Color Spaces Advantages and Disadvantages in Image Color Clustering Segmentation

Chavolla, Edgar; Zaldívar, Daniel; Cuevas, Erik; Pérez, Marco A.

doi:10.1007/978-3-319-63754-9_1

Cited by 38 publications

(17 citation statements)

References 21 publications

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“…This causes some incorrect color interpretations. The second issue occurs when the maximum and minimum RGB values sum two (saturation value undefined) [ 16 ]. The analytical performance of a CSA is strongly affected by the choice of chemo-responsive dyes [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

pH Colorimetric Sensor Arrays: Role of the Color Space Adopted for the Calculation of the Prediction Error

Pastore

Badocco

Bogialli

et al. 2020

Sensors

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A pH colorimetric sensor array was prepared and characterized by combining tetrabromophenol blue (TBB) and bromothymol blue (BB) embedded in organically modified silicate (OrMoSil) spots polyvinylidene fluoride (PVDF)-supported. The signal was based on the Hue profile (H). The individual calibrations of TBB and BB showed precisions with minimum values of 0.012 pH units at pH = 2.196 for TBB and 0.018 at pH = 6.692 for BB. The overall precision of 10 spots of the mixture TBB/BB increased in the pH range of 1.000–8.000 from a minimum value of pH precision of 0.009 at pH = 2.196 to 0.012 at pH = 6.692, with the worst value of 0.279 pH units at pH = 4.101. The possibility to produce an array with much more than 10 spots allows for improving precision. The H analytical performance was compared to those of other color spaces such as RGB, Lab, and XYZ. H was the best one, with prediction error in the range of 0.016 to 0.021 pH units, at least three times lower than the second-best (x coordinate), with 0.064 pH units. These results were also confirmed by the calculation of the main experimental contributions to the pH prediction error, demonstrating the consistency of the proposed calculation approach.

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Section: Introductionmentioning

confidence: 99%

pH Colorimetric Sensor Arrays: Role of the Color Space Adopted for the Calculation of the Prediction Error

Pastore

Badocco

Bogialli

et al. 2020

Sensors

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“…Some researchers have suggested shadow detection of outdoor images [20] and infrared images [21]. In this study, a shadow detection algorithm using LAB [22] color space and statistical values [23] was proposed as shown in Figure 5. The shadow detection method consists of the following steps.…”

Section: Shadow Detectionmentioning

confidence: 99%

The Development of a Defect Detection Model from the High-Resolution Images of a Sugarcane Plantation Using an Unmanned Aerial Vehicle

Tanut

Riyamongkol

2020

Information

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This article presents a defect detection model of sugarcane plantation images. The objective is to assess the defect areas occurring in the sugarcane plantation before the harvesting seasons. The defect areas in the sugarcane are usually caused by storms and weeds. This defect detection algorithm uses high-resolution sugarcane plantations and image processing techniques. The algorithm for defect detection consists of four processes: (1) data collection, (2) image preprocessing, (3) defect detection model creation, and (4) application program creation. For feature extraction, the researchers used image segmentation and convolution filtering by 13 masks together with mean and standard deviation. The feature extraction methods generated 26 features. The K-nearest neighbors algorithm was selected to develop a model for the classification of the sugarcane areas. The color selection method was also chosen to detect defect areas. The results show that the model can recognize and classify the characteristics of the objects in sugarcane plantation images with an accuracy of 96.75%. After the comparison with the expert surveyor’s assessment, the accurate relevance obtained was 92.95%. Therefore, the proposed model can be used as a tool to calculate the percentage of defect areas and solve the problem of evaluating errors of yields in the future.

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“…This shading space can communicate a more extensive shading extent or range than RGB and HSV because it includes all perceivable colors and that means its gamut exceeds than RGB color model [15]. One of the most important attributes of the Lab color space is the device independency, so is used as an interchange format between different devices [16].…”

Section: -Color Spacesmentioning

confidence: 99%

Low Light Underwater Image Enhancement Algorithm Based on Fusion Histogram Equalization Technique

Haleot

2020

IJASRE

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One of the main challenges during monitoring the underwater images is unbalanced illumination and poor contrast. These problems added a hazy appearance in the visuals for underwater images. Effective underwater image enhancement fusion algorithm founded on CLAHE technique for deferent color spaces were used in this article. At first, convert the original underwater image from RGB space to HSV space. Then, the algorithm separately applies CLAHE in this color space. After that, the HSV enhanced images are converted back to RGB spaces. This enhanced image convert to LAB space and sharpening filter is applied to demonstrate the contrast and converted back to RGB spaces. The results of investigational work show that, contrasted and the customary enhancement methods of underwater images, such that MSE, UQI and PSNR, of the proposed strategy are joined ideally. And has been seen that proposed framework superior to previously existing strategies for improvement of underwater image. It can be seen that the details proposed method of image that more abundant, image brightness is improved, and color is more distinct and the image details are clearer.

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Color Spaces Advantages and Disadvantages in Image Color Clustering Segmentation

Cited by 38 publications

References 21 publications

pH Colorimetric Sensor Arrays: Role of the Color Space Adopted for the Calculation of the Prediction Error

pH Colorimetric Sensor Arrays: Role of the Color Space Adopted for the Calculation of the Prediction Error

The Development of a Defect Detection Model from the High-Resolution Images of a Sugarcane Plantation Using an Unmanned Aerial Vehicle

Low Light Underwater Image Enhancement Algorithm Based on Fusion Histogram Equalization Technique

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