scholarly journals Underwater Digital Images Enhanced by L*A*B* Color Space and CLAHE on Gradient based Smoothing

2018 ◽  
Vol 11 (1) ◽  
pp. 55-68
Author(s):  
D. Anitha ◽  
S. MuthuKumaran
Keyword(s):  
Digital Images ◽  
Color Space ◽  
Gradient Based

The Research of the Digital Image-Processing of Colorful Painting

2014 ◽  
Vol 926-930 ◽  
pp. 3709-3712
Author(s):  
Yun Zhan ◽  
Jie Lei
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Images ◽  
Color Space ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Practical Application ◽  
Digital Image Processing Technique ◽  
Basic Concepts

The research of the digital image-processing of colorful painting is mainly to aim at the objective circumstances between the digital image and drawing flat vision distortion. This paper is based on the basic concepts of the digital image-processing technique. It expounds digital images advantage, collect, characteristics, recognition and the choice of the color space, the practical application of the digital image in the painting area in sequence. Through the study, we found computer has powerful ability to analyze management in the colorful painting field.

scholarly journals Evaluación de la descomposición de imágenes digitales, para la estimación indirecta de la turbidez en muestras de agua de cuerpos naturales

Bionatura ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 861-871
Keyword(s):  
Digital Images ◽  
San Antonio ◽  
Color Space ◽  
Correlation Equation ◽  
Research Use ◽  
Digital Cameras ◽  
Single Lens ◽  
Rgb Color Space ◽  
San Carlos ◽  
Analyze Data

Digital cameras offer the possibility to capture and analyze data in a simple and affordable way, allowing quick and in some cases on-site shots of the study object. This research use a DSLR (Digital Single Lens Reflex for its acronym in English) and a CCD (Charged coupled device for its acronym in English) to capture digital images analyzed under two methodologies using a MATLAB script, water samples Obtained from two areas of Antioquia Colombia, and three water bodies: rio San Carlos and brook San Antonio in San Carlos (Ant), in addition to brook La Marinilla in Marinilla (Ant). In addition to the foregoing, two capture environments were built to capture digital images and two trials in order to establish a correlation equation between the data obtained in light intensity and turbidity (NTU). Finding that there is a correlation between turbidity and the intensity of light extracted from digital images with R ² = 0.97428 for the first trial, and above 0.9 for the G-channel in the RGB color space.

scholarly journals A NOVEL FACE DETECTION AND TRACKING ALGORITHM IN REAL- TIME VIDEO SEQUENCES

2013 ◽  
pp. 25-29
Author(s):  
NAGAPRIYA KAMATH K ◽  
ASHWINI HOLLA ◽  
SUBRAMANYA BHAT
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Template Matching ◽  
Digital Images ◽  
Color Space ◽  
Color Model ◽  
Hsv Color Space ◽  
Detection And Tracking ◽  
Skin Color Model ◽  
Human Faces

Face detection is a image processing technology that determines the location and size of human faces in digital images or video. This module precedes face recognition systems that plays an important role in applications such as video surveillance, human computer interaction and so on. This proposed work focuses mainly on multiple face detection technique, taking into account the variations in digital images or video such as face pose, appearances and illumination. The work is based on skin color model in YCbCr and HSV color space. First stage of this proposed method is to develop a skin color model and then applying the skin color segmentation in order to specify all skin regions in an image. Secondly, a template matching is done to assure that the segmented image does not contain any non-facial part. This algorithm works to be robust and efficient.

Using Unmanned Aerial Vehicle to Obtain Digital Images and Estimating In-Situ Soil Water Content

2020 ◽  
Author(s):  
Ching-Hsiung Wang ◽  
Hong-Ru Lin ◽  
Jyun-Lin Chen ◽  
Shao-Yang Huang ◽  
Jet-Chau Wen
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Digital Images ◽  
Color Space ◽  
Gravimetric Method ◽  
Time Domain Reflectometry ◽  
Soil Samples ◽  
Cost Efficient ◽  
Space Data

