Algorithmic Art in Technical Images: Generating PseudoRandom Virtual Spaces

Abstract

The theory of technical images by Vilém Flusser has evolved into modern computational virtual synthesis. This paper explores generative spatial experiments using Perlin noise and pseudo-random algorithms, proposing a strategy to synthesize virtual random spaces in real time with 2D and 3D graphics. The outcomes are applicable in interactive programming environments and game engines, aiming to introduce a new design paradigm for architectural spaces, gaming, and media scenes, enhancing spatial diversity and variability.The study analyzes Perlin noise graphics and random number procedures, demonstrating a pseudo-random spatial landscape constructed through generative virtual textures and texture mapping techniques. Key findings include: (1) Perlin noise textures achieve enhanced complexity through multiple sampling and random number control; (2) Pseudo-random number generation creates continuous spaces with stable order while remaining unpredictable during parameter variations; and (3) Perlin noise enables smooth spatial forms through texture mapping, expanding the interface boundaries of pseudo-random spaces.