Orthogonal polynomial embedded image kernel

S. Abdulhussain, A. Ramli, A. Hussain, Basheera M. Mahmmod, Wissam A. Jassim
{"title":"Orthogonal polynomial embedded image kernel","authors":"S. Abdulhussain, A. Ramli, A. Hussain, Basheera M. Mahmmod, Wissam A. Jassim","doi":"10.1145/3321289.3321310","DOIUrl":null,"url":null,"abstract":"Preprocessing operations of images and video frame sequences are beneficial in computer vision algorithms. For example, smoothing frames is used to eliminate noise; while computing frame gradient in x-direction and y-direction is used for frame feature extraction or for finding frame edges. Such operations involve convolving operators (image kernels) with an image precomputing moments will add extra computation cost to computer vision algorithm. In case of video, the computational time accumulatively increased because of the convolution operation for each frame is performed. To overcome this problem, a mathematical model is established for computing preprocessed frame moments via embedding the operator (image kernel) in the orthogonal polynomial (OP) functions. The experimental results show that the computation time for feature extraction using the proposed method is noticeably reduced in the both trends: image size and moment selection order. The average speed up ratio of the proposed method to traditional method is 3x, 5x, 8x, and 40x for moment selection ratio 100%, 25%, 10%, and 5%, respectively. In addition, the percentage reduction in processing time for small image size is ~ 99% and for large image size is ~ 40%.","PeriodicalId":375095,"journal":{"name":"Proceedings of the International Conference on Information and Communication Technology - ICICT '19","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the International Conference on Information and Communication Technology - ICICT '19","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3321289.3321310","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20

Abstract

Preprocessing operations of images and video frame sequences are beneficial in computer vision algorithms. For example, smoothing frames is used to eliminate noise; while computing frame gradient in x-direction and y-direction is used for frame feature extraction or for finding frame edges. Such operations involve convolving operators (image kernels) with an image precomputing moments will add extra computation cost to computer vision algorithm. In case of video, the computational time accumulatively increased because of the convolution operation for each frame is performed. To overcome this problem, a mathematical model is established for computing preprocessed frame moments via embedding the operator (image kernel) in the orthogonal polynomial (OP) functions. The experimental results show that the computation time for feature extraction using the proposed method is noticeably reduced in the both trends: image size and moment selection order. The average speed up ratio of the proposed method to traditional method is 3x, 5x, 8x, and 40x for moment selection ratio 100%, 25%, 10%, and 5%, respectively. In addition, the percentage reduction in processing time for small image size is ~ 99% and for large image size is ~ 40%.
正交多项式嵌入图像核
图像和视频帧序列的预处理操作对计算机视觉算法是有益的。例如,平滑帧用于消除噪声;而在x方向和y方向上计算帧梯度用于提取帧特征或寻找帧边缘。这些操作涉及卷积算子(图像核),具有图像预计算矩,会给计算机视觉算法增加额外的计算成本。在视频的情况下,由于对每帧进行卷积操作,计算时间累积增加。为了克服这一问题,通过在正交多项式(OP)函数中嵌入算子(图像核),建立了计算预处理帧矩的数学模型。实验结果表明,在图像大小和矩选择顺序两方面,采用该方法进行特征提取的计算时间都明显缩短。当矩选择比为100%、25%、10%和5%时,本文方法与传统方法的平均加速比分别为3x、5倍、8倍和40倍。此外,小图像尺寸的处理时间减少百分比为~ 99%,大图像尺寸的处理时间减少百分比为~ 40%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信