Iterative adaptive subdivision surface approach to reduce memory consumption in rendering process (IteAS)

Husain, Noor Asma; Rahim, Mohd. Shafry Mohd.; Khan, Aysha; Al-Rodhaan, Mznah; Saba, Tanzila

doi:10.3233/ifs-141308

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Many algorithms for segmentation and analysis have been proposed. Among them is pixel identification to differentiate vessel pixels according to calculated feature vectors rather manually (Husain et al, ; Odstrcilik et al, ).…”

Section: Proposed Methodologymentioning

confidence: 99%

Retinal imaging analysis based on vessel detection

Jamal

Alkawaz

Rehman

et al. 2017

Microscopy Res & Technique

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With an increase in the advancement of digital imaging and computing power, computationally intelligent technologies are in high demand to be used in ophthalmology cure and treatment. In current research, Retina Image Analysis (RIA) is developed for optometrist at Eye Care Center in Management and Science University. This research aims to analyze the retina through vessel detection. The RIA assists in the analysis of the retinal images and specialists are served with various options like saving, processing and analyzing retinal images through its advanced interface layout. Additionally, RIA assists in the selection process of vessel segment; processing these vessels by calculating its diameter, standard deviation, length, and displaying detected vessel on the retina. The Agile Unified Process is adopted as the methodology in developing this research. To conclude, Retina Image Analysis might help the optometrist to get better understanding in analyzing the patient's retina. Finally, the Retina Image Analysis procedure is developed using MATLAB (R2011b). Promising results are attained that are comparable in the state of art.

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Section: Proposed Methodologymentioning

confidence: 99%

Retinal imaging analysis based on vessel detection

Jamal

Alkawaz

Rehman

et al. 2017

Microscopy Res & Technique

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“…However, using neural networks a relationship could be drawn between inputs and outputs; hence these features are particularly suitable to assist in the character segmentation process. Literature is evident that ensemble of neural networks improve accuracy as compared to the individual network [56][57][58][59][60]. Accordingly, logic behind the ensemble of neural networks is to train few neural networks separately and finally their decisions are averaged to have a better decision.…”

Section: Ensemble Of Neural Networkmentioning

confidence: 99%

An ensemble of neural networks for nonlinear segmentation of overlapped cursive script

Rehman

2020

IJCVR

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Precise character segmentation is the only solution towards higher Optical Character Recognition (OCR) accuracy. In cursive script, overlapped characters are serious issue in the process of character segmentations as characters are deprived from their discriminative parts using conventional linear segmentation strategy. Hence, non-linear segmentation is an utmost need to avoid loss of characters parts and to enhance character/script recognition accuracy. This paper presents an improved approach for non-linear segmentation of the overlapped characters in handwritten roman script. The proposed technique is composed of a sequence of heuristic rules based on geometrical features of characters to locate possible non-linear character boundaries in a cursive script word. However, to enhance efficiency, heuristic approach is integrated with trained ensemble neural network validation strategy for verification of character boundaries. Accordingly, correct boundaries are retained and incorrect are removed based on ensemble neural networks vote. Finally, based on verified valid segmentation points, characters are segmented non-linearly. For fair comparison CEDAR benchmark database is experimented. The experimental results are much better than conventional linear character segmentation techniques reported in the state of art. Ensemble neural network play vital role to enhance character segmentation accuracy as compared to individual neural networks.

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“…Alternatively, a robust watermark should not be failed unless a large amount of marked data is ignored. In designing process of a watermarking system, it is considered to attack and intended applications that could answer or correspond .…”

Section: Watermarking Propertiesmentioning

confidence: 99%

Concise analysis of current text automation and watermarking approaches

Alkawaz

Sulong

Saba

et al. 2016

Security Comm Networks

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With ceaseless utilization of web and other online advances, it has turned out to be amazingly simple to imitate, discuss, and convey digital material. Subsequently, confirmation and copyright assurance issues have been emerged. Text is the most widely used media of communication on the web as compared with images and videos. The significant part of books, daily papers, websites, commercial, research papers, reports, and numerous different archives are just the plain text. Therefore, copyrights protection of plain text is a critical issue that could not be accepted at all. This paper presents a concise analysis on information hiding techniques and their consequences. Various watermarking systems properties are highlighted along with different types of attacks and their possible defenses. Additionally, the current applications for text document watermarking and the reasons for the problems of text document watermarking are described. Finally, attacks characteristics are discussed including types of attacks, volumes, and nature on text. Current watermarking approaches are reviewed in depth, and consequently, their advantages and weaknesses are highlighted.

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Iterative adaptive subdivision surface approach to reduce memory consumption in rendering process (IteAS)

Cited by 7 publications

References 17 publications

Retinal imaging analysis based on vessel detection

Retinal imaging analysis based on vessel detection

An ensemble of neural networks for nonlinear segmentation of overlapped cursive script

Concise analysis of current text automation and watermarking approaches

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