Automatic Removal of Complex Shadows From Indoor Videos

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Description

Shadows in indoor scenarios are usually characterized with multiple light sources that produce complex shadow patterns of a single object. Without removing shadow, the foreground object tends to be erroneously segmented. The inconsistent hue and intensity of shadows make automatic removal a challenging task. In this thesis, a dynamic thresholding and transfer learning-based method for removing shadows is proposed. The method suppresses light shadows with a dynamically computed threshold and removes dark shadows using an online learning strategy that is built upon a base classifier trained with manually annotated examples and refined with the automatically identified examples in the new ... continued below

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viii, 53 pages : illustrations (some color)

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Mohapatra, Deepankar August 2015.

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This thesis is part of the collection entitled: UNT Theses and Dissertations and was provided by UNT Libraries to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 52 times . More information about this thesis can be viewed below.

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  • Mohapatra, Deepankar

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Shadows in indoor scenarios are usually characterized with multiple light sources that produce complex shadow patterns of a single object. Without removing shadow, the foreground object tends to be erroneously segmented. The inconsistent hue and intensity of shadows make automatic removal a challenging task. In this thesis, a dynamic thresholding and transfer learning-based method for removing shadows is proposed. The method suppresses light shadows with a dynamically computed threshold and removes dark shadows using an online learning strategy that is built upon a base classifier trained with manually annotated examples and refined with the automatically identified examples in the new videos. Experimental results demonstrate that despite variation of lighting conditions in videos our proposed method is able to adapt to the videos and remove shadows effectively. The sensitivity of shadow detection changes slightly with different confidence levels used in example selection for classifier retraining and high confidence level usually yields better performance with less retraining iterations.

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viii, 53 pages : illustrations (some color)

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UNT Theses and Dissertations

Theses and dissertations represent a wealth of scholarly and artistic content created by masters and doctoral students in the degree-seeking process. Some ETDs in this collection are restricted to use by the UNT community.

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  • August 2015

Added to The UNT Digital Library

  • March 4, 2016, 4:14 p.m.

Description Last Updated

  • April 17, 2017, 9:36 a.m.

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Citations, Rights, Re-Use

Mohapatra, Deepankar. Automatic Removal of Complex Shadows From Indoor Videos, thesis, August 2015; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc804942/: accessed December 18, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .