scholarly journals Special Edition. Image Processing in the Multimedia Age. A Color Difference Transform to Uniform Color Space for Image Feature Analysis.

1995 ◽  
Vol 49 (3) ◽  
pp. 360-362
Author(s):  
Mei Kodama ◽  
Tsuyoshi Hanamura ◽  
Hideyoshi Tominaga
Keyword(s):  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Image Feature ◽  
Feature Analysis ◽  
Special Edition

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

scholarly journals A perception-based color space for illumination-invariant image processing

2008 ◽  
Vol 27 (3) ◽  
pp. 1-7 ◽  
Author(s):  
Hamilton Y. Chong ◽  
Steven J. Gortler ◽  
Todd Zickler
Keyword(s):  
Image Processing ◽  
Color Space ◽  
Illumination Invariant

scholarly journals Automobile tire life prediction based on image processing and machine learning technology

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110027
Author(s):  
Jianchen Zhu ◽  
Kaixin Han ◽  
Shenlong Wang
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Life Prediction ◽  
Traffic Accidents ◽  
Confusion Matrix ◽  
Small Sample ◽  
Image Feature ◽  
Engineering Practice ◽  
Learning Technology ◽  
K Nearest Neighbor

With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts of automobiles, and their wear causes a large number of traffic accidents. Therefore, predicting tire life has become one of the key factors determining vehicle safety. This paper presents a tire life prediction method based on image processing and machine learning. We first build an original image database as the initial sample. Since there are usually only a few sample image libraries in engineering practice, we propose a new image feature extraction and expression method that shows excellent performance for a small sample database. We extract the texture features of the tire image by using the gray-gradient co-occurrence matrix (GGCM) and the Gauss-Markov random field (GMRF), and classify the extracted features by using the K-nearest neighbor (KNN) classifier. We then conduct experiments and predict the wear life of automobile tires. The experimental results are estimated by using the mean average precision (MAP) and confusion matrix as evaluation criteria. Finally, we verify the effectiveness and accuracy of the proposed method for predicting tire life. The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.

Color image design based on machine learning and SVM algorithm

2020 ◽  
pp. 1-12
Author(s):  
Li Bo
Keyword(s):  
Image Processing ◽  
Graphic Design ◽  
Management System ◽  
Computer Network ◽  
Color Image ◽  
Color Difference ◽  
Color Management ◽  
Development Mode ◽  
Operation Process ◽  
Svm Algorithm

In today’s society, graphic design, as a popular image processing technology, plays an increasingly important role in people’s lives. In the specific operation process of graphic design, It is no longer restricted to the traditional development mode, such as file format and other factors. With the development of computer network technology, people promote the development of graphic design by constructing color management system. At the same time, the construction of color management system can help people to change colors and define colors when they process image information and output pictures. In the process of printing pictures, in order to make the colors used in the design process clearly printed out and without color difference, there are still many problems to be considered. First, we need to consider the unexpected situation and the complexity of image processing. Based on the introduction of computer learning, this paper will discuss and study the development of graphic design by SVM theory.

Evaluation of change in color of denim fabric after various industrial washing processes with different fiber composition

2014 ◽  
Vol 11 (2) ◽  
pp. 107-110
Author(s):  
Mushtaq Mangat ◽  
A. Abbasi ◽  
Jakub Wiener
Keyword(s):  
Color Space ◽  
Color Difference ◽  
Fiber Composition ◽  
Obvious Change ◽  
Washing Machines ◽  
Difference Formula ◽  
Denim Fabric ◽  
Cielab Color

Traditional denim made by using 100% cotton and novel denim made by using cotton in warp and spun PP in the weft were treated in 11 different ways on industrial garment washing machines with the help of various textile auxiliaries and pumice. There is an obvious change in color of denim. This change was measured by using Spectrophotometer. Reflectance was taken as a variable to observe the intensity of change. Color difference was measured by using the CIELab color difference formula 1976. Color space coordinates (L*, a*, b*) and color difference ΔE were calculated between the untreated denim and treated denim.

scholarly journals A Hybrid Methodology for Automation the Diagnosis of Leukemia Based on Quantitative and Morphological Feature Analysis

2018 ◽  
Vol 12 (3) ◽  
pp. 56 ◽  
Author(s):  
Hussam N. Fakhouri ◽  
Saleh H. Al-Sharaeh
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
White Blood Cells ◽  
Medical Image Processing ◽  
Initial Diagnosis ◽  
Feature Analysis ◽  
Microscopic Images ◽  
Different Types ◽  
The Status ◽  
Blast Cells

Recent year’s witnessed a huge revolution for developing an automated diagnosis for different disease such as cancer using medical image processing. Many researches have been dedicated to achieve this goal. Analyzing medical microscopic histology images provide us with large information about the status of patient and the progress of diseases, help to determine if the tissue have any pathological changes. Automation of the diagnosis of these images will lead to better, faster and enhanced diagnosis for different hematological and histological tissue images such as cancer. This paper propose an automated methodology for analyzing cancer histology and hematology microscopic images to detect leukemia using image processing by combining two diagnosis procedures initial and advance; the initial diagnosis depend on the percentage of the white blood cells in microscopic images affected by leukemia as indicator for the existence of leukemia in the blood smear sample. Whereas, the advance diagnosis classifying the leukemia according into different types using feature bag classifier. The experimental results showed that the proposed methodology initial diagnosis is able to detect leukemia images and differentiate it from samples that do not have leukemia. While, advance diagnosis it is able to detect and classify most leukemia types and differentiate between acute and chronic, but in some cases in the chronic leukemia where the percent of blast cells and shape are similar; it gave a diagnosis of the type of leukemia to the most similar type.

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