.png){kind=icon}
.png){kind=icon}
Return
α-shapes for local feature detection
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2015.08.016.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Local image features are routinely used in state-of-the-art methods to solve many computer vision problems like image retrieval, classification, or 3D registration. As the applications become more complex, the research for better visual features is still active. In this paper we present a feature detector that exploits the inherent geometry of sampled image edges using alpha-shapes. We propose a novel edge sampling scheme that exploits local shape and investigate different triangulations of sampled points. We also introduce a novel approach to represent the anisotropy in a triangulation along with different feature selection methods. Our detector provides a small number of distinctive features that is ideal for large scale applications, while achieving competitive performance in a series of matching and retrieval experiments. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords:
Local features
Point sampling
Triangulation
alpha-shapes
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W