Christopher Palazzolo

By-Example Synthesis of Vector Textures, Pacific Graphics 2025, 2025
Christopher Palazzolo, Oliver van Kaick, David Mould
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We propose a new method for synthesizing an arbitrarily sized novel vector texture given a single raster exemplar. In an analysis phase, our method first segments the exemplar to extract primary textons, secondary textons, and a palette of background colors. Then, it clusters the primary textons into categories based on visual similarity, and computes a descriptor to capture each texton's neighborhood and inter-category relationships. In the synthesis phase, our method first constructs a gradient field with a set of control points containing colors from the background palette. Next, it places primary textons based on the descriptors, in order to replicate a similar texton context as in the exemplar. The method also places secondary textons to complement the background detail. We compare our method to previous work with a wide range of perceptual-based metrics, and show that we are able to synthesize textures directly in vector format with quality similar to methods based on raster image synthesis.

Breaking art: Synthesizing abstract expressionism through image rearrangement, Computers & Graphics, 2025
Christopher Palazzolo, Oliver van Kaick, David Mould
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We present an algorithm that creates interesting abstract expressionist images from segments of an input image. The algorithm operates by first segmenting the input image at multiple scales, then redistributing the resulting segments across the image plane to obtain an aesthetic abstract output. Larger segments are placed using neighborhood-aware descriptors, and smaller segments are arranged in a Poisson disk distribution. In our thorough analysis, we show that our results score highly according to several relevant aesthetic metrics, and that our style is indeed abstract expressionism. The results are visually appealing, provided the exemplar has a somewhat diverse color pallette and some amount of structure.

This paper investigates the sustainability of an ecosystem that involves the consumption and reproduction of wildlife on a day-by-day basis in addition to the growth of plants. Different from the traditional approaches such as the reinforcement learning algorithms or the predator-prey dynamical system analysis, we applied simulation techniques and developed computer programs that manage the evolution of the system. The results provide visualization for the system. Limitations and further improvements of the study are also discussed.