Persistent Link:
http://hdl.handle.net/10150/243970
Title:
Object Recognition and Classification
Author:
Johnson, Taylor Christine
Issue Date:
May-2012
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Object recognition and classification is a common problem facing computers. There are many shortcomings in proper identification of an object when it comes to computer algorithms. A very common process used to deal with classification problems is neural networks. Neural networks are modelled after the human brain and the neuron _rings that occur when an individual looks at an image and identifies the objects in it. In this work we propose a probabilistic neural network that takes into account the regional properties of an image of either an ant or an egg as determined by edge segmentation and an extraction of geometric features specific to the object. To do this the algorithm calculates the regional properties of a black and white representation of the object and then gives these properties to the probabilistic neural network which calculates the probability of the object being an ant or an egg.
Type:
text; Electronic Thesis
Degree Name:
B.S.
Degree Level:
bachelors
Degree Program:
Honors College; Mathematics
Degree Grantor:
University of Arizona

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleObject Recognition and Classificationen_US
dc.creatorJohnson, Taylor Christineen_US
dc.contributor.authorJohnson, Taylor Christineen_US
dc.date.issued2012-05-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractObject recognition and classification is a common problem facing computers. There are many shortcomings in proper identification of an object when it comes to computer algorithms. A very common process used to deal with classification problems is neural networks. Neural networks are modelled after the human brain and the neuron _rings that occur when an individual looks at an image and identifies the objects in it. In this work we propose a probabilistic neural network that takes into account the regional properties of an image of either an ant or an egg as determined by edge segmentation and an extraction of geometric features specific to the object. To do this the algorithm calculates the regional properties of a black and white representation of the object and then gives these properties to the probabilistic neural network which calculates the probability of the object being an ant or an egg.en_US
dc.typetexten_US
dc.typeElectronic Thesisen_US
thesis.degree.nameB.S.en_US
thesis.degree.levelbachelorsen_US
thesis.degree.disciplineHonors Collegeen_US
thesis.degree.disciplineMathematicsen_US
thesis.degree.grantorUniversity of Arizonaen_US
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