Name
Title | ||
|---|---|---|
Detecting Defects On Citrus Fruit Surface By Circularity Gradient Threshold Segmentation |
Abstract | ||
|---|---|---|
This paper presented an automatic segmentation method to detect defects on the surface of citrus fruits eroded by diseases or pests based on circularity gradient threshold. A visible imaging system was built and citrus fruits were shot by this system. The chromatic aberration map of green and blue components was obtained, and a segmentation method with a circularity gradient threshold was used to detect defects from healthy regions on fruit surface. The erosion coefficient was used to quantify erosion degree by diseases or pests. The segmentation results showed that by using circularity gradient threshold segmentation method, the missing alarm rate is increased by 0.19%, but the false alarm rate is decreased by 4.44% versus global Otsu threshold method on average. The algorithm can detect defects on fruit surface accurately and integrally, and it has important potential in fruit grading, control of diseases and pests. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD) | Citrus fruits, defects, circularity gradient threshold, chromatic aberration map, segmentation |
Field | DocType | Citations |
Pattern recognition,ALARM,Computer science,Segmentation,Visible imaging,Chromatic aberration,Image segmentation,Artificial intelligence,Constant false alarm rate,Machine learning,Grayscale | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |