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  4. Object detection proposals : evaluation of image processing algorithms for detecting airborne fungal spores in microscopic images
 
Zitierlink DOI
10.26092/elib/217

Object detection proposals : evaluation of image processing algorithms for detecting airborne fungal spores in microscopic images

Veröffentlichungsdatum
2018-11
Autoren
Shubbak, Mahmood  
Zusammenfassung
The present research project studies the application of software algorithms for detecting object proposals in digital images based on their graphical characteristics. The fast generation of accurate object proposals is widely seen as a promising solution to the efficiency problems of heavy classification and object recognition algorithms, which require long time and huge computational power when applied on entire images. Instead, object detection proposals (ODPs) with smaller sizes and focused content are an optimum alternative for such classification algorithms.
This report provides a systematic review and benchmarking of the state-of-the-art ODP algorithms. It further introduces a novel algorithm “Smart-Superpixels”, specifically developed for the purpose of airborne fungal spores’ detection in microscopic images. The benchmarking considers several qualitative and quantitative criteria regarding the algorithms speed, spatial efficiency, recall accuracy, localization precision and redundancy.
The benchmarking results show that the introduced algorithm: Smart-Superpixels has the highest overall performance with its fast operation, relatively high spatial efficiency, high recall accuracy and localization precision, as well as low redundancy.
Schlagwörter
Airborne Fungal Spores

; 

Algorithm

; 

Benchmark

; 

comparative analysis

; 

Computer

; 

Edge-Boxes

; 

MATLAB

; 

Microscope

; 

Microscopic Images

; 

Object Detection Proposals

; 

Objectness

; 

Selective-Search

; 

Smart-Superpixels
Institution
Universität Bremen  
Fachbereich
Fachbereich 01: Physik/Elektrotechnik (FB 01)  
Institute
Institut für Mikrosensoren, -aktoren und -systeme (IMSAS)  
Dokumenttyp
Buch, Monographie
Seitenzahl
52
Zweitveröffentlichung
Nein
Lizenz
http://creativecommons.org/licenses/by-nc-nd/3.0/de/
Sprache
Englisch
Dateien
Lade...
Vorschaubild
Name

Object Detection Proposals (1).pdf

Size

9.21 MB

Format

Adobe PDF

Checksum

(MD5):b23b217f2dface4d52962d6a431fcc01

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