Image Evolution Using 2D Power Spectra
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Procedurally generated textures have seen use in many applications, are a high-interest topic when exploring evolutionary algorithms, and hold a central interest for digital art. However, there is an existing difficulty in finding suitable heuristics for measuring perceived qualities of an image. Particular difficulty remains for quantifying aspects of style and shape. In an attempt to bridge the divide between computer vision and cognitive perception, one set of proposed measures from previous studies relate to image spatial frequencies. Based on existing research which uses power spectral density of spatial frequencies as an effective metric for image classification and retrieval, we believe this measure and others based on Fourier decomposition may be effective for guiding evolutionary texture synthesis. We briefly compare some alternative means of using frequency analysis to guide evolution of shape and composition, and refine fitness measures based on Fourier analysis and spatial frequency. Our exploration has been conducted with the goals of improving intuition of these measures, evaluating the utility of these measures for image composition, and observing possible adaptations of their use in digital evolutionary art. Multiple evolutionary guidance schemes with consideration of the spatial frequencies' power spectra and phase have been evaluated across numerous targets with mixed results. We will display our exploration of power spectral density measures and their effectiveness as used for evolutionary algorithm fitness targets, particularly for basic compositional guidance in evolutionary art. We also observe and analyze a previously identified phenomenon of spatial properties which could lead to further consideration of visual comfort and aesthetics.