Name
Abstract | ||
|---|---|---|
A new method for extracting the dots is proposed by the reflected light image of porous silicon (PSi) microarray utilization in this paper. The method consists of three parts: pretreatment, tilt correction and spot segmentation. First, based on the characteristics of different components in HSV (Hue, Saturation, Value) space, a special pretreatment is proposed for the reflected light image to obtain the contour edges of the array cells in the image. Second, through the geometric relationship of the target object between the initial external rectangle and the minimum bounding rectangle (MBR), a new tilt correction algorithm based on the MBR is proposed to adjust the image. Third, based on the specific requirements of the reflected light image segmentation, the array cells are segmented into dots as large as possible and the distance between the dots is equal in the corrected image. Experimental results show that the pretreatment part of this method can effectively avoid the influence of complex background and complete the binarization processing of the image. The tilt correction algorithm has a shorter computation time, which makes it highly suitable for tilt correction of reflected light images. The segmentation algorithm makes the dots in a regular arrangement, excludes the edges and the bright spots. This method could be utilized in the fast, accurate and automatic dots extraction of the PSi microarray reflected light image. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/s17061335 | SENSORS |
Keywords | Field | DocType |
porous silicon microarray,feflected light image,pretreatment,tilt correction,spot segmentation | Computer vision,HSL and HSV,Minimum bounding rectangle,Change detection,Segmentation,Rectangle,Hue,Image processing,Image segmentation,Artificial intelligence,Engineering | Journal |
Volume | Issue | ISSN |
17 | 6.0 | 1424-8220 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiqing Guo | 1 | 0 | 0.34 |
| Zhenhong Jia | 2 | 29 | 15.13 |
| Jie Yang | 3 | 282 | 57.59 |
| Nikola K Kasabov | 4 | 3645 | 290.73 |
| Chuan-Xi Li | 5 | 4 | 2.44 |