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.

Comparative Experiment of Color Reproduction Methods

2014 ◽  
Vol 1051 ◽  
pp. 967-970
Author(s):  
Qi Jia ◽  
Xu Liang Lv ◽  
Wei Dong Xu ◽  
Jiang Hua Hu ◽  
Xian Hui Rong
Keyword(s):  
Polynomial Regression ◽  
Rapid Development ◽  
Color Space ◽  
Digital Camera ◽  
Color Difference ◽  
High Tech ◽  
Comparative Experiment ◽  
Color Reproduction ◽  
Different Color ◽  
Time Image

Digital camera which has the advantage of real-time image transferring and easily processing is more and more widely used in the packaging and printing industry with the rapid development of high-tech electronics industry. However, the color in digital camera is not accurate which affect the application. To minimize the color difference between the color in the digital camera and the real color, the color reproduction methods is developing. The field comparative experiment is carried out to compare the performance of color reproduction methods, such as polynomial regression algorithm in different color space, and color checker passport. The results show that fourth order polynomial regression color reproduction in XYZ color space has the best performance.

Recent Advances in Medical Image Processing

2020 ◽  
pp. 1-14
Author(s):  
Zhen Huang ◽  
Qiang Li ◽  
Ju Lu ◽  
Junlin Feng ◽  
Jiajia Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Artificial Intelligence Technology ◽  
Deep Convolution Neural Network

<b><i>Background:</i></b> Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolution neural network is emerging as a principal machine learning method in computer vision and has received significant attention in medical imaging. <b><i>Key Message:</i></b> In this paper, we will review recent advances in artificial intelligence, machine learning, and deep convolution neural network, focusing on their applications in medical image processing. To illustrate with a concrete example, we discuss in detail the architecture of a convolution neural network through visualization to help understand its internal working mechanism. <b><i>Summary:</i></b> This review discusses several open questions, current trends, and critical challenges faced by medical image processing and artificial intelligence technology.

Algorithm for Real-Time Image Processing in the Robot Soccer

2011 ◽  
Vol 143-144 ◽  
pp. 737-741 ◽  
Author(s):  
Hai Bo Liu ◽  
Wei Wei Li ◽  
Yu Jie Dong
Keyword(s):  
Image Processing ◽  
Color Image ◽  
Vision System ◽  
Color Space ◽  
Color Image Segmentation ◽  
New Method ◽  
Robot System ◽  
Robot Soccer ◽  
Seed Fill ◽  
Time Image

Vision system is an important part of the whole robot soccer system.In order to win the game, the robot system must be more quick and more accuracy.A color image segmentation method using improved seed-fill algorithm in YUV color space is introduced in this paper. The new method dramatically reduces the work of calculation,and speeds up the image processing. The result of comparing it with the old method based on RGB color space was showed in the paper.The second step of the vision sub system is identification the color block that separated by the first step.A improved seed fill algorithm is used in the paper.The implementation on MiroSot Soccer Robot System shows that the new method is fast and accurate.

scholarly journals A G-Fresnel Optical Device and Image Processing Based Miniature Spectrometer for Mechanoluminescence Sensor Applications

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3528 ◽  
Author(s):  
Min ◽  
Kim ◽  
Song ◽  
Kim
Keyword(s):  
Image Processing ◽  
Visible Light ◽  
Color Space ◽  
Image Sensor ◽  
Processing Algorithm ◽  
Image Processing Algorithm ◽  
Spectral Image ◽  
Optical Device ◽  
Hsv Color Space ◽  
Color Space Conversion

This paper presents a miniature spectrometer fabricated based on a G-Fresnel optical device (i.e., diffraction grating and Fresnel lens) and operated by an image-processing algorithm, with an emphasis on the color space conversion in the range of visible light. The miniature spectrometer will be cost-effective and consists of a compact G-Fresnel optical device, which diffuses mixed visible light into the spectral image and a μ-processor platform embedded with an image-processing algorithm. The RGB color space commonly used in the image signal from a complementary metal–oxide–semiconductor (CMOS)-type image sensor is converted into the HSV color space, which is one of the most common methods to express color as a numeric value using hue (H), saturation (S), and value (V) via the color space conversion algorithm. Because the HSV color space has the advantages of expressing not only the three primary colors of light as the H but also its intensity as the V, it was possible to obtain both the wavelength and intensity information of the visible light from its spectral image. This miniature spectrometer yielded nonlinear sensitivity of hue in terms of wavelength. In this study, we introduce the potential of the G-Fresnel optical device, which is a miniature spectrometer, and demonstrated by measurement of the mechanoluminescence (ML) spectrum as a proof of concept.

