Object Recognition and Classification
| dc.contributor.author | Johnson, Taylor Christine | |
| dc.creator | Johnson, Taylor Christine | en_US |
| dc.date.accessioned | 2012-09-13T23:29:30Z | |
| dc.date.available | 2012-09-13T23:29:30Z | |
| dc.date.issued | 2012-05 | |
| dc.identifier.citation | Johnson, Taylor Christine. (2012). Object Recognition and Classification (Bachelor's thesis, University of Arizona, Tucson, USA). | |
| dc.identifier.uri | http://hdl.handle.net/10150/243970 | |
| dc.description.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. | |
| dc.language.iso | en | en_US |
| dc.publisher | The University of Arizona. | en_US |
| dc.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. | en_US |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
| dc.title | Object Recognition and Classification | en_US |
| dc.type | text | en_US |
| dc.type | Electronic Thesis | en_US |
| thesis.degree.grantor | University of Arizona | en_US |
| thesis.degree.level | bachelors | en_US |
| thesis.degree.discipline | Honors College | en_US |
| thesis.degree.discipline | Mathematics | en_US |
| thesis.degree.name | B.S. | en_US |
| dc.description.admin-note | Removed permission form from PDF and replaced file June 2023 | |
| refterms.dateFOA | 2018-09-04T00:16:46Z | |
| html.description.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. |
