pystorms: A simulation sandbox for the development and evaluation of stormwater control algorithms

Rimer, Sara P.; Mullapudi, Abhiram; Troutman, Sara C.; Ewing, Gregory J.; Bowes, Benjamin D.; Akin, Aaron A.; Sadler, Jeffrey M.; Kertesz, Ruben; McDonnell, Bryant E.; Montestruque, Luis A.; Hathaway, Jon M.; Goodall, Jonathan L.; Kerkez, Branko

doi:10.48550/arxiv.2110.12289

Cited by 2 publications

(1 citation statement)

References 42 publications

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“…Many traditional image segmentation tasks are unsupervised learning by using region [2], threshold [3][4][5], boundary [6], graph theory [7], energy functional [8] and so on. Though these methods have been widely used in simple structure images segmentation, often insufficient priori properties knowledge easily lead to a dissatisfied performance in dealing with the weak boundaries on natural images [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Image segmentation with boundary-to-pixel direction and magnitude based on watershed and attention mechanism

Xing

Wang

2022

SIViP

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An improved image segmentation algorithm with boundary-to-pixel direction and magnitude (IS-BPDM) is proposed to deal with small regions segmentation while keeping the accuracy of edge segmentation. First, we develop a BPDM network embedded with watershed and attention module and use an adaptive loss function to achieve each pixel's robust and accurate BPDM which is defined as a two-dimensional vector, including direction and magnitude, and pointing from its nearest boundary pixel to itself. Then, we use the leaned BPDMs to obtain the refined initial segmented regions by considering the pixels near boundary have shorter magnitude and near root pixels have longer magnitude, meanwhile adjacent pixels in different regions or nearby pixels on both sides of root pixel in same region have opposite directions and nearby pixels in same region have similar directions. Last, we utilize a fast grouping method according to direction similarity to combine these initial segmented regions into final segmentation. The experimental results show that compared with the state-of-art methods in image segmentation, the IS-BPDM approach proposed in this paper achieves better segmentation accuracy and high computational efficiency, and outperforms in small regions segmentation on public datasets.

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Section: Introductionmentioning

confidence: 99%

Image segmentation with boundary-to-pixel direction and magnitude based on watershed and attention mechanism

Xing

Wang

2022

SIViP

View full text Add to dashboard Cite

show abstract

Image Segmentation with Boundary-to-Pixel Direction and Magnitude Based on Watershed and Attention Mechanism

Xing

Wang

2022

Preprint

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An improved image segmentation algorithm with boundary-to-pixel direction and magnitude (IS-BPDM) is proposed to deal with small regions segmentation while keeping the accuracy of edge segmentation. First, we develop a BPDM network embedded with watershed and attention module and use an adaptive loss function to achieve each pixel’s robust and accurate BPDM which is defined as a two-dimensional vector, including direction and magnitude, and pointing from its nearest boundary pixel to itself. Then, we use the leaned BPDMs to obtain the refined initial segmented regions by considering the pixels near boundary have shorter magnitude and near root pixels have longer magnitude, meanwhile adjacent pixels in different regions or nearby pixels on both sides of root pixel in same region have opposite directions and nearby pixels in same region have similar directions. Last, we utilize a fast grouping method according to direction similarity to combine these initial segmented regions into final segmentation. The experimental results show that compared with the state-of-art methods in image segmentation, the IS-BPDM approach proposed in this paper achieves better segmentation accuracy and high computational efficiency, and outperforms in small regions segmentation on public datasets.

show abstract

pystorms: A simulation sandbox for the development and evaluation of stormwater control algorithms

Cited by 2 publications

References 42 publications

Image segmentation with boundary-to-pixel direction and magnitude based on watershed and attention mechanism

Image segmentation with boundary-to-pixel direction and magnitude based on watershed and attention mechanism

Image Segmentation with Boundary-to-Pixel Direction and Magnitude Based on Watershed and Attention Mechanism

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