Structure-preserving NPR framework for image abstraction and stylization

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

doi:10.1007/s11227-020-03547-w

Cited by 32 publications

(9 citation statements)

References 81 publications

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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%

“…The detailed literature on monument protection program guidelines and application has been reported by Adham [56]. Our previously published papers [1,10] enlighten the thorough literature survey on NPR image abstraction, covering for the period of 1960-2021 and it also mentions their significant future scope, applications, and challenges. The additional meticulous literature on NPR image abstraction has been reported by Kyprianidis et al [12].…”

Section: Machine Learning Feature Extraction and Classification Of Archaeological Monumentmentioning

confidence: 99%

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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.

Self Cite

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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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Section: Image Abstractionmentioning

confidence: 99%

Section: Machine Learning Feature Extraction and Classification Of Archaeological Monumentmentioning

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.

Self Cite

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“…Later discussed about heterogeneous distributed storage description and their challenges using different methods. Kumar et al, [17] presented the importance of processing diversified data in High performance computing environment (HPC) [18][19][20] and their constraints and challenges are clearly stated. Varsha M et al, [21] give a more detailed description of rice blast disease data.…”

Section: Utilization Of Analysis Resultsmentioning

confidence: 99%

Literature Survey on Big-Data Analytics and Tools

P¹,

Pavan²,

C³

2022

JJEM

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In the present technological world, a huge quantity of heterogeneous and complex data have been generated from various sources such as social media applications, sensors, IoT devices, Smartphone's, demographic data, business research and scientific data, worksheets, blogs, forums, E-commerce websites, search engines etc., These drastic data are more useful in one another way in our business or social activities to make some decisions. Thus, to get insight into data to find desired pattern it is necessary to make empirical analysis on the data. In this article, we have made a comprehensive literature that briefed the characteristics of big data, tools and techniques which are avail for processing heterogeneous-data and HDFS file systems to store the big-data.

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“…The proposed research work comprises four stages: (1) pre‐processing, (2) segmentation, (3) post‐processing, and (4) classification. At first, pre‐processed the input image by guided box filtering (GBF) (Kumar et al, 2021) for contrast enhancement and Background exclusion of input image. Then, the pre‐processed image is provided to segmentation procedure.…”

Section: Proposed Methodologymentioning

confidence: 99%

Micro aneurysm detection using optimizedresidual‐basedtemporal attentionConvolutional Neural NetworkwithInception‐V3transfer learning

Alotaibi

2024

Microscopy Res & Technique

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In this manuscript micro aneurysm detection using residual‐based temporal attention Convolutional Neural Network (CNN) with Inception‐V3 transfer learning optimized with equilibrium optimization algorithm (MA‐RTCNN‐Inception V3‐EOA) is proposed. The proposed research work contains four phases: (1) pre‐processing, (2) segmentation, (3) post‐processing, and (4) classification. At first, guided box filtering for contrast enhancement and background exclusion of input image. The proposed MA‐RTCNN‐Inception V3‐EOA based classification framework is implemented in MATLAB using several performances evaluating metrics like precision, sensitivity, f‐measure, specificity, accuracy, classification error rate, and Matthews's correlation coefficient and RoC analysis. The experimental outcome demonstrates that the proposed method provides 23.56%, 14.99%, and 21.37% higher accuracy and 31.26%, 57.69%, and 21.14% minimum classification error rate compared to existing methods, such as diabetic retinopathy identification utilizing prognosis of micro aneurysm and early diagnosis for non‐proliferative diabetic retinopathy depending on deep learning approaches (DRD‐CNN‐NPDR), a magnified adaptive feature pyramid network for automatic micro aneurysms identification (MAFPN‐AMD‐MAFP‐Net) respectively.Research Highlights Micro aneurysm detection using residual‐based temporal attention Convolutional Neural Network (CNN) is proposed. To get rid of the retina background, guided box filtering is applied. COAT is used for segmenting the images into smaller parts RTCNN is used for accurate micro aneurysms disease classification. RT‐CNN algorithm successfully identifies the micro aneurysms using EOA.

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Structure-preserving NPR framework for image abstraction and stylization

Cited by 32 publications

References 81 publications

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

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

Literature Survey on Big-Data Analytics and Tools

Micro aneurysm detection using optimizedresidual‐basedtemporal attentionConvolutional Neural NetworkwithInception‐V3transfer learning

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