Moreover, we show an additional machine learning technique by repeating the forwarding computation for the generated tile art image as an input image. After learning the network with the tile art images generated by the greedy approach as training dataset, it can generate tile art images that well-reproduce the original images with tile patterns. The second contribution of this paper is to propose an approximation method using machine learning with deep neural networks.
The experimental result shows that the GPU implementation attains a speed-up factor of 318 and 16.19 over the sequential CPU implementation and the parallel multi-core CPU implementation with 160 threads, respectively. Related Images: pattern background seamless mosaic abstract geometric design texture repeat tile. We have implemented it on NVIDIA Tesla V100 GPU. In addition, to shorten the computation time, we show the parallel algorithm and its GPU acceleration technique. The greedy approach is based on the characteristic of the human visual system to optimize generated images. Tiles come in a variety of designs including the geometric patterns, floral designs, vintage styles, retro designs, tribal patterns, and much more. The first contribution of this paper is to propose a tile image generation based on the greedy approach. The generated digital image resembles artistic representation given digital photos and illustrations. Glazed, etched, and texturized, porcelain tiles can resemble hardwood planks. Tile art image generation is one of the non-photorealistic rendering methods. A wide variety of colors and patterns make porcelain tile a chameleon of sorts.
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