Level Set KSVD
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Abstract (arXiv)
We present a new algorithm for image segmentation - Level-set KSVD. Level-set KSVD merges the methods of sparse dictionary learning for feature extraction and variational level-set method for image segmentation. Specifically, we use a generalization of the Chan-Vese functional with features learned by KSVD. The motivation for this model is agriculture based. Aerial images are taken in order to detect the spread of fungi in various crops. Our model is tested on such images of cotton fields. The results are compared to other methods.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 49 references that are in the map. First posted on arXiv 14 Nov 2023.
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2 of 49 references.
References · 2
- 2017A Review on Deep Learning Techniques Applied to Semantic Segmentationcited by 122
- 2022A note on the variation of geometric functionalscited by 1
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