東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Intelligent vision system for the de...

O'Brien, John G., III.

Linked to FindBook

Google Book

Amazon

博客來

Intelligent vision system for the detection of protozoa on microscope slides.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Intelligent vision system for the detection of protozoa on microscope slides./
Author:	O'Brien, John G., III.
Description:	306 p.
Notes:	Source: Dissertation Abstracts International, Volume: 63-11, Section: B, page: 5355.
Contained By:	Dissertation Abstracts International63-11B.
Subject:	Engineering, Agricultural. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3070035
ISBN:	9780493899787

Intelligent vision system for the detection of protozoa on microscope slides.
O'Brien, John G., III.

Intelligent vision system for the detection of protozoa on microscope slides. - 306 p.

Source: Dissertation Abstracts International, Volume: 63-11, Section: B, page: 5355.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2002.

A machine vision system for microscope slide analysis was developed. The apparatus was configured in a manner that enabled computer control of some tasks traditionally performed by a microscopist. Each sample slide was scanned for analysis utilizing developed software control routines. The system discretized each sample slide into a number of sub regions, which were approximately the size of the camera's field of view. Each region was saved and analyzed independently. Investigations into the physical characteristics of prepared water samples containing protozoa yielded information for the intelligent automation of the physical equipment.

ISBN: 9780493899787Subjects--Topical Terms:

1019504
Engineering, Agricultural.

Intelligent vision system for the detection of protozoa on microscope slides.
LDR:03080nam a2200301 4500 001 1968544
005 20141203122253.5
008 150210s2002 ||||||||||||||||| ||eng d
020 $a 9780493899787
035 $a (MiAaPQ)AAI3070035
035 $a AAI3070035
040 $a MiAaPQ $c MiAaPQ
100 1 $a O'Brien, John G., III. $3 2105723
245 1 0 $a Intelligent vision system for the detection of protozoa on microscope slides.
300 $a 306 p.
500 $a Source: Dissertation Abstracts International, Volume: 63-11, Section: B, page: 5355.
500 $a Adviser: John F. Reid.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2002.
520 $a A machine vision system for microscope slide analysis was developed. The apparatus was configured in a manner that enabled computer control of some tasks traditionally performed by a microscopist. Each sample slide was scanned for analysis utilizing developed software control routines. The system discretized each sample slide into a number of sub regions, which were approximately the size of the camera's field of view. Each region was saved and analyzed independently. Investigations into the physical characteristics of prepared water samples containing protozoa yielded information for the intelligent automation of the physical equipment.
520 $a This study involved the development of a system, the characterization of its performance, and an evaluation of its suitability for presumptive and confirmed classification of Giardia and Cryptosporidium on microscope slides. Occurrence and subsequent detection of these protozoa is generally a rate event. Measurements were incorporated into a neural network discrimination scheme that performed a presumptive identification of protozoa on the microscope slide. Regions testing positive for the presumptive test were subjected to a confirmation procedure that tested for the presence of internal structures. Presumptive classification performance using a neural network algorithm was evaluated. Results indicated 100 percent of cysts/oocysts present were appropriately classified. The system developed in this research provides a useful test-bed for future investigations into biological samples on microscope slides.
520 $a 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.
590 $a School code: 0090.
650 4 $a Engineering, Agricultural. $3 1019504
650 4 $a Engineering, Environmental. $3 783782
690 $a 0539
690 $a 0775
710 2 $a University of Illinois at Urbana-Champaign. $3 626646
773 0 $t Dissertation Abstracts International $g 63-11B.
790 $a 0090
791 $a Ph.D.
792 $a 2002
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3070035