An Image Segmentation Method Based on Adaptability Threshold

2013 ◽  
Vol 734-737 ◽  
pp. 2912-2916
Author(s):  
Hui Li ◽  
Ping He
Keyword(s):  
Image Segmentation ◽  
Strain Measurement ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Important Application ◽  
Original Image ◽  
Segmentation Method ◽  
One Dimensional ◽  
The One ◽  
Application Fields

Automation strain measurement of the sheet metal deforming becomes one of the important application fields of computer vision. The algorithm of image segmentation based on adaptability threshold was presented for image segmentation of metal steel. In order to validate the proposed method, it is tested and compared with Ostu method and the one-dimensional maximum entropy method. Experiment results indicate that the method is simple and effective, and has an advantage of reservation of the main features of the original image.

scholarly journals Research on Target Segmentation Method Based on Maximum Entropy Method

2019 ◽  
Vol 612 ◽  
pp. 032001
Author(s):  
Weiwei Gao ◽  
Hongyun Wang ◽  
Dan Fang ◽  
Yi Wang ◽  
Hongyan Zhang ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Segmentation Method ◽  
Target Segmentation

Vein Image Segmentation Based on One-Dimentional Gray and Filter Erosion Method

2014 ◽  
Vol 635-637 ◽  
pp. 1049-1055 ◽  
Author(s):  
Xun Zhang ◽  
Yong Hong Guo ◽  
Gang Li ◽  
Jin Long He
Keyword(s):  
Image Segmentation ◽  
Test Results ◽  
Segmentation Method ◽  
Low Contrast ◽  
One Dimensional ◽  
Hand Vein ◽  
Binary Morphology

For the low contrast and serious noises, a fast image segmentation method based on one-dimensional gray segmentation, binary morphology erosion and area elimination is proposed. Since veins are thin and long, the vein image can be easily distinguished from background by judging the gray difference from nearby pixels when they are vertically or horizontally scanned. Then the processed image is diposed with erosion and area elimination to filter the noise. According to test results on the hand vein images which got from the equipment constructed by ourselves, it is proved that the method is more suitable for hand vein image segmentation than others and clear vein images can be botained quickly.

Implementation and Evaluation of the Image Segmentation Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 1314-1317
Author(s):  
Li Jun Chen ◽  
Yong Jie Ma
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Maximum Entropy ◽  
Entropy Method ◽  
Segmentation Algorithm ◽  
Segmentation Method ◽  
Improved Genetic Algorithm ◽  
And Performance ◽  
Standard Genetic Algorithm ◽  
Better Than

In order to achieve better image segmentation and evaluate the segmentation algorithm, a segmentation method based on 2-D maximum entropy and improved genetic algorithm is proposed in this paper, and the ultimate measurement accuracy criterion is adopted to evaluate the performance of the algorithm. The experimental results and the evaluation results show that segmentation results and performance of the proposed algorithm are both better than the segmentation method based on 2-D maximum entropy method and the standard genetic algorithm. The segmentation of the proposed algorithm is complete and spends less time; it is an effective method for image segmentation.

Combining global and local scaling (3D) methods to detect pore spaces in CT soil images

2021 ◽  
Author(s):  
Juan José Martin Sotoca ◽  
Antonio Saa Requejo ◽  
Sergio Zubelzu ◽  
Ana M. Tarquis
Keyword(s):  
Image Segmentation ◽  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Pore Space ◽  
Entropy Method ◽  
Local Scaling ◽  
Fractal Properties ◽  
Segmentation Methods ◽  
Soil Pore Space ◽  
Global And Local

<p>The characterization of the spatial distribution of soil pore structures is essential to obtain different parameters that will be useful in developing predictive models for a range of physical, chemical, and biological processes in soils. Over the last decade, major technological advances in X-ray computed tomography (CT) have allowed for the investigation and reconstruction of natural porous soils at very fine scales. Delimiting the pore structure (pore space) from the CT soil images applying image segmentation methods is crucial when attempting to extract complex pore space geometry information.</p><p>Different segmentation methods can result in different spatial distributions of pores influencing the parameters used in the models [1]. A new combined global & local segmentation (2D) method called “Combining Singularity-CA method” was successfully applied [2]. This method combines a local scaling method (Singularity-CA method) with a global one (Maximum Entropy method). The Singularity-CA method, based on fractal concepts, creates singularity maps, and the CA (Concentration Area) method is used to define local thresholds that can be applied to binarize CT images [3]. Comparing Singularity-CA method with classical methods, such as Otsu and Maximum Entropy, we observed that more pores can be detected mainly due to its ability to amplify anomalous concentrations. However, some small pores were detected incorrectly. Combining Singularity-CA (2D) method gives better pore detection performance than the Singularity-CA and the Maximum Entropy method applied individually to the images.</p><p>The Combining Singularity-CV (3D) method is presented in this work. It combines the Singularity – CV (Concentration Volume) method [4] and a global one to improve 3D pore space detection.</p><p> </p><p>References:</p><p>[1] Zhang, Y.J. (2001). A review of recent evaluation methods for image segmentation: International symposium on signal processing and its applications. Kuala Lumpur, Malaysia, 13–16, pp. 148–151.</p><p>[2] Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B., Paz-González, A., and Tarquis, A.M. (2018). Combining global and local scaling methods to detect soil pore space. J. of Geo. Exploration, vol. 189, June 2018, pp 72-84.</p><p>[3] Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B. and Tarquis, A.M. (2017). New segmentation method based on fractal properties using singularity maps. Geoderma, vol. 287, February 2017, pp 40-53. http://dx.doi.org/10.1016/j.geoderma.2016.09.005.</p><p>[4] Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B. and Tarquis, A.M. (2018). Local 3D segmentation of soil pore space based on fractal properties using singularity maps. Geoderma, vol. 311, February 2018, pp 175-188. http://dx.doi.org/10.1016/j.geoderma.2016.11.029.</p><p> </p><p>Acknowledgements:</p><p>The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330.</p>

