<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Image segmentation has always been a research hotspot in computer vision. Traditional image segmentation is based on pixels, and it is difficult to achieve the desired target effect. The manual segmentation method is too cumbersome and has limited precision. There is a certain degree of instability in the selection of interactively segmented pixels, which will affect the final segmentation results to a large extent.</p><p>In this paper, the superpixel block generated by the SLIC algorithm is used to preprocess the image, and then based on the Graph cut algorithm, the selection of seed points is improved, and the pixels of the background area are attempted to be optimized to obtain a more excellent target point sample. On top of the image segmentation, the foreground object is further divided with a frame, the pixels in the background are optimized, and the advantages of other segmentation algorithms are extracted to achieve the goal of global optimization, and interactive image segmentation is achieved.</p></abstract>

Image segmentation has always been a research hotspot in computer vision. Traditional image segmentation is based on pixels, and it is difficult to achieve the desired target effect. The manual segmentation method is too cumbersome and has limited precision. There is a certain degree of instability in the selection of interactively segmented pixels, which will affect the final segmentation results to a large extent.In this paper, the superpixel block generated by the SLIC algorithm is used to preprocess the image, and then based on the Graph cut algorithm, the selection of seed points is improved, and the pixels of the background area are attempted to be optimized to obtain a more excellent target point sample. On top of the image segmentation, the foreground object is further divided with a frame, the pixels in the background are optimized, and the advantages of other segmentation algorithms are extracted to achieve the goal of global optimization, and interactive image segmentation is achieved.

Analysis and Design of Image Segmentation Algorithm Based on Super-pixel and Graph Cut

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Image segmentation has always been a research hotspot in computer vision. Traditional image segmentation is based on pixels, and it is difficult to achieve the...

Analysis and Design of Image Segmentation Algorithm Based on Super-pixel and Graph Cut

Data publikacji: 14 paź 2019

Zakres stron: 25 - 30

DOI: https://doi.org/10.21307/ijanmc-2019-017

Słowa kluczowe
Interactive Segmentation, Super-Pixel, Graph Cut, Maximum Flow/ Minimum Cut, K-Means, SLIC

© 2018 Feng Xiao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Analysis and Design of Image Segmentation Algorithm Based on Super-pixel and Graph Cut

Data publikacji: 14 paź 2019

Zakres stron: 25 - 30

DOI: https://doi.org/10.21307/ijanmc-2019-017

Słowa kluczoweInteractive Segmentation, Super-Pixel, Graph Cut, Maximum Flow/ Minimum Cut, K-Means, SLIC

© 2018 Feng Xiao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Słowa kluczowe
Interactive Segmentation, Super-Pixel, Graph Cut, Maximum Flow/ Minimum Cut, K-Means, SLIC