A machine vision approach to detect and categorize hydrocarbon fires in aircraft dry bays and engine compartments

IEEE Transactions on Industry Applications

Volumn 36, Issue 2, 2000, Pages 459-466

  Foo, Simon Y ^a  

^a FAMU FSU COLLEGE OF ENGINEERING   (United States)

Author keywords

Fire detection; Machine vision

Indexed keywords

FIRE DETECTORS; FLASHLIGHTS; HEURISTIC METHODS; HYDROCARBONS; IMAGE ANALYSIS; STATISTICAL METHODS;

AIRCRAFT DRY BAYS; HYDROCARBON FIRES;

COMPUTER VISION;

EID: 0033893936     PISSN: 00939994     EISSN: None     Source Type: Journal    
DOI: 10.1109/28.833762     Document Type: Article

References (7)

1. Machine vision fire detector for millisecond response, Photonics Spectra, pp. 18-20, Oct. 1993.
2. Payday for machine vision, Photonics Spectra, pp. 76-84, Mar. 1994.
3. R. C. Gonzalez and R. E. Woods, Digital Image Processing. Reading, MA: Addison-Wesley, 1992.
4. A. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall, 1989.
5. S. Y. Foo, A machine vision approach to detect and categorize fires in aircraft dry bays and engine compartments, in Conf. Rec. IEEE-IAS th Anna. Meeting, Lake Buena Vista, FL, Oct. 1995, pp. 1557-1564.
6. J. W. Barnes, Statistical Analysis for Engineers and Scientists. New York: McGraw-Hill, 1994.
7. Matlab User's Guide, The Math Works Inc., Natick, MA, 1999.