Intelligent Vision System for the Detection of Protozoa on Microscope Slides

Three approaches were evaluated for suitability in the confirmation process. These methods were then checked for correspondence between the results indicated by an expert human observer. Most texture measurements alone were not found to be useful. A heuristic method, was computationally more efficient and performed with an 80 percent accuracy. Using a neural network classifier, performance ranged from 50 to 100 percent depending on the parameters tested. Overall, the correspondence between the system and expert suggested a strong relationship to classifications of unknown objects.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mechanical Science and Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• O'Brien, John G., III

Contributors dc:contributor

• Reid, John F.

• Engineering, Environmental

Language dc:language

eng

Identifier

(MiAaPQ)AAI3070035

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/83777