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Fusion of RGB and thermal data for i...

Smith, Ryan E.

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Fusion of RGB and thermal data for improved scene understanding.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fusion of RGB and thermal data for improved scene understanding./
作者:	Smith, Ryan E.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	69 p.
附註:	Source: Masters Abstracts International, Volume: 56-04.
Contained By:	Masters Abstracts International56-04(E).
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10267781
ISBN:	9781369706215

Fusion of RGB and thermal data for improved scene understanding.
Smith, Ryan E.

Fusion of RGB and thermal data for improved scene understanding. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 69 p.

Source: Masters Abstracts International, Volume: 56-04.

Thesis (M.Eng.)--Mississippi State University, 2017.

Thermal cameras are used in numerous computer vision applications, such as human detection and scene understanding. However, the cost of high quality and high resolution thermal sensors is often a limiting factor. Conversely, high resolution visual spectrum cameras are readily available and generally inexpensive. Herein, we explore the creation of higher quality upsampled thermal imagery using a high resolution visual spectrum camera and Markov random fields theory. This paper also presents a discussion of the tradeoffs from this approach and the effects of upsampling, both from quantitative and qualitative perspectives. Our results demonstrate the successful application of this approach for human detection and the accurate propagation of thermal measurements within images for more general tasks like scene understanding. A tradeoff analysis of the costs related to performance as the resolution of the thermal camera decreases are also provided.

ISBN: 9781369706215Subjects--Topical Terms:

649834
Electrical engineering.

Fusion of RGB and thermal data for improved scene understanding.
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520 $a Thermal cameras are used in numerous computer vision applications, such as human detection and scene understanding. However, the cost of high quality and high resolution thermal sensors is often a limiting factor. Conversely, high resolution visual spectrum cameras are readily available and generally inexpensive. Herein, we explore the creation of higher quality upsampled thermal imagery using a high resolution visual spectrum camera and Markov random fields theory. This paper also presents a discussion of the tradeoffs from this approach and the effects of upsampling, both from quantitative and qualitative perspectives. Our results demonstrate the successful application of this approach for human detection and the accurate propagation of thermal measurements within images for more general tasks like scene understanding. A tradeoff analysis of the costs related to performance as the resolution of the thermal camera decreases are also provided.
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