Name
Abstract | ||
|---|---|---|
Image registration has received great attention from researchers over the last few decades. SIFT (Scale Invariant Feature Transform), a local descriptor-based technique is widely used for registering and matching images. To establish correspondences between images, SIFT uses a Euclidean Distance ratio metric. However, this approach leads to a lot of incorrect matches and eliminating these inaccurate matches has been a challenge. Various methods have been proposed attempting to mitigate this problem. In this paper, we propose a scale and orientation harmony-based pruning method that improves image matching process by successfully eliminating incorrect SIFT descriptor matches. Moreover, our technique can predict the image transformation parameters based on a novel adaptive clustering method with much higher matching accuracy. Our experimental results have shown that the proposed method has achieved averages of approximately 16% and 10% higher matching accuracy compared to the traditional SIFT and a contemporary method respectively. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/DICTA.2018.8615800 | 2018 Digital Image Computing: Techniques and Applications (DICTA) |
Keywords | Field | DocType |
scale ratio,orientation difference,K-means clustering,SIFT,local descriptor,key point,Euclidean distance,image registration | k-means clustering,Computer vision,Scale-invariant feature transform,Pattern recognition,Computer science,Image matching,Euclidean distance,Image transformation,Artificial intelligence,Cluster analysis,Image registration | Conference |
ISBN | Citations | PageRank |
978-1-5386-6603-6 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Md Tahmid Hossain | 1 | 0 | 0.68 |
| Shyh Wei Teng | 2 | 151 | 21.02 |
| Dengsheng Zhang | 3 | 2462 | 100.00 |
| Suryani Lim | 4 | 0 | 0.34 |
| Guojun Lu | 5 | 124 | 9.01 |