A survey of recent advances in visual feature detection

Abstract

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. We survey the recent progress and advances in visual feature detection.The relations among different kinds of features are covered.Representative feature detection algorithms are described.We categorize and discuss the pros/cons for different kinds of visual features.We put some emphasis on future challenges in feature design through this survey.