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.

New method of morphological color image processing

2010 ◽  
Vol 30 (8) ◽  
pp. 2101-2104
Author(s):  
Hong-zhong TANG ◽  
Hui-xian HUANG ◽  
Xue-feng GUO ◽  
Ye-wei XIAO
Keyword(s):  
Image Processing ◽  
Color Image ◽  
New Method ◽  
Color Image Processing

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 IDENTIFICATION OF MEGALOBLASTIC ANEMIA CELLS THROUGH THE USE OF IMAGE PROCESSING TECHNIQUES

2018 ◽  
Vol 4 (3) ◽  
pp. 1-5 ◽  
Author(s):  
Asaad Babker ◽  
Vyacheslav Lyashenko
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Blood Smear ◽  
Color Image ◽  
Megaloblastic Anemia ◽  
Cell Structure ◽  
Color Space ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Blood Smear Image

Objective: Our aim is to show the possibility of using different image processing techniques for blood smear analysis. Also our aim is to determine the sequence of image processing techniques to identify megaloblastic anemia cells. Methods: We consider blood smear image. We use a variety of image processing techniques to identify megaloblastic anemia cells. Among these methods, we distinguish the modification of the color space and the use of wavelets. Results: We developed a sequence of image processing techniques for blood smear image analysis and megaloblastic anemia cells identification. As a characteristic feature for megaloblastic anemia cells identification, we consider neutrophil image structure. We also use the morphological methods of image analysis in order to reveal the nuclear lobes in neutrophil structure. Conclusion: We can identify the megaloblastic anemia cells. To do this, we use the following sequence of blood smear image processing: color image modification, change of the image contrast, use of wavelets and morphological analysis of the cell structure. 

scholarly journals Extraction of urban buildings using color image processing and morphological operations.

2021 ◽  
Vol 11 (1) ◽  
pp. 45-66
Author(s):  
Mete Durlu ◽  
Ozan Eski ◽  
Emre Sumer
Keyword(s):  
Image Processing ◽  
Satellite Images ◽  
Color Image ◽  
Color Image Segmentation ◽  
Morphological Operations ◽  
Morphological Image Processing ◽  
Urban Buildings ◽  
Colour Image ◽  
High Resolution Satellite Images

In many geospatial applications, automated detection of buildings has become a key concern in recent years. Determination of building locations provides great benefits for numerous geospatial applications such as urban planning, disaster management, infrastructure planning, environmental monitoring. The study  aims to present a practical technique for extracting the buildings from high-resolution satellite images using color image segmentation and binary morphological image processing. The proposed method is implemented on satellite images of 4 different selected study areas of the city of Batikent, Ankara.  According to experiments conducted on the study areas, overall accuracy, sensitivity, and F1 values were computed to be on average, respectively. After applying morphological operations, the same metrics are calculated . The results show that the determination of urban buildings can be done more successfully with the suitable combination of morphological operations using rectangular structuring element. Keywords: Building Extraction; Colour Image Processing;Colour space conversion; Image Morphology; Remote Sensing        

A Robustness Watermarking Algorithm for Digital Color Image Print-Scan Process

2013 ◽  
Vol 333-335 ◽  
pp. 992-997
Author(s):  
Yun Lu Ge ◽  
Hui Han ◽  
Xiao Dong Sun ◽  
Sheng Pin Wang ◽  
Sheng Yun Ji
Keyword(s):  
Color Image ◽  
Random Noise ◽  
Color Space ◽  
Low Frequency ◽  
New Method ◽  
Color Spaces ◽  
Image Watermark ◽  
Color Space Conversion ◽  
Frequency Components ◽  
Orthogonal Transform

Most of watermarking algorithms are for digital grey image, which are not robust against the attacks of print-scan process, and the embedded information capacity is small. To solve these problem, a new method based on DWT transform and Walsh orthogonal transform for the print-scan process of digital color image was proposed. The method chosed the color spaces conversion from RGB to CIEL*a*b* for digital color image. The low frequency components of the DWT transform image was embed the watermark. The results show that the correlation of watermark is improved using Walsh orthogonal transform, the watermark extraction rate is high and image watermark is distinct and readable after print-scan process. And this method is robust against the various attacks of the print-scan process, such as color space conversion, image halftone, D/A conversion, A/D conversion, scaling, rotation, cropping, skew, and random noise signals.

scholarly journals Color Image Segmentation Based on Different Color Space Models Using Automatic GrabCut

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Dina Khattab ◽  
Hala Mousher Ebied ◽  
Ashraf Saad Hussein ◽  
Mohamed Fahmy Tolba
Keyword(s):  
Image Segmentation ◽  
Comparative Study ◽  
Color Image ◽  
Color Space ◽  
User Interaction ◽  
Color Image Segmentation ◽  
Space Representation ◽  
Color Spaces ◽  
Different Color ◽  
The Comparative Study

This paper presents a comparative study using different color spaces to evaluate the performance of color image segmentation using the automatic GrabCut technique. GrabCut is considered as one of the semiautomatic image segmentation techniques, since it requires user interaction for the initialization of the segmentation process. The automation of the GrabCut technique is proposed as a modification of the original semiautomatic one in order to eliminate the user interaction. The automatic GrabCut utilizes the unsupervised Orchard and Bouman clustering technique for the initialization phase. Comparisons with the original GrabCut show the efficiency of the proposed automatic technique in terms of segmentation, quality, and accuracy. As no explicit color space is recommended for every segmentation problem, automatic GrabCut is applied withRGB,HSV,CMY,XYZ, andYUVcolor spaces. The comparative study and experimental results using different color images show thatRGBcolor space is the best color space representation for the set of the images used.

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