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Simulation of Laser Profilometer Measurements in the Presence of Speckle Using Perlin Noise [PDF]
Sensors, 2023 In the manufacturing industry, inspection systems play a crucial role in ensuring product quality. High-resolution profilometric sensors have become increasingly popular for inspection due to their ability to provide detailed surface information. However,
Sara Roos-Hoefgeest +3 more
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Instant-SFH: Non-Iterative Sparse Fourier Holograms Using Perlin Noise [PDF]
SensorsHolographic displays are an upcoming technology for AR and VR applications, with the ability to show 3D content with accurate depth cues, including accommodation and motion parallax.
David Li +4 more
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Generative AI Shanshui animation enhancement using Perlin noise and diffusion models
Discover Artificial IntelligenceDeep learning models have achieved remarkable advancements in image generation but face persistent challenges in synthesizing traditional Shanshui (mountain-water) landscape paintings due to limited domain-specific training data and the complexity of ...
Kanoksak Wattanachote +3 more
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Perlin noise as a hierarchical neutral landscape model [PDF]
Web Ecology, 2022 Neutral landscape models based on fractal algorithms are used widely by landscape ecologists. Perlin noise is a spectral synthesis algorithm that has not been widely adopted by landscape ecologists, even though the hierarchical properties of Perlin noise
T. R. Etherington
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Traffic generator using Perlin Noise [PDF]
2012 IEEE Global Communications Conference (GLOBECOM), 2012 Study of high speed networks such as optical next generation burst or packet switched networks require large amounts of synthetic traffic to feed simulators. Methods to generate self-similar long range dependent traffic already exist but they usually work by generating large blocks of traffic of fixed time duration.
Daniel Morato +2 more
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Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data
2020 25th International Conference on Pattern Recognition (ICPR), 2021 We propose a novel network initialization method using Perlin noise for training image classification networks with a limited amount of data. Our main idea is to initialize the network parameters by solving an artificial noise classification problem, where the aim is to classify Perlin noise samples into their noise categories.
Hirokatsu Kataoka
exaly +4 more sources
Perlin Noise Improve Adversarial Robustness
CoRR, 2021 Adversarial examples are some special input that can perturb the output of a deep neural network, in order to make produce intentional errors in the learning algorithms in the production environment. Most of the present methods for generating adversarial examples require gradient information.
Chengjun Tang +4 more
openaire +2 more sources
upU-Net Approaches for Background Emission Removal in Fluorescence Microscopy
Journal of Imaging, 2022 The physical process underlying microscopy imaging suffers from several issues: some of them include the blurring effect due to the Point Spread Function, the presence of Gaussian or Poisson noise, or even a mixture of these two types of perturbation ...
Alessandro Benfenati
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Procedural Generation in Games: Focusing on Dungeons [PDF]
SHS Web of Conferences, 2022 This report is talking about the applications of procedural generation in games. There are many AI-generated games on the market, such as No Man’s Sky.
Shen Zhenyuan
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Generative Art with Swarm Landscapes
Entropy, 2020 We present a generative swarm art project that creates 3D animations by running a Particle Swarm Optimization algorithm over synthetic landscapes produced by an objective function.
Diogo de Andrade +3 more
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