Name
Abstract | ||
|---|---|---|
This paper presents a method for identifying the most similar image in the visible image library and the given infrared image. First the method of visible and infrared image edge extraction respectively, and then use the perceptual hash encoding method of edge image, and get the image similarity by comparing the two image hash encoding Hamming distance. As the infrared image noise more perceptual hash algorithm based on pixel values may have errors of judgment, so the use of hash algorithm to get similarity on the 3 visible images using the SURF algorithm to compare the similarity. Based on the slope consistency, two image matching points are obtained. The most visible images of matching points are selected as the most similar images to infrared images. Experimental results show that the method can identify the visible image with the highest similarity between infrared image and visible image library. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICAIT.2017.8388951 | 2017 9th International Conference on Advanced Infocomm Technology (ICAIT) |
Keywords | Field | DocType |
perception hash,SURF,edge extraction,slope consistent | Computer vision,Infrared image,Computer science,Feature extraction,Hamming distance,Artificial intelligence,Hash function,Pixel,Infrared,Perception,Encoding (memory) | Conference |
ISBN | Citations | PageRank |
978-1-5386-3629-9 | 0 | 0.34 |
References | Authors | |
3 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guohui Zhou | 1 | 73 | 29.90 |
| Lijun Xiao | 2 | 1 | 2.03 |
| Xingyu Pei | 3 | 0 | 0.34 |
| Chenxi Li | 4 | 25 | 11.27 |
| Huiping Qin | 5 | 1 | 0.71 |
| Jinpeng Zhang | 6 | 0 | 0.34 |
| Zhiyu Fang | 7 | 0 | 0.34 |