Using a combined global and local scaling 3D segmentation method to calculate pore connectivity measures in CT soil images

2021 ◽  
Author(s):  
Juan José Martín Sotoca ◽  
Antonio Saa-Requejo ◽  
Sergio Zubelzu ◽  
Ana M. Tarquis
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Pore Space ◽  
Three Dimensional ◽  
Simulation Models ◽  
Entropy Method ◽  
Segmentation Method ◽  
Local Scaling ◽  
Structural Funds ◽  
Technological Advances

<p>The study of the spatial characteristics of soil pore networks is essential to obtain different parameters that will be useful in developing simulation models for a range of physical, chemical, and biological processes in soils. Over the last decade, major technological advances in X-ray computed tomography (CT) have allowed for the investigation and reconstruction of natural porous soils at very fine scales. Delimiting the pore network (pore space) from the CT soil images applying image binarization methods is a critical step. Different binarization methods can result in different spatial distributions of pores influencing the connectivity metrics used in the models.</p> <p>A combined global & local 2D segmentation method called “Combining Singularity-CA method” was successfully applied improving pore space detection. This method combines a local scaling method (Singularity-CA method) with a global one (Maximum Entropy method). The Singularity-CA method, based on fractal concepts, creates singularity maps, and the CA (Concentration Area) method is used to define local thresholds that can be applied to binarize CT soil images. Combining Singularity-CA (2D) method obtains better performance than the Singularity-CA and the Maximum Entropy method applied individually to the soil images.</p> <p>A new three dimensional binarization method is presented in this work. It combines the 3D Singularity-CV (Concentration Volume) method and a global one to improve 3D pore space detection. Porosity and connectivity metrics of soil pore spaces are calculated and compared to other segmentation methods.</p> <p> </p> <p>Acknowledgements:</p> <p>The authors acknowledge the support from Project No. PGC2018-093854-B-I00 of the "Ministerio de Ciencia, Innovación y Universidades" of Spain and the funding from the "Comunidad de Madrid" (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330.</p>

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.

Image Segmentation Method Based on Fisher Criterion and Genetic Algorithm

2011 ◽  
Vol 474-476 ◽  
pp. 928-932
Author(s):  
Xian Xiang Fu ◽  
Zu Jue Chen ◽  
Yong Fu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Precision Agriculture ◽  
Automatic Segmentation ◽  
Adaptive Genetic Algorithm ◽  
Salt And Pepper Noise ◽  
Segmentation Method ◽  
Fisher Criterion ◽  
One Dimensional ◽  
Salt And Pepper

Precise recognition of the weed by computer vision, furthermore raising the weeding efficiency, reducing the use of herbicide, and decreasing the pollution to the environment is one of the key technologies in the field of precision agriculture. To determine the optimal threshold in image automatic segmentation and solve one-dimensional histogram without obvious peak and valley distribution, image segmentation method based on fisher criterion and improved adaptive genetic algorithm is proposed. This method can preserve the multifamily of population and the astringency of the algorithm, and can overcome the problems of poor astringency and premature occurrence. The result shows that the proposed approach has better immunity to Salt and Pepper Noise and greatly shortens the time of image segmentation.

2D Image Segmentation Through the One Dimensional Mumford-Shah Functional

2016 ◽  
Vol 112 (1) ◽  
pp. 9-18
Author(s):  
A. Lizbeth Cortés Cortes ◽  
Carlos Guillén Galván ◽  
Rafael Lemuz López ◽  
Juan Escamilla Reyna
Keyword(s):  
Image Segmentation ◽  
One Dimensional ◽  
The One
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