A survey of recent advances in visual feature detection
A survey of recent advances in visual feature detection
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DOI：
10.1016/j.neucom.2014.08.003
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发表时间：
2015-02-03
期刊：
影响因子：
6
通讯作者：
Ding, Xiaoqing
中科院分区：
文献类型：
--
作者：
Li, Yali;Wang, Shengjin;Ding, Xiaoqing
特征检测是计算机视觉和图像处理中的一个基本且重要的问题。它是一个低级处理步骤，是基于计算机视觉的应用程序的重要部分。本文的目的是对视觉特征检测的最新进展和进展进行调查。首先我们从心理学角度描述边缘、角点和斑点之间的关系。其次，我们将检测边缘、角点和斑点的算法分为不同的类别，并为每个类别中代表性的最新算法提供详细的描述。考虑到机器学习越来越多地涉及视觉特征检测，我们更加重视基于机器学习的特征检测方法。第三，介绍了评价标准和数据库。通过这项调查，我们希望展示视觉特征检测的最新进展，并确定未来的趋势和挑战。 (C) 2014 Elsevier B.V. 保留所有权利。
Feature detection is a fundamental and important problem in computer vision and image processing. It is a low-level processing step which serves as the essential part for computer vision based applications. The goal of this paper is to present a survey of recent progress and advances in visual feature detection. Firstly we describe the relations among edges, corners and blobs from the psychological view. Secondly we classify the algorithms in detecting edges, corners and blobs into different categories and provide detailed descriptions for representative recent algorithms in each category. Considering that machine learning becomes more involved in visual feature detection, we put more emphasis on machine learning based feature detection methods. Thirdly, evaluation standards and databases are also introduced. Through this survey we would like to present the recent progress in visual feature detection and identify future trends as well as challenges. (C) 2014 Elsevier B.V. All rights reserved.