Fluorescent-Based Temperature Measurement with Simple Compensation of Photo-Degradation Using Hydrogel-Tool and Color Space Conversion

2013 ◽  
Vol 25 (4) ◽  
pp. 596-602 ◽  
Author(s):  
Hisataka Maruyama ◽  
◽  
Taisuke Masuda ◽  
Fumihito Arai
Keyword(s):  
Color Space ◽  
Color Difference ◽  
Temperature Measurements ◽  
Blue Color ◽  
Photo Degradation ◽  
Color Space Conversion ◽  
System A ◽  
Red Color ◽  
Brightness Fluctuation ◽  
Increasing Temperature

We developed a method to obtain stable and longlifetime temperature measurements using a fluorescence micromeasurement system. A hydrogel tool containing nano-semiconductor quantum dots (Q-dots) was developed as a fluorescent temperature indicator. We used image processing to convert RGB information to other color information to compensate for photodegradation. The temperature was calibrated using the hydrogel tool in several color spaces, includingRGB(R: red,G: green,B: blue),HSV(H: hue,S: saturation,V: value (brightness)), andYCrCb(Y: brightness,Cr: red color difference,Cb: blue color difference). The calibration results showed thatR,G,B,Y, andCrdecreased monotonically with increasing temperature, whereasHandCbdid not decrease monotonically. The photodegradation analysis showed thatCrwas robust against the brightness fluctuation; however,R,G, andBstrongly affected the brightness fluctuation because these values included the brightness information. These results show that temperature measurements based onCrvalues are suitable to compensate for photodegradation and have a sensitivity of -1.3%/K and an accuracy of 0.3 K. These values are the same as those obtained using the fluorescence intensity method.

Color changes of ash wood (Fraxinus excelsior L.) caused by thermal modification in air and steam

2021 ◽  
Vol 116 ◽  
pp. 21-27
Author(s):  
Jakub Gawron ◽  
Monika Marchwicka
Keyword(s):  
Color Space ◽  
Color Difference ◽  
Thermal Modification ◽  
Fraxinus Excelsior ◽  
Space Model ◽  
Color Changes ◽  
Cielab Color Space ◽  
Before And After ◽  
Total Color Difference ◽  
Cielab Color

Color changes of ash wood (Fraxinus excelsior L.) caused by thermal modification in air and steam. Ash wood samples of 20x20x30 mm were subjected to thermal modification in different conditions. The thermal modification was conducted in air at 190 °C and in steam at 160 °C. For both environments modification lasted 2, 6 and 10 hours. Samples color parameters were measured before and after thermal modification on the basis of the mathematical CIELab color space model. Changes in all parameters (L, a and b) were observed, the highest in lightness (L) - darker color. The total color difference (ΔE) and chromaticity change (ΔC) were calculated for all samples. The highest value of ΔE was obtained for wood modified in the air at 190 °C for 10 h. The highest value of ΔC was obtained for wood modified in steam at 160 °C for 10 h. However, the value obtained for wood modified in the air at 190 °C for 10 h were only slightly lower.

Edge color difference detection of color image based on artificial intelligence technology

2021 ◽  
pp. 1-16
Author(s):  
Hao Li
Keyword(s):  
Artificial Intelligence ◽  
Color Image ◽  
Chromatic Aberration ◽  
Color Difference ◽  
Scale Space ◽  
Gray Scale ◽  
Difference Detection ◽  
Artificial Intelligence Technology ◽  
Image Edge ◽  
Detection Effect

In order to solve the problems of the traditional methods in detecting color image edge chromatic aberration, such as the poor accuracy of detection and the poor detection effect, a color image edge chromatic aberration detection method based on artificial intelligence technology is proposed. The approximate principal component analysis method is used to segment the color image and smooth the image denoising; The linear gray-scale transformation is applied to the color image to enlarge the smaller gray-scale space to the larger gray-scale space according to the linear relationship and obtain the edge information of the color image; The artificial intelligence technology is used to locate the edge sub-pixel of the image to complete the edge color difference detection of the color image. The experimental results show that the detection accuracy of the proposed method is about 98%, and the detection effect is good, which is feasible.

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