- author = "Christopher Harris",
- title = "An investigation into the Application of Genetic Programming techniques to Signal Analysis and Feature Detection",
- school = "University College, London",
- year = "1997",
- address = "UK",
- month = "26 " # sep,
- keywords = "genetic algorithms, genetic programming, Canny Edge Detector, soft brood selection, Demes, steady state GA, memory, STGP, evolving data structures, basis functions, type systems in constraining search, Turing completeness, recursion and iteration, Building Block Hypothesis in GP, GP-COM",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/c.harris/thesisps.zip",
-
URL = "http://ethos.bl.uk/OrderDetails.do?did=33&uin=uk.bl.ethos.286626",
-
URL = "https://discovery.ucl.ac.uk/id/eprint/10104563/",
- size = "186 pages",
-
abstract = "This thesis investigates the use of the Genetic
Programming (GP) machine learning paradigm to computer
vision problem domains related to the detection of
features within discrete data signals. The use of GP to
find a solution to such problems involves two pieces of
design. The first is the set of elements that the
search process uses to construct possible problem
solutions, known as the primitive set. The second is
the design of an appropriate reward mechanism for
candidate solutions, known as the fitness function. The
wide range of signal types and features to be detected
in computer vision detection problems ensures that
representation of both data and solutions is of prime
importance.
Two contributions made by this thesis are to provide approaches to both primitive set design and fitness function design that are generic to feature detection problems. A three-component fitness function design is presented which reflects the desirable properties of a generic feature detector. Work is also presented on a design for a primitive set that is applicable to a wide range of signal processing problems. These techniques are explored using the classic problems of edge detection and template matching as experimental test-beds.
Work in this thesis on edge detection produces filter functions that outperform those produced by human experts under real-world conditions. This process can be used to produce edge detectors optimised for specific sets of data.
Working from the basis that Strongly Typed Genetic Programming (STGP) is essential for solving vision problems, a fourth contribution is the adoption of STGP as a general syntactic constraint mechanism for the production of GP program trees. This provides a structuring mechanism in addition to allowing the use of complex and relevant data types within candidate programs. By using the structuring to enforce a hierarchy of abstraction in terms of data types and representations, we can increase the power of GP to solve hard problems, and allow more intelligent use of the limited search power available with finite computational resources. This is a form of abstraction not previously used in GP.",
-
notes = "uk.bl.ethos.286626
UCL internal use:000902299
https://discovery.ucl.ac.uk/id/eprint/10104563/ digitised by ProQuest.",
