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.

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 Quaternion-Based Improved Artificial Bee Colony Algorithm for Color Remote Sensing Image Edge Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Dujin Liu ◽  
Huajun Wang ◽  
Sen Wang ◽  
Guolin Pu ◽  
Xiaoya Deng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Artificial Bee Colony ◽  
Color Image ◽  
Image Data ◽  
Remote Sensing Image ◽  
Bee Colony ◽  
Image Edge ◽  
Image Details ◽  
Detection Effect

As the color remote sensing image has the most notable features such as huge amount of data, rich image details, and the containing of too much noise, the edge detection becomes a grave challenge in processing of remote sensing image data. To explore a possible solution to the urgent problem, in this paper, we first introduced the quaternion into the representation of color image. In this way, a color can be represented and analyzed as a single entity. Then a novel artificial bee colony method named improved artificial bee colony which can improve the performance of conventional artificial bee colony was proposed. In this method, in order to balance the exploration and the exploitation, two new search equations were presented to generate candidate solutions in the employed bee phase and the onlookers phase, respectively. Additionally, some more reasonable artificial bee colony parameters were proposed to improve the performance of the artificial bee colony. Then we applied the proposed method to the quaternion vectors to perform the edge detection of color remote sensing image. Experimental results show that our method can get a better edge detection effect than other methods.

Sign in / Sign up

Export Citation Format

Share Document