<p>Soil water content (SWC) is a vital factor for soil sciences. Nowadays, there are many methods for estimating SWC, including the Time-domain reflectometry (TDR) and the Gravimetric method. Nevertheless, most of them may cause damages to soil structure and require a large workforce and resources. The optical method is a non-destructive and cost-efficient; therefore, recommended for SWC estimations.</p><p>This study analyses soil samples at the field site, as well as it uses aerial photo-shooting to obtain the digital image distribution of surface soil. Both soil samples and digital images were categorized into groups; 9 in total, depending on time parameters (one group equals one day). More specifically, the gravimetric method was selected for the SWC measurements in the laboratory, while the images were modified in such a way so to match the CIE 1931 XYZ color space resolution for further calculations. Then, comparing the CIE 1931 XYZ color space data with the Soil Water Content correlation of 9 groups by validation.</p><p>According to the findings, the sensitivity of CIE 1931 XYZ color space in SWC alternations is high. Additionally, it can be observed that the SWC result data of the model are similar to the SWC measurements; therefore, the CIE 1931 XYZ color space can be applied to agriculture and disaster prevention, and it is a cost-efficient method for SMC estimations, and it can provide several benefits.</p>

scholarly journals Color Measurement of Segmented Printed Fabric Patterns in Lab Color Space from RGB Digital Images

2019 ◽  
Vol 05 (01) ◽  
pp. 1-18 ◽  
Author(s):  
Charles Kumah ◽  
Ning Zhang ◽  
Rafiu King Raji ◽  
Ruru Pan
Keyword(s):  
Digital Images ◽  
Color Space ◽  
Color Measurement ◽  
Lab Color Space

scholarly journals Deteksi Kematangan Buah Jambu Kristal Berdasarkan Fitur Warna Menggunakan Metode Transformasi Ruang Warna Hsv (Hue Saturation Value) Dan K-Nearest Neighbor

2021 ◽  
Vol 1 (2) ◽  
pp. 76-88
Author(s):  
Adhi Wibowo ◽  
Diwahana Mutiara Candrasari Hermanto ◽  
Kusuma Indah Lestari ◽  
Hadion Wijoyo
Keyword(s):  
Nearest Neighbor ◽  
Digital Images ◽  
Color Space ◽  
Transformation Method ◽  
K Nearest Neighbor ◽  
Hsv Color Space ◽  
Guava Fruit ◽  
Space Transformation ◽  
Color Space Transformation ◽  
Good Percentage

Guava has properties that are easily damaged, improper handling of guava fruit can result in a decrease in quality and quality. In general, to measure maturity is still done manually, the weakness of this method is the level of accuracy that is not consistent and tends to experience errors. Utilization of images is very important to determine the maturity of guava fruit by utilizing digital images. With the existence of digital images, to determine the maturity of guava fruit based on its color, it can be done computing (technology-based), namely by applying image processing using the HSV (Hue, Saturation, Value) color space transformation method. The HSV (Hue, Saturation, Value) color model groups the intensity components of the carried color information (hue and saturation) in image colors. The results of the ripeness detection can be seen in each test with a percentage value of 91.67% for the ripe guava category, 90% for the raw guava fruit category. The percentage value for testing the overall data has a good percentage value which is influential in detecting the maturity of crystal guava, which is 95%. So it can be concluded that the detection of ripeness of crystal guava fruit can be done by applying the HSV color space transformation method.

scholarly journals Estimation of percentage of impurities in coffee using a computer vision system

2022 ◽  
Vol 26 (2) ◽  
pp. 142-148
Author(s):  
Anderson G. Costa ◽  
Eudócio R. O. da Silva ◽  
Murilo M. de Barros ◽  
Jonatthan A. Fagundes
Keyword(s):  
Linear Models ◽  
Digital Images ◽  
Vision System ◽  
Color Space ◽  
Principal Component Regression ◽  
Principal Component ◽  
Coffee Beans ◽  
Hsi Color Space ◽  
Pcr Method ◽  
Data Group

ABSTRACT The quality and price of coffee drinks can be affected by contamination with impurities during roasting and grinding. Methods that enable quality control of marketed products are important to meet the standards required by consumers and the industry. The purpose of this study was to estimate the percentage of impurities contained in coffee using textural and colorimetric descriptors obtained from digital images. Arabica coffee beans (Coffea arabica L.) at 100% purity were subjected to roasting and grinding processes, and the initially pure ground coffee was gradually contaminated with impurities. Digital images were collected from coffee samples with 0, 10, 30, 50, and 70% impurities. From the images, textural descriptors of the histograms (mean, standard deviation, entropy, uniformity, and third moment) and colorimetric descriptors (RGB color space and HSI color space) were obtained. The principal component regression (PCR) method was applied to the data group of textural and colorimetric descriptors for the development of linear models to estimate coffee impurities. The selected models for the textural descriptors data group and the colorimetric descriptors data group were composed of two and three principal components, respectively. The model from the colorimetric descriptors showed a greater capacity to estimate the percentage of impurities in coffee when compared to the model from the textural descriptors.

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