Browsing by Author "Schreiber, Shaun"

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    Texture synthesis with neural networks
    (Stellenbosch : Stellenbosch University, 2018-12) Schreiber, Shaun; Geldenhuys, J.; De Villiers, H. A. C.; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Computer Science.
    ENGLISH ABSTRACT : Creating detailed texture maps for virtual environments is often a timeconsuming process. Procedural texture generation enables the creation of more rich and detailed virtual environments with minimal input needed from an artist. However, finding a flexible generative model of real world textures remains an open problem. There are currently two key limiting factors. The first key limitation is a lack of available knowledge on the capability of the various neural network based techniques and how the components associated with each technique affects the quality of synthesized textures. The second key limitation in modern generative models is the inability to apply localized constraints in situations where there are complex interactions between two regions within a texture. To address these limitations, three areas of interest (training set, network architecture, and texture representation) involving the synthesis process are identified specifically for neural network-based techniques and their effects on the synthesized textures are investigated. Included in this investigation is a comparative study focusing on subjective quality and quantitative error measurement between the currently available techniques. Second, a novel convolutional neural network-based texture model is proposed, consisting of four summary statistics (content or feature maps, Gramian matrices, transformed Gramian matrices, and total variation), as well as spectrum constraints. The Fourier transform and windowed Fourier transform are investigated in applying spectrum constraints, and it is found that the windowed Fourier transform improved the quality and consistency of the generated textures. During the component investigation, it was identified that the VGG-19 network still produces comparable results when compared to more modern network architectures. Additionally, it was also demonstrated that direct methods are capable of producing results equal to the iterative approach if stochastic textures are synthesized, but produces unsatisfactory results with irregular and regular textures. Finally, the efficacy of the proposed technique is demonstrated by comparing the generated output with that of related techniques.