Name
Abstract | ||
|---|---|---|
A substantial number of local feature extraction and description methodologies have been proposed as image recognition algorithms. However, these algorithms do not exhibit adequate performance with regard to repeatability, accuracy, and time consumption for both affine transformation and monotonic intensity change. In this paper, we propose a new descriptor, named Resistant to Affine Transformation and Monotonic Intensity Change (RATMIC). Unlike traditional descriptors, we utilize an adaptive division strategy and intensity order to construct the new descriptor, which is actually resistant to affine transformation and monotonic intensity change. Extensive experiments demonstrate the effectiveness and efficiency of the new descriptor compared to existing state-of-the-art descriptors. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.cviu.2013.10.010 | Computer Vision and Image Understanding |
Keywords | Field | DocType |
description methodology,new descriptor,adequate performance,state-of-the-art descriptors,intensity order,monotonic intensity change,affine transformation,extensive experiment,adaptive division strategy,traditional descriptors,new descriptor resistant | Affine transformation,Affine shape adaptation,Computer vision,Monotonic function,Topology,Algorithm,Feature extraction,Artificial intelligence,Intensity change,Mathematics | Journal |
Volume | Issue | ISSN |
120, | 1 | 1077-3142 |
Citations | PageRank | References |
5 | 0.42 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeyi Huang | 1 | 5 | 0.42 |
| Wenxiong Kang | 2 | 102 | 17.58 |
| Qiuxia Wu | 3 | 103 | 9.25 |
| Xiaopeng Chen | 4 | 5 | 0.42 |