Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.765036
Title: Research into illumination variance in video processing
Author: Javadi, Seyed Mahdi Sadreddinhajseyed
ISNI:       0000 0004 7658 8023
Awarding Body: Brunel University London
Current Institution: Brunel University
Date of Award: 2018
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Abstract:
Inthisthesiswefocusontheimpactofilluminationchangesinvideoand we discuss how we can minimize the impact of illumination variance in video processing systems. Identifyingandremovingshadowsautomaticallyisaverywellestablished and an important topic in image and video processing. Having shadowless image data would benefit many other systems such as video surveillance, tracking and object recognition algorithms. Anovelapproachtoautomaticallydetectandremoveshadowsispresented in this paper. This new method is based on the observation that, owing to the relative movement of the sun, the length and position of a shadow changes linearly over a relatively long period of time in outdoor environments,wecanconvenientlydistinguishashadowfromotherdark regions in an input video. Then we can identify the Reference Shadow as the one with the highest confidence of the mentioned linear changes. Once one shadow is detected, the rest of the shadow can also be identifiedandremoved. Wehaveprovidedmanyexperimentsandourmethod is fully capable of detecting and removing the shadows of stationary and moving objects. Additionally we have explained how reference shadows can be used to detect textures that reflect the light and shiny materials such as metal, glass and water. ...
Supervisor: Li, Y. ; Liu, X. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.765036  DOI: Not available
Keywords: Shadow detection and removal ; Cloud detection and removal ; Reflectance and glare detection ; Video and image processing
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