from k = 2 to k = 8, 11.1% in HSV color model and 16.7% in CIE Lab color model. Comparisons are also made for the three color models and generally the best cluster separation is found within HSV, followed by the RGB and CIE Lab color models.  

scholarly journals The Application of Computer Image Analysis Based on Textural Features for the Identification of Barley Kernels Infected with Fungi of the Genus Fusarium

2018 ◽  
Vol 22 (3) ◽  
pp. 49-56 ◽  
Author(s):  
Ewa Ropelewska
Keyword(s):  
Image Analysis ◽  
Selection Method ◽  
Color Model ◽  
Textural Features ◽  
Discriminative Power ◽  
Selection Methods ◽  
Computer Image Analysis ◽  
Genus Fusarium ◽  
Color Models ◽  
Lab Color Model

AbstractThe aim of this study was to develop discrimination models based on textural features for the identification of barley kernels infected with fungi of the genus Fusarium and healthy kernels. Infected barley kernels with altered shape and discoloration and healthy barley kernels were scanned. Textures were computed using MaZda software. The kernels were classified as infected and healthy with the use of the WEKA application. In the case of RGB, Lab and XYZ color models, the classification accuracies based on 10 selected textures with the highest discriminative power ranged from 95 to 100%. The lowest result (95%) was noted in XYZ color model and Multi Class Classifier for the textures selected using the Ranker method and the OneR attribute evaluator. Selected classifiers were characterized by 100% accuracy in the case of all color models and selection methods. The highest number of 100% results was obtained for the Lab color model with Naive Bayes, LDA, IBk, Multi Class Classifier and J48 classifiers in the Best First selection method with the CFS subset evaluator.

scholarly journals Purwarupa Pengoperasian TV Dengan Mendeteksi Isyarat Jari Berbasis Pengolahan Citra Digital

2015 ◽  
Vol 5 (1) ◽  
pp. 31
Author(s):  
Handreas Okta Wibawa ◽  
Agus Harjoko
Keyword(s):  
Image Processing ◽  
Computer Technology ◽  
Processing System ◽  
Data Conversion ◽  
Color Model ◽  
Human Beings ◽  
Best Value ◽  
Hsv Color Model ◽  
Serial Data ◽  
Hand Image

Abstrak            Perkembangan teknologi komputer dan elektronika yang sangat pesat saat ini membuat manusia mulai mencari solusi lain untuk melakukan kegiatan menjadi lebih mudah dan praktis. Teknologi-teknologi yang berkembang dan dapat dimanfaatkan saat ini sangat memungkinkan manusia untuk memudahkan aktifitas dalam mengoperasikan TV dengan isyarat jari.            Dalam penelitian ini, pengolahan citra digital merupakan teknologi yang digunakan untuk mengoperasikan TV dengan memanfaatkan webcam dan komputer sebagai pengolah citra. Input dari penelitian ini berupa citra tangan hasil tangkapan webcam pada komputer. Kemudian citra tangan diolah melalui sistem pengolah citra untuk mendapatkan hasil berupa nilai pada jari tangan. Nilai tersebut menunjukkan angka yang sama dengan jumlah jari tangan yang terdeteksi oleh  webcam pada layar monitor. Mikrokontroler berfungsi menerima data serial dari hasil pemrosesan citra tangan dan berfungsi sebagai konversi nilai data serial menjadi instruksi yang dapat mengoperasikan TV. Model warna yang dipakai adalah model HSV.            Langkah awal pengujian pada penelitian ini dilakukan dengan setting ruangan agar pengambilan citra  mendapatkan hasil yang terbaik pada setiap ruangan. Hasil dari pengujian ini didapatkan nilai yang terbaik untuk pengoperasian TV dengan isyarat jari yaitu : untuk nilai intensitas cahaya sebesar 12 lux, nilai jarak terbaik sebesar 70 cm dan background terbaik berupa warna biru. Kata kunci—  TV, pengolahan citra, model warna HSV Abstract            The development computer technology and electronics very rapidly at this time makes people start looking for other solutions to perform activities easier and more practical. Technologies are developed and can be used at this time it is possible to facilitate the activities human beings to operate TV with finger gesture.            In this research, digital image processing of technology can be used to operate TV using webcam and computer as image processing. Input this research is hand image that catched by webcam on computer. Then hand image processed through image processing system to obtain results form value fingers. This value indicates the number equal to number fingers detected by webcam camera on monitor screen. Microcontroller function is receives serial data from results image processing and serves serial data conversion value into instructions can operate TV. Color model used in research is HSV.                First step testing this research is setting room in order to capture image for get best results in any room. Results of the research found best value for testing operation TV with finger gesture is : for value light intensity is 12 lux, best distance is 70 cm and best baskground is blue. Keywords— TV, image processing, HSV color model

