Influence of procedural noise on the glossiness of 2.5D printed patches

Perlin noise, a type of procedural noise, was used for the design of elevation files for 2.5D printing. This printing method uses elevation data from a height map to create physical relief by superimposing layers of ink. In this experiment, the grayscale values of noise functions were used as elevation values to build different surface structures in UV curable ink by 2.5D printing. Printed samples with varying levels of Perlin noise were created and their reflectance properties were studied by measuring the values of specular gloss. The roughness and specular gloss of the printed surfaces were effectively influenced when varying the persistence and octaves of the noise functions. The aim of implementing the procedural approach to a high-resolution printing method has been to explore the reflectance properties of custom noise functions when transferred to the physical realm. This might contribute to better understand the effect of surface structure on the appearance of materials. Potentially, this approach will enable the use of relief printing to produce structures with a more natural appearance and a desired gloss value by using a low-cost computing process.

A note about reproducibility in visual ITC

We present several orders of magnitude of some anomaly occurring rates f that we obtained in visual instrumental transcommunication. We used two techniques, the first one is numeric and relies on Perlin noise, it was run by the second author who presents solid mediumship and obtained f ~ 10^(-3). The second one is a reproduction of a mist technique developed by the Institut français de recherche et d'expérimentation spirite (Ifres), it was run by the first author who has no mediumistic ability and obtained f ~ 10^(-5).

Remote Sensing Image Augmentation Based on Text Description for Waterside Change Detection

Since remote sensing images are difficult to obtain and need to go through a complicated administrative procedure for use in China, it cannot meet the requirement of huge training samples for Waterside Change Detection based on deep learning. Recently, data augmentation has become an effective method to address the issue of an absence of training samples. Therefore, an improved Generative Adversarial Network (GAN), i.e., BTD-sGAN (Text-based Deeply-supervised GAN), is proposed to generate training samples for remote sensing images of Anhui Province, China. The principal structure of our model is based on Deeply-supervised GAN(D-sGAN), and D-sGAN is improved from the point of the diversity of the generated samples. First, the network takes Perlin Noise, image segmentation graph, and encoded text vector as input, in which the size of image segmentation graph is adjusted to 128 × 128 to facilitate fusion with the text vector. Then, to improve the diversity of the generated images, the text vector is used to modify the semantic loss of the downsampled text. Finally, to balance the time and quality of image generation, only a two-layer Unet++ structure is used to generate the image. Herein, “Inception Score”, “Human Rank”, and “Inference Time” are used to evaluate the performance of BTD-sGAN, StackGAN++, and GAN-INT-CLS. At the same time, to verify the diversity of the remote sensing images generated by BTD-sGAN, this paper compares the results when the generated images are sent to the remote sensing interpretation network and when the generated images are not added; the results show that the generated image can improve the precision of soil-moving detection by 5%, which proves the effectiveness of the proposed model.

The spherical primitive and perlin noise method to recreate realistic aggregate shapes

An algorithm to re-create virtual aggregates with realistic shapes is presented in this paper. The algorithm has been implemented in the Unity 3D platform. The idea is to re-create realistically the virtual coarse and crushed aggregates that are normally used as a material for the construction of roads. This method consists of two major procedures: (i) to combine a spherical density function with a noise matrix based on the Perlin noise to obtain shapes of appropriate angularity and, (ii) deform the shapes until their minor ferret, aspect ratio and, thickness are equivalent to those wanted. The efficiency of the algorithm has been tested by reproducing nine types of aggregates from different sources. The results obtained indicate that the method proposed can be used to realistically re-create in 3D coarse aggregates. Graphic abstract

Prime gradient noise

Procedural noise functions are fundamental tools in computer graphics used for synthesizing virtual geometry and texture patterns. Ideally, a procedural noise function should be compact, aperiodic, parameterized, and randomly accessible. Traditional lattice noise functions such as Perlin noise, however, exhibit periodicity due to the axial correlation induced while hashing the lattice vertices to the gradients. In this paper, we introduce a parameterized lattice noise called prime gradient noise (PGN) that minimizes discernible periodicity in the noise while enhancing the algorithmic efficiency. PGN utilizes prime gradients, a set of random unit vectors constructed from subsets of prime numbers plotted in polar coordinate system. To map axial indices of lattice vertices to prime gradients, PGN employs Szudzik pairing, a bijection F: ℕ2 → ℕ. Compositions of Szudzik pairing functions are used in higher dimensions. At the core of PGN is the ability to parameterize noise generation though prime sequence offsetting which facilitates the creation of fractal noise with varying levels of heterogeneity ranging from homogeneous to hybrid multifractals. A comparative spectral analysis of the proposed noise with other noises including lattice noises show that PGN significantly reduces axial correlation and hence, periodicity in the noise texture. We demonstrate the utility of the proposed noise function with several examples in procedural modeling, parameterized pattern synthesis, and solid texturing.