A Gaussian mixture-based approach to synthesizing nonlinear feature functions for automated object detection
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @PhdThesis{PeiFang_Guo:thesis,
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author = "Pei Fang Guo",
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title = "A Gaussian mixture-based approach to synthesizing
nonlinear feature functions for automated object
detection",
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school = "Electrical and Computer Engineering, Concordia
University",
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year = "2010",
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address = "Canada",
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month = aug,
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keywords = "genetic algorithms, genetic programming",
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URL = "http://spectrum.library.concordia.ca/979537/",
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URL = "http://spectrum.library.concordia.ca/979537/1/NR67351.pdf",
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URL = "https://www.genealogy.math.ndsu.nodak.edu/id.php?id=147057",
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size = "92 pages",
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abstract = "Feature design is an important part to identify
objects of interest into a known number of categories
or classes in object detection. Based on the
depth-first search for higher order feature functions,
the technique of automated feature synthesis is
generally considered to be a process of creating more
effective features from raw feature data during the run
of the algorithms. This dynamic synthesis of nonlinear
feature functions is a challenging problem in object
detection. This thesis presents a combinatorial
approach of genetic programming and the expectation
maximization algorithm (GP-EM) to synthesize nonlinear
feature functions automatically in order to solve the
given tasks of object detection. The EM algorithm
investigates the use of Gaussian mixture which is able
to model the behaviour of the training samples during
an optimal GP search strategy. Based on the Gaussian
probability assumption, the GP-EM method is capable of
performing simultaneously dynamic feature synthesis and
model-based generalization. The EM part of the approach
leads to the application of the maximum likelihood (ML)
operation that provides protection against
inter-cluster data separation and thus exhibits
improved convergence. Additionally, with the GP-EM
method, an innovative technique, called the histogram
region of interest by thresholds (HROIBT), is
introduced for diagnosing protein conformation defects
(PCD) from microscopic imagery. The experimental
results show that the proposed approach improves the
detection accuracy and efficiency of pattern object
discovery, as compared to single GP-based feature
synthesis methods and also a number of other object
detection systems. The GP-EM method projects the
hyperspace of the raw data onto lower-dimensional
spaces efficiently, resulting in faster computational
classification processes.",
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notes = "Supervisors: Prabir Bhattacharya and Nawwaf Kharma
ID Code: 979537",
- }
Genetic Programming entries for
Pei Fang Guo
Citations