Name
Abstract | ||
|---|---|---|
Continued increase in complexity of digital image sensors means that defects are more likely to develop in the field, but little concrete information is available on in-field defect growth. This paper presents an algorithm to help quantify the problem by identifying defects and potentially tracking defect growth. Building on previous research, this technique is extended to utilize a more realistic defect model suitable for analyzing real-world camera systems. Monte Carlo simulations show that abnormal sensitivity defects are successfully detected by analyzing only 40 typical photographs. Experimentation also indicates that this technique can be applied to imagers with up to 4% defect density, and that noisy images can be diagnosed successfully with only a small reduction in accuracy. Extension to colour imagers has been accomplished through independent analysis of colour planes of images. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/DFT.2006.48 | Arlington, VA |
Keywords | Field | DocType |
Monte Carlo methods,fault tolerant computing,image colour analysis,image sensors,sensor arrays,Monte Carlo simulations,abnormal sensitivity defects,colour imagers,defect growth,image sensor arrays,in-field defects,on-line mapping,real-world camera systems | Computer vision,Monte Carlo method,Image sensor,Computer graphics (images),Computer science,Electronic engineering,Digital image,Artificial intelligence | Conference |
ISSN | ISBN | Citations |
1550-5774 | 0-7695-2706-X | 4 |
PageRank | References | Authors |
1.08 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jozsef Dudas | 1 | 20 | 4.58 |
| Cory Jung | 2 | 7 | 2.34 |
| Tzong-Lin Wu | 3 | 4 | 2.77 |
| Glenn H. Chapman | 4 | 167 | 34.10 |