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 Ripeness Level Classification of Banana Fruit Based on Hue Saturate Value (HSV) Color Space Using K-Nearest Neighbor Algorithm

2021 ◽  
Vol 10 (2) ◽  
pp. 941-947
Keyword(s):  
Nearest Neighbor ◽  
Color Space ◽  
Banana Fruit ◽  
K Nearest Neighbor ◽  
Hsv Color Space ◽  
Feature Extraction Method ◽  
Sorting Process ◽  
K Nearest Neighbor Algorithm ◽  
Fruit Sorting

Many types of bananas are cultivated locally in Indonesia, including the Muli Banana or Musa Acuminata Linn. During the post-harvest period of banana fruit, there is a problem in the sorting process of bananas based on their level of maturity. The fruit sorting process manually uses the human eye, but it is ineffective due to decreased vision and the large quantity of fruit. Therefore, we need a system that can quickly classify the ripeness of the banana fruit. This study aims to create a system that can organize the maturity level of the banana fruit. The classification system designed using the HSV color feature extraction method and the K-Nearest Neighbor classification algorithm. After going through the testing phase, the system can classify bananas into three classes: unripe, ripe, and rotten. System testing used 30 test data images, and the results show 2 test images whose classification results are wrong and 28 other test images whose classification results are correct. Based on calculations, the accuracy achieved by the system is 93.333%.

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

A Correspondence-Free Color Chart Design for Color Calibration

2020 ◽  
Vol 2020 (1) ◽  
pp. 100-104
Author(s):  
Hakki Can Karaimer ◽  
Rang Nguyen
Keyword(s):  
Color Space ◽  
Fixed Number ◽  
New Approach ◽  
Color Chart ◽  
Color Calibration ◽  
Space Transformation ◽  
Color Space Transformation ◽  
Chart Design ◽  
Calibration Approach

Colorimetric calibration computes the necessary color space transformation to map a camera's device-specific color space to a device-independent perceptual color space. Color calibration is most commonly performed by imaging a color rendition chart with a fixed number of color patches with known colorimetric values (e. g., CIE XYZ values). The color space transformation is estimated based on the correspondences between the camera's image and the chart's colors. We present a new approach to colorimetric calibration that does not require explicit color correspondences. Our approach computes a color space transformation by aligning the color distributions of the captured image to the known distribution of a calibration chart containing thousands of colors. We show that a histogram-based colorimetric calibration approach provides results that are onpar with the traditional patch-based method without the need to establish correspondences.

A Biologically Inspired Saliency Priority Extraction Using Bayesian Framework

2019 ◽  
Vol 10 (2) ◽  
pp. 1-20
Author(s):  
Jila Hosseinkhani ◽  
Chris Joslin
Keyword(s):  
Nearest Neighbor ◽  
Saliency Detection ◽  
Color Space ◽  
Bayesian Framework ◽  
Nearest Neighbor Search ◽  
Video Frame ◽  
K Nearest Neighbor ◽  
Feature Maps ◽  
Ranking System ◽  
New Feature

In this article, the authors used saliency detection for video streaming problem to be able to transmit regions of video frames in a ranked manner based on their importance. The authors designed an empirically-based study to investigate bottom-up features to achieve a ranking system stating the saliency priority. We introduced a gradual saliency detection model using a Bayesian framework for static scenes under conditions that we had no cognitive bias. To extract color saliency, we used a new feature contrast in Lab color space as well as a k-nearest neighbor search based on k-d tree search technique to assign a ranking system into different colors according to our empirical study. To find the salient textured regions we employed contrast-based Gabor energy features and then we added a new feature as intensity variance map. We merged different feature maps and classified saliency maps using a Naive Bayesian Network to prioritize the saliency across a frame. The main goal of this work is to create the ability to assign a saliency priority for the entirety of a video frame rather than simply extracting a salient area which is widely performed.

A Novel Piece-Wise Linear Algorithm for Human Skin Segmentation

2015 ◽  
Vol 743 ◽  
pp. 317-320
Author(s):  
Ravi Subban ◽  
Pasupathi Perumalsamy ◽  
G. Annalakshmi
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Skin Detection ◽  
Color Spaces ◽  
Color Component ◽  
Skin Segmentation ◽  
Space Transformation ◽  
Color Space Transformation ◽  
Comprehensive Knowledge ◽  
Novel Method

This paper presents a novel method for skin segmentation in color images using piece-wise linear bound skin detection. Various color schemes are investigated and evaluated to find the effect of color space transformation over the skin detection performance. The comprehensive knowledge about the various color spaces helps in skin color modeling evaluation. The absence of the luminance component increases performance, which also supports in finding the appropriate color space for skin detection. The single color component produces the better performance than combined color component and reduces computational complexity.

scholarly journals Color Edge Detection Based on the Fusion of Intensity and Chromatic Differences

2020 ◽  
Vol 8 (6) ◽  
pp. 1038-1041
Keyword(s):  
Edge Detection ◽  
Color Space ◽  
Mathematical Methods ◽  
Image Intensity ◽  
Space Transformation ◽  
Color Space Transformation ◽  
Intensity Information

Edge detection is the name for a set of mathematical methods which target at classifying points in an image at which the image intensity varies sharply or, has discontinuities. The paper tries to find the solution for detecting color edges based on color and intensity information of two new images H-image and T-image crafted on color space transformation, that will produce two-resulted edges derivates of H-image and T-image and are at last coalesced to obtain final edge.

scholarly journals A Reliable Acoustic EMISSION Based Technique for the Detection of a Small Leak in a Pipeline System

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1472 ◽  
Author(s):  
Thang Bui Quy ◽  
Sohaib Muhammad ◽  
Jong-Myon Kim
Keyword(s):  
Acoustic Emission ◽  
Nearest Neighbor ◽  
Transformation Method ◽  
Operating Conditions ◽  
Training Data ◽  
Pipeline System ◽  
K Nearest Neighbor ◽  
Knn Classifier ◽  
Water Pipelines ◽  
Ae Signals

This paper proposes a reliable leak detection method for water pipelines under different operating conditions. This approach segments acoustic emission (AE) signals into short frames based on the Hanning window, with an overlap of 50%. After segmentation from each frame, an intermediate quantity, which contains the symptoms of a leak and keeps its characteristic adequately stable even when the environmental conditions change, is calculated. Finally, a k-nearest neighbor (KNN) classifier is trained using features extracted from the transformed signals to identify leaks in the pipeline. Experiments are conducted under different conditions to confirm the effectiveness of the proposed method. The results of the study indicate that this method offers better quality and more reliability than using features extracted directly from the AE signals to train the KNN classifier. Moreover, the proposed method requires less training data than existing techniques. The transformation method is highly accurate and works well even when only a small amount of data is used to train the classifier, whereas the direct AE-based method returns misclassifications in some cases. In addition, robustness is also tested by adding Gaussian noise to the AE signals. The proposed method is more resistant to noise than the direct AE-based method.

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