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Three problems in imaging systems: texture re-rendering in online decoration design, a novel monochrome halftoning algorithm, and face set recognition with convolutional neural networks
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
In this thesis, studies on three problems in imaging systems will be discussed.
The first problem deals with re-rendering segments of online indoor room images with preferred textures through websites to try new decoration ideas. Previous methods need too much manual positioning and alignment. In the thesis, a novel approach is presented to automatically achieve a natural outcome with respect to indoor room geometry layout.
For the second problem, the laser electrophotographic system is eagerly looking for a digital halftoning algorithm that can deal with unequal printing resolution, since most halftoning algorithms are focused on equal resolution. In the thesis, a novel monochrome halftoning algorithm is presented to render continuous tone images with limited numbers of tone levels for laser printers with unequal printing resolution.
For the third problem, a novel face set recognition method is presented. Face set recognition is important for face video analysis and face clustering in multiple imaging systems. And it is very challenging considering the variation of image sharpness, face directions and illuminations for different frames, as well as the number and the order of images in the face set. To tackle the problem, a novel convolutional neural network system is presented to generate a fixed-dimensional compact feature representation for the face set. The system collects information from all the images in the set while having emphasis on more frontal and sharper face images, and it is regardless of the number and the order of images. The generated feature representations allow direct, immediate similarity computation for face sets, thus can be directly used for recognition. The experiment result shows that our method outperforms other state of-the-art methods on the public test dataset.