- author = "Daniel Howard and Simon C. Roberts and Conor Ryan",
- title = "The Boru Data Crawler for Object Detection Tasks in Machine Vision",
- booktitle = "Applications of Evolutionary Computing, Proceedings of EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN",
- year = "2002",
- editor = "Stefano Cagnoni and Jens Gottlieb and Emma Hart and Martin Middendorf and G{"}unther Raidl",
- volume = "2279",
- series = "LNCS",
- pages = "222--232",
- address = "Kinsale, Ireland",
- publisher_address = "Berlin",
- month = "3-4 " # apr,
- organisation = "EvoNet",
- publisher = "Springer-Verlag",
- keywords = "genetic algorithms, genetic programming, evolutionary computation, applications",
- ISBN = "3-540-43432-1",
-
DOI = "
10.1007/3-540-46004-7_23",
- size = "11 pages",
- abstract = "A 'data crawler' is allowed to meander around an image deciding what it considers to be interesting and laying down flags in areas where its interest has been aroused. These flags can be analysed statistically as if the image was being viewed from afar to achieve object recognition. The guidance program for the crawler, the program which excites it to deposit a flag and how the flags are combined statistically, are driven by an evolutionary process which has as objective the minimisation of misses and false alarms. The crawler is represented by a tree-based Genetic Programming (GP) method with fixed architecture Automatically Defined Functions (ADFs). The crawler was used as a post-processor to the object detection obtained by a Staged GP method, and it managed to appreciably reduce the number of false alarms on a real-world application of vehicle detection in infrared imagery.",
-
notes = "EvoWorkshops2002, part of cagnoni:2002:ews
READMEM WRITEMEM working memory. Mark decisions branch. Flags. Second results branch. Looking for cars
",
