Real-Time Image Segmentation for Augmented Reality by Combiningmulti-Channel Thresholds.
Abstract: Extracting foreground objects from an image is a hot research topic. Doing thisfor high quality real world images in real-time on limited hardware such as asmart phone, is a demanding task. This master thesis shows how this problemcan be addressed using Otsu’s method together with Gaussian probability dis-tributions to create classifiers in different colour channels. We also show howclassifiers can be combined resulting in higher accuracy than using only the indi-vidual classifiers. We also propose using inter-class variance together with imagevariance to estimate classifier quality.A data set was produced to evaluate performance. The data set featuresreal-world images captured by a smart phone and objects of varying complex-ity against plain backgrounds that can be found in a typical office or urban space.
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