Oil Painting Style Rendering Based on Kuwahara Filter

Gao, Junjie; Li, Da‐Jin; Gao, Wenran

doi:10.1109/access.2019.2931037

Cited by 9 publications

(4 citation statements)

References 41 publications

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“…We used two saliency estimation methods, graph-based manifold ranking saliency (GBMR) and frequency-tuned (FT) methods, to extract saliency maps for each image. The GBMR method [ 57 ] is more accurate for single images, while the FT method [ 56 ] is better for maintaining interframe coherence in video sequences [ 58 ]. The saliency map is a grayscale image that shows the strength and size of the saliency according to the mean and standard deviation of the pixel values, respectively [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting the aesthetics of dynamic generative artwork based on statistical image features: A time-dependent model

Meng,

et al. 2023

PLoS ONE

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Several automated aesthetic assessment models were developed to assist artists in producing artwork with high aesthetic appeal. However, most of them focused on static visual art, such as photographs and paintings, and evaluations of dynamic art received less attention. Dynamic visual art, especially computer-based art, includes diverse forms of artistic expression and can enhance an audience’s aesthetic experience. A model for evaluating dynamic visual art can provide valuable feedback and guidance for improving artistic skills and creativity, thereby benefiting audiences. In this study, we created eight generative artworks with dynamic art forms based on a commonly used method. We established a time-dependent model to predict the aesthetics based on visual features. We quantified the artworks according to selected image features and found that these features could effectively capture the characteristics of the changing visual forms during the generation process. To explore the effects of time-varying features on aesthetic appeal, we built a panel regression model and found that the aesthetic appeal of the generated artworks was significantly affected by their skewness of the luminance distribution, vertical symmetry, and mean hue value. Furthermore, our study demonstrated that the aesthetic appeal of dynamic generative artworks could be predicted by integrating image features into the temporal domain.

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Section: Methodsmentioning

confidence: 99%

Predicting the aesthetics of dynamic generative artwork based on statistical image features: A time-dependent model

Meng,

et al. 2023

PLoS ONE

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“…Brush strokes refer to the markers produced by the painter in the process of painting, which can be used to describe the characterized objects or to express the subjective emotion. As stated in many literatures [13,22,23], image gradient plays an important role in selecting brush strokes. To consistently imitate the most representative brush stroke from images of the oil paintings, the standard deviation SD(i) in a sliding windows W(i) on the source image is calculated as a gradient map ∇S.…”

Section: 2 Adaptive Selection Of Brush Strokementioning

confidence: 99%

Printed Texture Guided Color Feature Fusion for Impressionism Style Rendering of Oil Paintings

Geng

et al. 2022

Mathematics

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As a major branch of Non-Photorealistic Rendering (NPR), image stylization mainly uses computer algorithms to render a photo into an artistic painting. Recent work has shown that the ex-traction of style information such as stroke texture and color of the target style image is the key to image stylization. Given its stroke texture and color characteristics, a new stroke rendering method is proposed. By fully considering the tonal characteristics and the representative color of the original oil painting, it can fit the tone of the original oil painting image into a stylized image whilst keeping the artist's creative effect. The experiments have validated the efficacy of the proposed model in comparison to three state-of-the-arts. This method would be more suitable for the works of pointillism painters with a relatively uniform style, especially for natural scenes, otherwise, the results can be less satisfactory.

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“…A real-time video artistic stylization system was presented and demonstrated a variety of painterly styles [8,9]. Since then, studies and algorithms for creating various painting styles have been continuously introduced [10,11]. Recently, research has been conducted on robot painting system.…”

Section: Related Workmentioning

confidence: 99%

Emotion-Inspired Painterly Rendering

2020

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When painters draw, they do not draw what they see; they reflect their emotions through specific forms of artistic expression. Through this empathy, humans derive aesthetic satisfaction. Psychological studies on art and emotions have been extensively conducted since the 20th century, and it has been found that artistic activities such as painting and writing have a positive effect on human emotions. However, it is difficult for a non-professional painter to express human emotions in a picture using artistic elements. The aim of this study was to propose a "user emotion-inspired painterly rendering process" to help non-professionals reflect their emotions through painting. To accomplish this aim, a painter-rendering system that automatically creates digital paintings with parameterized painting features was used. We analyzed the correlation between human emotions and the rendering parameters affecting the painting style through a user study, and we estimated the rendering parameter values for specific emotions. The results showed that colors and emotions were closely linked, and color scheme was an influential factor in the paintings. Furthermore, we developed a recoloring algorithm based on Kobayashi's three color systems classified as human emotions, and we created paintings that better reflect specific emotions. The proposed algorithm was validated through a user satisfaction survey. Hence, the proposed system can improve a user's understanding of painting and create paintings that reflect specific emotions.

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Oil Painting Style Rendering Based on Kuwahara Filter

Cited by 9 publications

References 41 publications

Predicting the aesthetics of dynamic generative artwork based on statistical image features: A time-dependent model

Predicting the aesthetics of dynamic generative artwork based on statistical image features: A time-dependent model

Printed Texture Guided Color Feature Fusion for Impressionism Style Rendering of Oil Paintings

Emotion-Inspired Painterly Rendering

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