Sparse matrix-based image enhancement for target detection using deep learning

Shih-Yu Chen; Zhe-Yuan Kao; Fu-Ming Yang; Yen-Chung Chen

doi:10.1117/12.2306744

24 May 2018 Sparse matrix-based image enhancement for target detection using deep learning

Shih-Yu Chen , Zhe-Yuan Kao, Fu-Ming Yang , Yen-Chung Chen

Proceedings Volume 10679, Optics, Photonics, and Digital Technologies for Imaging Applications V; 106791Q (2018) https://doi.org/10.1117/12.2306744
Event: SPIE Photonics Europe, 2018, Strasbourg, France

Abstract

Leaf maturation from initiation to senescence is a phenological event of plants that is a result of the influences of temperature and water availability on physiological activities during a life cycle. Detection of newly grown leaves (NGL) is therefore useful in diagnosis if growth of trees, tree stress and even climatic change. There are many important applications that can naturally be modeled as a low-rank plus a sparse contribution. This paper develop a new algorithm and application to detect NGL. It uses first sparse matrix as a preprocessing to enhance target and applied deep learning to segment the image. The experimental results show that our proposed method can detect targets effectively and decrease false alarm rate.

Citation Download Citation

Shih-Yu Chen , Zhe-Yuan Kao, Fu-Ming Yang , and Yen-Chung Chen "Sparse matrix-based image enhancement for target detection using deep learning", Proc. SPIE 10679, Optics, Photonics, and Digital Technologies for Imaging Applications V, 106791Q (24 May 2018); https://doi.org/10.1117/12.2306744

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