The Detection Method of Lane Line Based on the Improved Otsu Threshold Segmentation

2015 ◽  
Vol 741 ◽  
pp. 354-358 ◽  
Author(s):  
Yang Shan Tang ◽  
Dao Hua Xia ◽  
Gui Yang Zhang ◽  
Li Na Ge ◽  
Xin Yang Yan
Keyword(s):  
Image Segmentation ◽  
Processing Time ◽  
Detection Method ◽  
Segmentation Algorithm ◽  
Test Results ◽  
Threshold Segmentation ◽  
Otsu Method ◽  
Time Requirements ◽  
Lane Line ◽  
Center Distance

For overcoming the shortage of Otsu method, proposed an improved Otsu threshold segmentation algorithm. On the basis of Otsu threshold segmentation algorithm, the gray level was divided into two classes according to the image segmentation, to determine the best threshold by comparing their center distance, so as to achieve peak line recognition under the condition of multiple gray levels. Then did experiments on image segmentation of the lane line with MATLAB by traditional Otsu threshold segmentation algorithm and the improved algorithm, the threshold of traditional Otsu threshold segmentation algorithm is 144 and the threshold of the improved Otsu threshold segmentation algorithm is 131, the processing time is within 0.453 s. Test results show that the white part markings appear more, the intersection place of white lines and the background is more clear, so this method can identify lane markings well and meet the real-time requirements.

Review of Otsu Segmentation Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 1959-1961 ◽  
Author(s):  
Yan Xue Dong
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Simple Calculation ◽  
Segmentation Algorithm ◽  
Image Thresholding ◽  
Otsu Method

Image segmentation is the key step in the process from image processing to image analysis. Otsu method is one of the most successful methods for image thresholding because of its simple calculation. Otsu method can select threshold automatically and divide the object from the background in the image. In this paper, various Otsu algorithm are studied.

scholarly journals Texture Detection of Aluminum Foil Based on Top-Hat Transformation and Connected Region Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Fei He ◽  
Yuxing Hu ◽  
Jian Wang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Digital Image Processing ◽  
Detection Method ◽  
Aluminum Foil ◽  
Quantitative Detection ◽  
Test Results ◽  
Detection Time ◽  
Texture Detection ◽  
Better Than

A new method of texture detection for aluminum foil based on digital image processing technology is proposed. Top-hat transformation and image segmentation technology based on the connected domain are used to change the method of determining texture fraction by using human experience. Compared with the brightness method, pit detection method, and EBSD technology, this method can complete quantitative detection efficiently, automatically, and accurately, and reduce the detection time and manpower. It eliminates the instability of manual detection and ensures the accuracy of detection. By this method, the error of test results can be controlled within 1.6%, which is much better than 7.3% of the brightness method and 4% of the pitting method. It provides more accurate test results for the production process control of aluminum foil.

Research of Image Segmentation Based on Iterative Threshold

2014 ◽  
Vol 701-702 ◽  
pp. 330-333
Author(s):  
Lei Shao ◽  
Yi Mu ◽  
Peng Guo ◽  
Jun Liu ◽  
Guo Ling Dong ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Image Recognition ◽  
Segmentation Algorithm ◽  
Segmentation Method ◽  
Threshold Segmentation ◽  
Target Character ◽  
The One ◽  
Definition Of ◽  
Iterative Threshold

Image segmentation is the key step in image recognition,the result of segmentation affects the one of recognition directly.The article introduces the concept and detailed definition of the image segmentation. The segmentation algorithm of iterative threshold in detail. According to the intrinsic characteristics of weed images, just can use the iteration threshold segmentation method, and implements them by Matlab programme, then processes three weed images, respectively to obtain effective results , and establishes a good base for the pick-up of the target character.

Evaluation and Research on Block-Division Based Image Segmentation Algorithm

2014 ◽  
Vol 525 ◽  
pp. 723-726
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Ying Sun
Keyword(s):  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Experimental Results ◽  
The Other ◽  
Image Recovery ◽  
Threshold Method ◽  
Threshold Segmentation ◽  
Segmentation Algorithms ◽  
Image Segmentation Algorithm

The block-division based segmentation algorithm is raised in this paper, which overcomes the shortcomings of the whole and single threshold method. Each block is processed with two-value segmentation algorithm, and finally, all blocks are combined for image recovery. In order to evaluate the performance of block-division based segmentation algorithms, some experiments are carried out by using Matlab 7.0. Experimental results show that block-division based segmentation algorithms can perform well and get a better result than the other threshold segmentation algorithms.

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