A comprehensive survey on non-photorealistic rendering and benchmark developments for image abstraction and stylization

Kumar, M. Pawan; Poornima, B.; Nagendraswamy, H. S.; Manjunath, C R

doi:10.1007/s42044-019-00034-1

Cited by 28 publications

(19 citation statements)

References 137 publications

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“…ICC-based operating system-level color management software uses computer core technology to provide a color management environment for other application software and uses the operating system to provide a series of functions, which is transparent to users and does not require application overhead. Color management is implemented independently at the operating system level [12].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Design and Realization of Animation Composition and Tone Space Conversion Algorithm

Liang

2021

Complexity

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In recent years, with the development of society and the rapid development of the animation industry, people are paying more and more attention to and requirements for animation production. As an indispensable part of animation production, picture composition plays a major role in animation production. It can give full play to the application of color matching and light and shadow design and enhance the depth and space of the animation screen. Tone space conversion refers to the conversion or representation of color data in one color space into corresponding data in another color space. Its purpose is to distinguish and process color components such as hue and saturation in an image. This article first introduces the domestic and foreign research status of digital image preprocessing and analyzes the basic principles of several color space conversions in detail. Then, several color space conversion algorithms are studied, and the performance of the algorithms is compared and analyzed. The paper focuses on the hardware implementation and optimization of the algorithm for converting RGB color space into HSI color space to meet the real-time requirements. This article focuses on the mutual conversion between the RGB tone space and the HSI tone space and describes in detail how each color component in the HSI tone space is converted from the three RGB color components from a geometric perspective, and then the conversion is derived, and several general conversion methods of RGB to HSI tone space are introduced; two conversion methods of geometric derivation method and standard modulus algorithm are implemented in the software, and the comparison verification is carried out, and the comparison is made from the perspective of hardware implementation. The pros and cons of the two methods are discussed. Finally, the paper summarizes the shortcomings in the design and proposes further research directions in the future.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Design and Realization of Animation Composition and Tone Space Conversion Algorithm

Liang

2021

Complexity

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“…Kumar et al [1,10,11] presented a comprehensive literature survey on current progress of NPR line drawing, abstraction and artistic stylization of images from 1960 to 2020 has been reported. The work also enlightens the scope for further work on diversified applications, limitations and challenges thereof.…”

Section: Image Abstractionmentioning

confidence: 99%

“…Non-photorealistic rendering (NPR) is a promising structure-preserving image abstraction and decorative stylization methodology since the nineteenth century and emerged as a division of image analysis, computer graphics and cuttingedge computer vision research [1,2]. It brings down the complexity of image sample and renders the most excellent delineation by protecting the noticeable structural patterns and suppressing the obscure features from the input images.…”

Section: Introductionmentioning

confidence: 99%

Image Abstraction Framework as a Pre-processing Technique for Accurate Classification of Archaeological Monuments Using Machine Learning Approaches

Kumar¹,

Poornima

Nagendraswamy

et al. 2021

SN COMPUT. SCI.

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This work extricates the image characteristic features for the classification of archeological monument images. At the preprocessing stage, archeological dataset sample images are treated by using structure safeguarding image abstraction framework, which can deliver the most effective image abstraction output by manipulating the perceptible features in the given low-illuminated and underexposed color image samples. Proposed abstraction-framework effectively boosted the significant image property features like color, edge, sharpness, contrast and suppresses complexity and noise. The image properties were also refined at each phase based on the attained statistical feature disposal information. The work adopted the Harris feature identification technique to identify the most significant image features in the input and enhanced images. The framework also preserves significant features in the foreground of an image by intelligently integrating the series of filters during rigorous experimental work and also diminishes the background content of an input image. The proposed archeological system evaluates every stage of the result with assorted subjective matters and calculates the image quality and properties assessment statistical attributes. By this way prominent features in an image have been recognized. The efficiency of this work has been corroborated by performing the trials on the selected archeological dataset. In addition, user's visual feedback and the standard image quality assessment techniques were also used to evaluate the proposed pre-processing framework. Based on the obtained abstraction images from the framework, this work extracts the image gray color texture features using GLCM, color texture from CTMs and deep-learning features from AlexNet for the classification of archeological monument classification. This work adopted a support vector machine as a classifier. To corroborate the efficiency of the proposed method, an experiment was conducted on our own data set of Chalukya, Kadamba, Hoysala and new engraving monuments, each domain consisting of 500 archeological data set samples with large intra-class variation, with different environmental lighting condition, low-illumination and different pose. Implementation of this work was carried out in MATLAB-2020 with HPC Nvidia Tesla P100 GPU, and obtained results show that combination of multiple features significantly improves the performance to the extent of 98.10%.

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“…Kumar et al [14] recently produced a NPR benchmark that closely follows the principles of NPRgeneral. It consists of 32 images, and its goal was to augment the NPRgeneral with more varied and more complex type images.…”

Section: Benchmark Datasetsmentioning

confidence: 99%

Benchmarking non-photorealistic rendering of portraits

Rosin

Mould

Berger

et al. 2017

Proceedings of the Symposium on Non-Photorealistic Animation and Rendering

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Despite the recent upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer, the state of performance evaluation in this field is limited, especially compared to the norms in the computer vision and machine learning communities. Unfortunately, the task of evaluating image stylisation is thus far not well defined, since it involves subjective, perceptual and aesthetic aspects. To make progress towards a solution, this paper proposes a new structured, three level, benchmark dataset for the evaluation of stylised portrait images. Rigorous criteria were used for its construction, and its consistency was validated by user studies. Moreover, a new methodology has been developed for evaluating portrait stylisation algorithms, which makes use of the different benchmark levels as well as annotations provided by user studies regarding the characteristics of the faces. We perform evaluation for a wide variety of image stylisation methods (both portrait-specific and general purpose, and also both traditional NPR approaches and neural style transfer) using the new benchmark dataset.

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A comprehensive survey on non-photorealistic rendering and benchmark developments for image abstraction and stylization

Cited by 28 publications

References 137 publications

[Retracted] Design and Realization of Animation Composition and Tone Space Conversion Algorithm

[Retracted] Design and Realization of Animation Composition and Tone Space Conversion Algorithm

Image Abstraction Framework as a Pre-processing Technique for Accurate Classification of Archaeological Monuments Using Machine Learning Approaches

Benchmarking non-photorealistic rendering of portraits

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