scholarly journals Исследование изменений в спектре поглощения современных хлоринсодержащих препаратов для фотодинамической терапии и метиленового синего в результате воздействия светодиодного излучения с длиной волны 656± 10 nm

2020 ◽  
Vol 129 (7) ◽  
pp. 972
Author(s):  
А.В. Беликов ◽  
Ю.В. Семяшкина ◽  
С.Н. Смирнов ◽  
А.Д. Тавалинская
Keyword(s):  
Methylene Blue ◽  
Aqueous Solutions ◽  
Absorption Spectra ◽  
Exposure Time ◽  
Spectral Range ◽  
Color Model ◽  
Absorption Bands ◽  
Lab Color Model ◽  
Cie Lab ◽  
The Impact

The changes in absorption spectra of aqueous solutions of modern chlorine-containing photosensitizing preparations "Revixan" (Areal, Russia) and "Chloderm" (Chloderm, Russia) depending on the intensity of LED radiation with wavelength of 656 ± 10 nm and exposure time were studied in spectral range 600-700 nm. The parameters of the CIE Lab color model of the image of "Revixan" aqueous solution before and after LED exposure were investigated. The changes in absorption spectra of aqueous solutions of methylene blue with different initial concentrations arising after exposure to LED radiation with intensity of 180 ± 20 mW/cm2 were studied in the spectral range 400-900 nm. It was shown that the impact of LED radiation changes the absorption spectra of the studied preparations and increases the parameter L (lightness) of the CIE Lab color model for "Revixan". An increase in the LED radiation intensity and exposure time leads to a decrease in absorption for "Revixan" and "Chloderm" in spectral range 600-700 nm and to a shift of the peaks of absorption bands lying in this range towards a longer wavelength. The impact of LED radiation on aqueous solutions of methylene blue leads to a decrease in their absorption in spectral range 400-900 nm.

scholarly journals Region-based image retrieval using region of interest (ROI) according to incremental frame and clustering color image

2022 ◽  
Vol 9 (1) ◽  
pp. 138-147
Author(s):  
Mamat et al. ◽  
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Region Of Interest ◽  
Color Model ◽  
Retrieval Performance ◽  
Global Features ◽  
Hsv Color Model ◽  
High Level ◽  
Cie Lab ◽  
Large Image Databases

Content-based image retrieval involves the extraction of global feature images for their retrieval performance in large image databases. Extraction of global features image cause problem of the semantic gap between the high-level meaning and low-level visual features images. In this study RBIR, Region of Interest Based (ROI) Image Retrieval Using Incremental Frame of Color Image was proposed. It combines several methods, including filtering process, image partitioning using clustering and incremental frame formation, complementation law of theory set to generate ROI, NROI, or ER of the region. The concept of weighting as well as a significant query is also incorporated as a query strategy. Extensive experiments were also conducted on the Wang database and the color model selected was the CIE lab. Experimental results show the proposed method is efficient in image retrieval. The performance of the proposed method shows a better average IPR value of 3.51% compared to RGB and 22.92% with the HSV color model. Meanwhile, it also performs better by 36%, 5%, and 24% compared to methods CH (8,2,2), CH (8,3,3), and CH (16,4,4).

