A Novel Pencil Drawing Algorithm Based on Non-Symmetry and Anti-Packing Pattern Representation Model

Zheng, Yunping; Sarem, Mudar

doi:10.1109/access.2019.2960877

Cited by 5 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The artist proposes a style conversion scheme based on a priori knowledge of intersections in a regular distribution of tones. Emphasis is placed on the use of asymmetry patterns to enhance the image painting by using the edge extraction method as a model, which highlights the details of the image and can transform the style of the image through the combination of image and tone [14]. He J illustrates the language of painting as a painting technique that incorporates the arts of color, line, light, subject, and image.…”

Section: Literature Reviewmentioning

confidence: 99%

Localized Imagery and Narrative of Chinese Contemporary Oil Painting Based on Big Data

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

In this paper, a dataset is obtained by clustering data through a big data clustering algorithm, and a multidimensional state space vector with chaotic trajectories is established by reconstructing the phase space after several iterations to calculate the maximum and minimum similarity distances. The clustering between the localized imagery and narratives of contemporary oil paintings is realized based on the extracted chaotic associative dimensional features. A total of 80 students from an art class at a school were selected to conduct an imagery analysis of the cultural roots of imaginative oil painting narratives, in which the evaluation score of the research class in the imagery of shape and color could reach 10 points. Therefore, the localization of oil painting is a theoretical concept for a Chinese oil painting to go global, which meets the realistic needs and development trend of Chinese oil painting.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Localized Imagery and Narrative of Chinese Contemporary Oil Painting Based on Big Data

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

“…As a more abstract image generation technique, nonrealistic drawing techniques are becoming a focus of research in the field of computer graphics. Nonrealistic drawing is a technique that adapts to the content of an image, preserving certain key details in the image [20].…”

Section: Introductionmentioning

confidence: 99%

Application of Line Integral Convolution Techniques in the Generation of Fine Art Painting Effects

Cui

2022

Mobile Information Systems

View full text Add to dashboard Cite

Since the 1980s, nonrealistic drawing techniques have become one of the main focuses of research in computer graphics. In recent years, with the rapid development of computer graphics, nonrealistic drawing techniques have become capable of rendering any digital image into an image with an alternative artistic style. As a result, many scholars have focused on the pencil sketch style. Against this backdrop, research into how to create more realistic art drawings has become a hot topic. Although the rapid development of computer technology has led to advances in art drawing techniques, traditional fine art painting methods are not yet able to simulate realistic pencil sketches in a quite realistic way. In other words, the following issues have been noted. For example, they fail to simulate the gradation of shades and densities of real sketches, the direction of the shadows does not conform to the general rules of sketching, and the results produced are not artistic and realistic. After all, unlike realistic graphics, which seeks a photo-like realism, nonrealistic graphics wants to highlight the message of the image with the help of computer simulations. As a result, nonrealistic graphics often ignores unimportant details in the image. On the other hand, nonrealistic graphics can also simulate an artist’s painting to a certain extent, thus producing an artistically effective image. Many nonrealistic drawing methods have been devised to simulate the styles of oil, watercolour, and ink, but not many algorithms have been developed to generate colour sketches. In order to address this topic, this study improves a fine art drawing generation approach based on the line integral convolution techniques. To be specific, this investigation begins by segmenting the colour images through cluster analysis. Two basic colours are then assigned to each region by calculating the colour variability, and the density of each colour is calculated using a two-tone mapping technique. After that, the line integral convolution technique is applied to generate sketch textures for each of the two basic colour layers, and the two layers are fused to produce the coloured textures. At the same time, a neon transform is used to generate the sketch contour lines. Eventually, the contours are blended with the coloured textures to obtain the coloured sketch effect. The experimental results show that the art painting generation method designed in this paper is able to automatically transform from colour images to coloured pencil drawings, thus improving the efficiency of the process as well as meeting the real-time requirements.

show abstract

“…Huang et al (2019) [5] found that virtual brush modeling plays a vital role in virtual painting, and a powerful virtual brush model can truly reflect the features of real brushes and enhance the realism of virtual painting. Zheng et al (2019) [6] proved that the artistic stylization of image is an important content of non-photorealistic rendering research, such non-realistic stylized research has a strong realistic romantic temperament and can well express the researcher's artistic pursuit. It is a good application example of the combination of computer science and art.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Computer Real-time Graphics and Image Using the Visual Effects of Non-photorealistic Rendering of Ink Painting

2022

FAR

View full text Add to dashboard Cite

This study is to explore the effect of deep learning algorithms applied to the non-photorealistic rendering (NPR) of ink painting. The powerful processing capabilities of NPR technology and convolutional neural networks (CNN) in computer vision are analyzed. Aiming at the features of ink texture images, a new sample-based ink texture synthesis method is proposed. The feature representation of ink texture is calculated using the CNN model, and then an ink texture with the same feature representation is generated on a random image. In addition, a synthesis method of ink painting picture is proposed based on the CNN. The results show that the ink texture generated by the ink texture synthesis algorithm is very good, and the Visual Geometry Group Network (VGG-Net) model shows the best generation effect. For images generated by high-level networks lack a lot of pixel information, but retain the main feature information of the image. When the learning rate is 10, the effect of the ink image synthesized based on the content image is slightly worse; and the image generated by the fusion of multiple styles confirms the feasibility of synthesizing ink images of different "sizes and styles". Therefore, this ink image synthesized on the basis of the CNN model shows a better effect, which extends the possibility of computer creation of richer ink painting works.

show abstract

A Novel Pencil Drawing Algorithm Based on Non-Symmetry and Anti-Packing Pattern Representation Model

Cited by 5 publications

References 26 publications

Localized Imagery and Narrative of Chinese Contemporary Oil Painting Based on Big Data

Localized Imagery and Narrative of Chinese Contemporary Oil Painting Based on Big Data

Application of Line Integral Convolution Techniques in the Generation of Fine Art Painting Effects

Deep Learning in Computer Real-time Graphics and Image Using the Visual Effects of Non-photorealistic Rendering of Ink Painting

Contact Info

Product

Resources

About