Image-Abstraction Framework as a Preprocessing Technique for Extraction of Text From Underexposed Complex Background and Graphical Embossing Images

Kumar, Pavan; Poornima, B.; Nagendraswamy, H. S.; Manjunath, C R; Rangaswamy, B. E.

doi:10.4018/ijdai.2021010101

Cited by 3 publications

(4 citation statements)

References 25 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%

“…A noticeable feature of the K-TMO is its ability to retrieve the overexposed picture features and it is the most useful in the domain of scene analysis application systems and surveillance monitoring, where the target scene is absolutely blatant. The exhaustive accomplishment of Kirana TMO is presented in our previous article [11].…”

Section: Kirana Tone Mapping Operator (K-tmo)mentioning

confidence: 99%

“…weighted gradient and sticks filtering, and it provides a better outcome in terms of safeguarding the visualization of nonlinear and linear leading structures. The detailed implementation of WGSF is expressed in the previous article [11,14].…”

Section: Weighted Gradient Sticks Filtering (Wgsf)mentioning

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: Kirana Tone Mapping Operator (K-tmo)mentioning

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

View full text Add to dashboard Cite

show abstract

“…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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HDR and image abstraction framework for dirt free line drawing to convey the shapes from blatant range images

Kumar¹,

Poornima²,

Nagendraswamy

et al. 2021

Multidim Syst Sign Process

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Image-Abstraction Framework as a Preprocessing Technique for Extraction of Text From Underexposed Complex Background and Graphical Embossing Images

Cited by 3 publications

References 25 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

HDR and image abstraction framework for dirt free line drawing to convey the shapes from blatant range images

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