Date of Graduation
2017
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Chemical and Biomedical Engineering
Committee Chair
Xin Li
Committee Co-Chair
Mark Tseytlin
Committee Member
Matthew C Valenti
Abstract
Specular reflection exists when one tries to record a photo or video through a transparent glass medium or opaque surfaces such as plastics, ceramics, polyester and human skin, which can be well described as the superposition of a transmitted layer and a reflection layer. These specular reflections often confound the algorithms developed for image analysis, computer vision and pattern recognition. To obtain a pure diffuse reflection component, specularity (highlights) needs to be removed. To handle this problem, a novel and robust algorithm is formulated. The contributions of this work are three-fold.;First, the smoothness of the video along with the temporal coherence and illumination changes are preserved by reducing the flickering and jagged edges caused by hand-held video acquisition and homography transformation respectively.;Second, this algorithm is designed to improve upon the state-of-art algorithms by automatically selecting the region of interest (ROI) for all the frames, reducing the computational time and complexity by utilizing the luminance (Y) channel and exploiting the Augmented Lagrange Multiplier (ALM) with Alternating Direction Minimizing (ADM) to facilitate the derivation of solution algorithms.;Third, a quantity metrics is devised, which objectively quantifies the amount of specularity in each frame of a hand-held video. The proposed specularity removal algorithm is compared against existing state-of-art algorithms using the newly-developed quantity metrics. Experimental results validate that the developed algorithm has superior performance in terms of computation time, quality and accuracy.
Recommended Citation
Guggilapu, PriyaankaDevi, "Robust Specularity Removal from Hand-held Videos" (2017). Graduate Theses, Dissertations, and Problem Reports. 5722.
https://researchrepository.wvu.edu/etd/5722