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Feature Extraction in Image Processi...

Li, Yiran.

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Feature Extraction in Image Processing and Deep Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Feature Extraction in Image Processing and Deep Learning./
作者:	Li, Yiran.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	142 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:	Dissertation Abstracts International79-12B(E).
標題:	Applied mathematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10791649
ISBN:	9780438183162

Feature Extraction in Image Processing and Deep Learning.
Li, Yiran.

Feature Extraction in Image Processing and Deep Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 142 p.

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2018.

This thesis develops theoretical analysis of the approximation properties of neural networks, and algorithms to extract useful features of images in fields of deep learning, quantum energy regression and cancer image analysis. The separate applications are connected by using representation systems in harmonic analysis; we focus on deriving proper representations of data using Gabor transform in this thesis. A novel neural network with proven approximation properties dependent on its size is developed using Gabor system. In quantum energy regression, invariant representation of chemical molecules using electron densities is obtained based on the Gabor transform. Additionally, we dig into pooling functions, the feature extractor in deep neural networks, and develop a novel pooling strategy originated from the maximal function with stability property and stable performance. Anisotropic representation of data using the Shearlet transform is also explored in its ability to detect regions of interests of nuclei in cancer images.

ISBN: 9780438183162Subjects--Topical Terms:

2122814
Applied mathematics.

Feature Extraction in Image Processing and Deep Learning.
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