Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol. 9, Issue 5 , 2022
Graphics Extraction - Vector Processing XSLT and XML Transformation

Author : Irudaya Ratnam S 1 Dr. Nalini Chidambaram 2 A. Punitha 3

Date of Publication :7th March 2016

Abstract: Software application has reduced many manual related repeated works. Traditional sparse image models treat color image pixel as a scalar, which represents color channels separately or concatenate color channels as a monochrome image (Sharpening the images done by designing team). In this project, we propose a vector art representation model for images using quaternion matrix analysis. End user will view these images from DOCX or any other form of documentations for example: PDF file, these document files are for example: prepared by various Doctors using chemical theories and published for student reference. If user wants information regarding study materials, student will have links to the website then they need to download the document files and they will refer the images for their studies. To reduce these manual work process called manual image processing (MIP). In this project new approaches got implemented that is vector art graphics extraction. Using this algorithm user will create images and published for the end user for example students. Once published end users will refer these images and user will use it for their own educations study material preparation purpose. Also by separating these images it will reduce the size of storage in the local system disk space. Since if we store the document files it would occupy more space when compare to individual image files with captions. The images will be in the following order: Horizontal, Vertical and Image inside the image that is small part of the image over the part of main image. Current system will provide only sharpen the image. That is Vector sparse representation, quaternion matrix analysis, color image, dictionary learning, and image restored in a location. Only related images user can extract and prepare with their study materials, instead of depending on the document. These approaches reduce the disk space.

Reference :

    1. Graphics extraction from heterogeneous online documents with hierarchical random fields – 2015 - Published: Friday, 25 December 2015 - Graphical objects are important elements of freely handwritten notes but their segmentation from the document is challenging due to their irregular properties. This project introduces an original solution for automatically segmenting diagrams and drawings from unstructured online documents (Basic logic of this project got from this project approach.)
    2. Context-Aware Patch-Based Image In-painting Using Markov Random Field Modeling – 2015 - Published: Friday, 25 December 2015 - where textural descriptors are used to guide and accelerate the hunt for wellmatching (candidate) patches. A completely unique highdown splitting procedure divides the image into variable size blocks consistent with their context, constraining thereby the rummage around for candidate patches to nonlocal image regions with matching context. (This part of logic used to split images and its text context)
    3. Semi supervised Biased Maximum Margin Analysis for Interactive Image Retrieval – 2012 - Published: Thursday, 27 September 2012. - With many potential practical applications, content-based image retrieval (CBIR) has attracted substantial attention during the past few years. A variety of relevance feedback (RF) schemes have been developed as a powerful tool to bridge the semantic gap between low-level visual features and high level semantic concepts, and thus to improve the performance of CBIR systems. (Using this approach we implement content based 8 images extraction logic using xml and xslt/xsl)
    4. Image Super resolution Using Support Vector Regression - Published: Monday, 25 June 2012 - Support vector machine (SVM) is a statistical learning algorithm that is capable of estimating high-dimensional functions. Recently, support vector regression (SVR) - the use of SVM for regression - has been used to generate superresolution images. In this paper, we propose to apply the SVR algorithm on edge pixels only so as to reduce the emboss effect that would appear in the edge region of an enlarged image if the SVR is applied on the entire input image. Image vector processing principle and logic used by this approach.

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