A Level Set Approach to Image Segmentation With Intensity Inhomogeneity

By Kaihua Zhang, Lei Zhang, Kin-Man Lam, David Dapeng Zhang. IEEE Transactions on Cybernetics, 2016

394 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/74a1c6d7207d288b6fabfb9a951903be03ad3be3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yujie Li
2. Edgar Riba
3. Li He
4. Junwu Zhu
5. Angel Domingo Sappa
6. Seiichi Serikawa
7. Xavier Soria Poma
8. Yun Li

Citations in the corpus

• Wound intensity correction and segmentation with convolutional neural networks (2017, methodology): “The method of Zhang et al. [26] is the most recent, but the accuracy of the algorithm is insufficient, when compared with the method of Li et al. [27], because the computing time is shorter with the use of semi-implicit AOS.”
• Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection (2019, background): “Edge detection is a recurrent task required for several classical computer vision processes (e.g., segmentation [39], image recognition [38, 30]), or even in the modern tasks such as image-to-image translation [41], photo sketching [18] and so on.”