Genetic Programming for Multiple Class Object Detection
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{zhang:1999:GPmcod,
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author = "Mengjie Zhang and Victor Ciesielski",
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title = "Genetic Programming for Multiple Class Object
Detection",
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booktitle = "12th Australian Joint Conference on Artificial
Intelligence",
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year = "1999",
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editor = "Norman Foo",
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volume = "1747",
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series = "LNAI",
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pages = "180--192",
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address = "Sydney, Australia",
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publisher_address = "Berlin",
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month = "6-10 " # dec,
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publisher = "Springer-Verlag",
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keywords = "genetic algorithms, genetic programming, Machine
learning, Neural networks, Vision",
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ISBN = "3-540-66822-5",
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URL = "http://www.springer.com/computer/ai/book/978-3-540-66822-0",
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size = "13 pages",
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abstract = "We describe an approach to the use of genetic
programming for object detection problems in which the
locations of small objects of multiple classes in large
pictures must be found. The evolved programs use a
feature set computed from a square input field large
enough to contain each of objects of interest and are
applied, in moving window fashion, over the large
pictures in order to locate the objects of interest.
The fitness function is based on the detection rate and
the false alarm rate. We have tested the method on
three object detection problems of increasing
difficulty with four different classes of interest. On
pictures of easy and medium difficulty all objects are
detected with no false alarms. On difficult pictures
there are still significant numbers of errors, however
the results are considerably better than those of a
neural network based program for the same problems.",
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notes = "http://www.cse.unsw.edu.au/~ai99/",
- }
Genetic Programming entries for
Mengjie Zhang
Victor Ciesielski
Citations