Image color style migration method for mobile applications based color feature extraction

Cai, Xingquan; Ge, Yakun; Cai, Runbo; Guo, Ting

doi:10.3233/jcm-190020

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…Moreover, the designed image style transfer system consumes a shorter time in transferring various types of images, thereby achieving real-time transfer. Cai et al (2019) proposed a transfer method of image color style based on color feature extraction for mobile applications; this method used dichotomy to extract the color features of the template image; then, it employed the Quartz2D engine of iOS based on the color features of the template image to draw the target image; finally, it utilized the iOS Metal interface to adjust the color saturation of the target image and sharpen the edge of the target image; during the entire process, the color style of the source image was transferred to the new image, realizing the transfer system of image color style for IOS applications ( Cai et al, 2019 ). However, it only transfers the image color.…”

Section: Discussionmentioning

confidence: 99%

Application of Deep Learning Algorithms to Visual Communication Courses

Wang

et al. 2021

Front. Psychol.

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There are rare studies on the combination of visual communication courses and image style transfer. Nevertheless, such a combination can make students understand the difference in perception brought by image styles more vividly. Therefore, a collaborative application is reported here combining visual communication courses and image style transfer. First, the visual communication courses are sorted out to obtain the relationship between them and image style transfer. Then, a style transfer method based on deep learning is designed, and a fast transfer network is introduced. Moreover, the image rendering is accelerated by separating training and execution. Besides, a fast style conversion network is constructed based on TensorFlow, and a style model is obtained after training. Finally, six types of images are selected from the Google Gallery for the conversion of image style, including landscape images, architectural images, character images, animal images, cartoon images, and hand-painted images. The style transfer method achieves excellent effects on the whole image besides the part hard to be rendered. Furthermore, the increase in iterations of the image style transfer network alleviates lack of image content and image style. The image style transfer method reported here can quickly transmit image style in less than 1 s and realize real-time image style transmission. Besides, this method effectively improves the stylization effect and image quality during the image style conversion. The proposed style transfer system can increase students’ understanding of different artistic styles in visual communication courses, thereby improving the learning efficiency of students.

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Section: Discussionmentioning

confidence: 99%

Application of Deep Learning Algorithms to Visual Communication Courses

Wang

et al. 2021

Front. Psychol.

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“…Dichotomy is utilized to extract color features of template images. e color style of the source image can be transferred to a new image, which can be used as a design element in product design to improve the speed of product design [2]. Bai et al proposed a multiscale and multilayer feature fusion network, which uses a local binary pattern to generate three LBP-RGB feature maps.…”

Section: Related Workmentioning

confidence: 99%

Design of Watercolor Cultural and Creative Products Based on Style Transfer Algorithm

Wang

Alamusi.²

2022

Mathematical Problems in Engineering

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Due to the long design cycle of watercolor cultural and creative products, a design method based on style transfer algorithm is proposed. Here, the design of Shanghai style watercolor painting cultural and creative products is taken as the research object, VGG-19 network is used as the style transfer model, and the loss function of style transfer algorithm is designed. The simulation results show that the proposed method can effectively extract and reconstruct the color, texture, brushstroke, and other styles of Shanghai style watercolor painting. The generated new images are added as design elements, which makes the cultural and creative products have both appearance and personalization. In addition, the newly generated cultural and creative products have also obtained high satisfaction from experts and users, which meet the aesthetic changes. It can be seen that proposed method provides a new idea for the design of watercolor cultural and creative products, which shortens the product design cycle, reduces the design cost, and has a certain practicality.

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“…At the same time, it refers to and summarizes the research and development experience of other industrial inspection systems, as well as the latest scienti c research achievements in the elds of mathematics and computer technology [6]. The representation of content and style in machine vision based on genetic algorithm is separable, and then the two representations can be processed independently on this basis, thus forming a new style migration of images [7]. The machine vision technology based on genetic algorithm aims to imitate human vision system.…”

Section: Introductionmentioning

confidence: 99%

Application of machine vision based on genetic algorithm in image painting style method and image processing optimization

Jin

2023

Soft Comput

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In recent years, the research on the non photorealistic painting of watercolor painting through computer algorithms has made rapid progress, and its speci c application has also been increasingly valued. This paper proposes a new machine vision method based on genetic algorithm, and applies it to the research of oil painting style and image processing optimization. First of all, it provides a stroke layout method based on machine vision important areas. It analyzes the key areas through face, visual focus analysis and other technologies, and realizes the starting point, angle calculation, accuracy optimization and image quality optimization of pen painting based on the data of this area, thus realizing the function of distinguishing the primary and secondary of oil painting. Secondly, the machine vision method based on genetic algorithm can explore the style characteristics of any oil painting graphics according to the characteristics of oil painting, and create artistic images through this method. The experimental resultsshow that the machine vision method based on genetic algorithm not only has achieved good results in the practical application of oil painting style, but also can optimize the image processing.

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Image color style migration method for mobile applications based color feature extraction

Cited by 5 publications

References 6 publications

Application of Deep Learning Algorithms to Visual Communication Courses

Application of Deep Learning Algorithms to Visual Communication Courses

Design of Watercolor Cultural and Creative Products Based on Style Transfer Algorithm

Application of machine vision based on genetic algorithm in image painting style method and image processing optimization

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