Shadow Removal Using Inpainting and Interpolation Technique

2014 ◽  
Vol 626 ◽  
pp. 32-37 ◽  
Author(s):  
Ajayan Lekshmi ◽  
C. Christopher Seldev
Keyword(s):  
Image Inpainting ◽  
Smooth Transition ◽  
Color Model ◽  
Shadow Removal ◽  
Bilinear Interpolation ◽  
False Color ◽  
Novel Approach ◽  
Reconstruction Quality ◽  
Hsv Color Model ◽  
Interpolation Technique

Shadows are viewed as undesired information that strongly affects images. Shadows may cause a high risk to present false color tones, to distort the shape of objects, to merge, or to lose objects. This paper proposes a novel approach for the detection and removal of shadows in an image. Firstly the shadow and non shadow region of the original image is identified by HSV color model. The shadow removal is based on exemplar based image inpainting. Finally, the border between the reconstructed shadow and the non shadow areas undergoes bilinear interpolation to yield a smooth transition between them. They would lead to a better fitting of the shadow and non shadow classes, thus resulting in a potentially better reconstruction quality.

scholarly journals Potentialities and Limitations of Research on VHRS Data: Alexander the Great’s Military Camp at Gaugamela on the Navkur Plain in Kurdish Iraq as a Test Case

2021 ◽  
Vol 13 (5) ◽  
pp. 904
Author(s):  
Tomasz Pirowski ◽  
Michał Marciak ◽  
Marcin Sobiech
Keyword(s):  
Image Processing ◽  
High Resolution ◽  
Vegetation Indices ◽  
Test Case ◽  
Linear Features ◽  
Negative Results ◽  
Research Perspectives ◽  
Military Camp ◽  
Different Color ◽  
Very High

This paper presents a selected aspect of research conducted within the Gaugamela Project, which seeks to finally identify the location of one of the most important ancient battles: the Battle of Gaugamela (331 BCE). The aim of this study was to discover material remains of the Macedonian military camp on the Navkur Plain in Kurdish Iraq. For this purpose, three very high resolution satellite (VHRS) datasets from Pleiades and WorldView-2 were acquired and subjected to multi-variant image processing (development of different color composites, integration of multispectral and panchromatic images, use of principle component analysis transformation, use of vegetation indices). Documentation of photointerpretation was carried out through the vectorization of features/areas. Due to the character of the sought-after artifacts (remnants of a large enclosure), features were categorized into two types: linear features and areal features. As a result, 19 linear features and 2 areal features were found in the study area of the Mahad hills. However, only a few features fulfilled the expected geometric criteria (layout and size) and were subjected to field groundtruthing, which ended in negative results. It is concluded that no traces have been found that could be interpreted as remnants of an earthen enclosure capable of accommodating around 47,000 soldiers. Further research perspectives are also suggested.

scholarly journals Image Colorization

2019 ◽  
Author(s):  
Дарья Михалина ◽  
Daria Mikhalina ◽  
Александр Кузьменко ◽  
Aleksandr Kuz'menko ◽  
Константин Дергачев ◽  
...  
Keyword(s):  
Neural Network ◽  
Urban Areas ◽  
Black And White ◽  
Number Of Layers ◽  
The Neural Network ◽  
Natural Landscapes ◽  
Video Card ◽  
Trained Neural Network ◽  
Lab Color Model ◽  
Cie Lab

The article discusses one of the latest ways to colorize a black and white image using deep learning methods. For colorization, a convolutional neural network with a large number of layers (Deep convolutional) is used, the architecture of which includes a ResNet model. This model was pre-trained on images of the ImageNet dataset. A neural network receives a black and white image and returns a colorized color. Since, due to the characteristics of ResNet, an input multiple of 255 is received, a program was written that, using frames, enlarges the image for the required size. During the operation of the neural network, the CIE Lab color model is used, which allows to separate the black and white component of the image from the color. For training the neural network, the Place 365 dataset was used, containing 365 different classes, such as animals, landscape elements, people, and so on. The training was carried out on the Nvidia GTX 1080 video card. The result was a trained neural network capable of colorizing images of any size and format. As example we had a speed of 0.08 seconds and an image of 256 by 256 pixels in size. In connection with the concept of the dataset used for training, the resulting model is focused on the recognition of natural landscapes and urban areas.

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