@inproceedings{e83875f926cf480d93df344666b53e23,
title = "The Boru data crawler for object detection tasks in machine vision",
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.",
author = "Daniel Howard and Roberts, {Simon C.} and Conor Ryan",
year = "2002",
doi = "10.1007/3-540-46004-7_23",
language = "English",
isbn = "3540434321",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "222--232",
editor = "Stefano Cagnoni",
booktitle = "Applications of Evolutionary Computing - EvoWorkshops 2002",
note = "EvoWorkshops 2002: 2nd European Workshop on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2002, the 4th European Workshop on Evolutionary Computation in Image Analysis and Signal Processing, EvoIASP 2002, and the 3rd EvoSTIM/EvoPLAN ; Conference date: 03-04-2002 Through 04-04-2002",
}