Name
Abstract | ||
|---|---|---|
Auto-focusing task, which automatically obtains the best image focus, plays an important role to improve the image definition for the industrial image measurement application. Image-based auto-focusing is one of the widely used methods for this task because of its fast response, convenience, and intelligence. In general, the image-based auto-focusing algorithm often consists of two important steps which are the image definition evaluation and the search strategy. In this paper, we have developed an image auto-focusing algorithm for industrial image measurement. First, we propose a new image definition evaluation method based on the fuzzy entropy, which can reduce the negative effects of noise and variations of light intensity and lens magnification. Second, a combined search method is proposed to combine the multi-scale global search and fine-level curve fitting method, which can avoid the disturbance of the local peaks and obtain the best image focus. The proposed image auto-focusing algorithm has the advantages of high focusing accuracy, high repeatability and stability under the variations of lens magnification, and light intensity index, which make it applicable for the industrial image measurement. Experimental results and comparisons on the practical industrial image measurement system have been presented to show the effectiveness and superiority of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1186/s13634-016-0368-5 | EURASIP J. Adv. Sig. Proc. |
Keywords | Field | DocType |
Image auto-focusing, Fuzzy entropy, Image definition evaluation, Global search, Curve fitting, Industrial image measurement | Computer vision,Image warping,Feature detection (computer vision),Non-local means,Computer science,Image texture,Binary image,Image quality,Image processing,Algorithm,Artificial intelligence,Digital image processing | Journal |
Volume | Issue | ISSN |
2016 | 1 | 1687-6180 |
Citations | PageRank | References |
1 | 0.43 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuxin Liu | 1 | 1 | 0.43 |
| Manhua Liu | 2 | 323 | 23.91 |
| Zhongyuan Yang | 3 | 1 | 0.43 |