Image-based pencil drawing synthesized using convolutional neural network feature maps

Cai, Xiuxia; Song, Bin

doi:10.1007/s00138-018-0906-2

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…In (20), x is the original sample, p x is the distribution of x, z is the noise data, and p z is the prior probability distribution of input noise variables. V(D, G) is minimized by the generator, and the discriminator maximizes it.…”

Section: Ganmentioning

confidence: 99%

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Contemporary Advertising Text Art Design and Effect Evaluation by IoT Deep Learning under the Smart City

Zhang

Tang

et al. 2022

Security and Communication Networks

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This work intends to solve the problem that the current artistic typeface generation methods rely too much on manual intervention, lack novelty, and the single font local feature and the global feature extraction method cannot fully describe the font features. Firstly, it proposes a handwritten word recognition model based on generalized search trees (GIST) and the pyramid histogram of oriented gradient (PHOG). The local features and global features of the font are fused. Secondly, a model of automatic artistic typeface generation based on generative adversarial networks (GAN) is constructed, which can use hand-drawn fonts to automatically generate artistic typefaces in the desired style through training as needed. Finally, the generation of the huaniao typeface is used as an example. By constructing the dataset, the effectiveness of the two models is verified. The experimental results show the following: (1) The proposed handwritten character recognition model based on GIST and PHOG has a higher recognition rate of different fonts than the single GIST and PHOG features by more than 5.8%. The total recognition time is reduced by more than 49.4%, and the performance is improved significantly. (2) Compared with other popular algorithms, the constructed GAN-based automatic artistic typeface generation model has the best quality of the generation of huaniao on both the pencil sketch and the calligraphy character image dataset. Models have broad application prospects in contemporary advertising text art design. This study aims to provide important technical support for the automation of contemporary advertising text art design and the improvement of overall efficiency.

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Section: Ganmentioning

confidence: 99%

“…e CNN algorithm can not only generate pencil sketches from natural images but also preserve the tones of natural images, and the painting style is flexible. Pen sketch images are correspondingly distorted and deformed to make their forms richer and more diverse [20].…”

Section: E Validity Verification Of the Automatic Generation Model Of...mentioning

confidence: 99%

Contemporary Advertising Text Art Design and Effect Evaluation by IoT Deep Learning under the Smart City

Zhang

Tang

et al. 2022

Security and Communication Networks

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“…The final pencil texture map, which is denoted as T, is computed via an exponentiation operation, as expressed in Eq. (10), in which H is an actual pencil texture image. Finally, the pencil outline and the pencil texture are combined to obtain the pencil drawing result and the calculation method is expressed as Eq.…”

Section: ) Contour Extractionmentioning

confidence: 99%

Parallel Fast Pencil Drawing Generation Algorithm Based on GPU

Qiu

Liu

et al. 2019

IEEE Access

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With the development of image processing technology, pencil drawing has been widely used in video games and mobile phone applications. However, the existing pencil drawing algorithms require a large amount of time to convert a real picture into a pencil drawing; hence, it is difficult to apply them to real-time systems. This paper proposes a parallel fast pencil drawing generation algorithm based on the graphics processing unit (GPU) to accelerate the real-time rendering process of sketch painting. The parallelism of the pencil drawing generation algorithm is identified via a theoretical analysis at first. Then, sub-algorithms of the sequential algorithm are designed in parallel using the compute unified device architecture (CUDA) programming model and executed via thread-level parallel techniques. Furthermore, an optimal cache pattern of data that reduce the access time of the most frequently used data is structured using shared memory and constant memory. Finally, task-level parallelism is achieved by the CUDA stream technology, which overlaps independent sub-tasks for further acceleration. On the CUDA platform, the experimental results demonstrate that the proposed parallel algorithm can achieve a significant increase in speedup. The proposed algorithm achieves a performance improvement of 448.59 times compared with the sequential algorithm, on 2560×1920-resolution images, and maintains a high degree of similarity with the real pencil paintings. Hence, the proposed algorithm is suitable for real-time pencil drawing rendering and has promising application prospects in non-photorealistic rendering. INDEX TERMS Non-photorealistic rendering, pencil drawing, parallel algorithm, GPU platform, convolution operation, CUDA.

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Robust pencil drawing generation via fast Retinex decomposition

Hao

Guo

2021

Computers & Graphics

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Image-based pencil drawing synthesized using convolutional neural network feature maps

Cited by 4 publications

References 27 publications

Contemporary Advertising Text Art Design and Effect Evaluation by IoT Deep Learning under the Smart City

Contemporary Advertising Text Art Design and Effect Evaluation by IoT Deep Learning under the Smart City

Parallel Fast Pencil Drawing Generation Algorithm Based on GPU

Robust pencil drawing generation via fast Retinex decomposition

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