.png){kind=icon}
返回
Shape classification via image-based multiscale description
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2011.02.016.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We introduce a new multiscale Fourier-based object description in 2-D space using a low-pass Gaussian filter (LPGF) and a high-pass Gaussian filter (HPGF), separately. Using the LPGF at different scales (standard deviation) represents the inner and central part of an object more than the boundary. On the other hand using the HPGF at different scales represents the boundary and exterior parts of an object more than the central part. Our algorithms are also organized to achieve size, translation and rotation invariance. Evaluation indicates that representing the boundary and exterior parts more than the central part using the HPGF performs better than the LPGF-based multiscale representation, and in comparison to Zernike moments and elliptic Fourier descriptors with respect to increasing noise. Multiscale description using HPGF in 2-D also outperforms wavelet transform-based multiscale contour Fourier descriptors and performs similar to the perimeter descriptors without any noise. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Shape classification
Fourier-based description
Multiscale representation
Gaussian filter
Feature extraction
Computer vision
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏
查看更多内容