An Automatic Graphic Pattern Generation Algorithm and Its Application to the Multipurpose Camouflage Pattern Design

Abdi, Arash; Safabakhsh, Reza

doi:10.1109/tcyb.2022.3140394

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…After feature extraction, math functions and computer languages are used for autogen algorithms. AGPG models generate multi-purpose camouflage patterns [11], IFS and curve functions simulate floral patterns [3], and color transfer methods create colored mandalas [12]. Adversarial networks and aesthetics guide auto-gen pattern design [13].…”

Section: Automatic Pattern Generation Technologymentioning

confidence: 99%

Research on the Design of Zhuang Brocade Patterns Based on Automatic Pattern Generation

Ni,

Huang,

et al. 2024

Applied Sciences

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To promote the inheritance of Zhuang brocade culture and the rapid extraction of features and automatic generation of patterns, this paper constructs a feature dataset of Zhuang brocade patterns and proposes an automatic generation technology using relative coordinates and regional content replacement. Firstly, by sorting through a large number of cases, a feature dataset of Zhuang brocade patterns is constructed. For the significant features of Zhuang brocade patterns, intelligent extraction algorithms and processes are used to effectively extract the color matching, patterns, and organizational forms of the patterns into the feature dataset. Secondly, to generate Zhuang brocade patterns quickly, an automatic generation technology based on genotype encoding and regional replacement algorithms is proposed, which encodes these pattern elements into a format that can be interpreted by computer algorithms. Finally, through similarity evaluation, the method’s feasibility for rapid extraction and generation of Zhuang brocade patterns is effectively verified. This method is significant for the inheritance of Zhuang brocade patterns and the development of the intangible cultural heritage industry.

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Section: Automatic Pattern Generation Technologymentioning

confidence: 99%

Research on the Design of Zhuang Brocade Patterns Based on Automatic Pattern Generation

Ni,

Huang,

et al. 2024

Applied Sciences

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“…After experimentation, it was determined that the problem lay in the loss function of equation (8), where the Gram matrix used to express the style is not suitable for EFDM and causes contradictions with the training loss function. Therefore, the loss function equation (10) was modified as follows. In addition, to retain the content better, a new training strategy was proposed wherein different weights and feature expressions are used when training the SEncoder and its SDecoder counterpart.…”

Section: Loss Functionmentioning

confidence: 99%

“…Due to the enormous market demand, there are many design studios and software solutions capable of creating images. [9][10][11] However, these solutions require professional designers to spend a lot of time learning and using complex software, which makes them impractical and even inaccessible to ordinary people. Therefore, although digital printing technology is suitable for personalized production, it still suffers from the problem of high cost incurred by design difficulties, which prevents its large-scale adoption.…”

mentioning

confidence: 99%

Digital printing image generation method based on style transfer

Su,

Zhao,

Zhang

et al. 2023

Textile Research Journal

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Digital printing has been widely used in textile printing production. In the process of designing digital printing patterns, an image generative model is needed to assist in obtaining more diversified patterns. However, the current model involves large storage space and high computing cost, which affects the promotion of digital printing customized production. To solve the problem, this article proposes a digital printing image generation method based on style transfer. Firstly, a style transfer method based on exact feature distribution matching is constructed to realize the accurate matching from image content to style features. And a balanced loss function is used to enhance the universality of the proposed method. Furthermore, knowledge distillation is introduced to compress the method proposed to reduce the hardware requirements when processing high-resolution digital printing images. Finally, a segmented training strategy is proposed to solve the performance degradation caused by model compression. The experimental results show that when processing images with a resolution of 3000 × 3000, the storage capacity of the model is only 2.68 MB and only 0.20 TFLOPs is required. The maximum processing resolution is more than 8K. The pattern obtained by this model is of high quality and can meet the needs of digital printing production.

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Maneuvering target oriented digital camouflage image generation method

Pei

Huang

et al. 2023

Displays

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An Automatic Graphic Pattern Generation Algorithm and Its Application to the Multipurpose Camouflage Pattern Design

Cited by 6 publications

References 39 publications

Research on the Design of Zhuang Brocade Patterns Based on Automatic Pattern Generation

Research on the Design of Zhuang Brocade Patterns Based on Automatic Pattern Generation

Digital printing image generation method based on style transfer

Maneuvering target oriented digital camouflage image generation method

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