Paper Info
Title
Optimizing Optics and Imaging for Pattern Recognition Based Screening Tasks
Abstract
We present a method for simulating lower quality images starting from higher quality ones, based on acquired image pairs from different optical setups. The method does not require estimates of point (or line) spread functions of the system, but utilizes the relative transfer function derived from images of real specimen of interest in the observed application. Thanks to the use of a larger number of real specimen, excellent stability and robustness of the method is achieved. The intended use is exploring the influence of image quality on features and classification accuracy in pattern recognition based screening tasks. Visual evaluation of the obtained images strongly confirms usefulness of the method. The approach is quantitatively evaluated by observing stability of feature values, proven useful for PAP-smear classification, between synthetic and real images from seven different microscope setups. The evaluation shows that features from the synthetically generated lower resolution images are as similar to features from real images at that resolution, as features from two different images of the same specimen, taken at the same low resolution, are to each other.
Year
DOI
Venue
2014
10.1109/ICPR.2014.572
Pattern Recognition
Keywords
Field
DocType
feature extraction,image classification,image resolution,PAP-smear classification,feature values stability,image quality,lower quality image simulation,pattern recognition based screening tasks,synthetically generated lower resolution images,visual evaluation
Computer vision,Pattern recognition,Computer science,Image processing,Image quality,Robustness (computer science),Relative transfer function,Sub-pixel resolution,Microscope,Artificial intelligence,Real image,Image resolution
Conference
ISSN
Citations 
PageRank 
1051-4651
1
0.39
References 
Authors
5
4
Name
Order
Citations
PageRank
J. Lindblad1181.51
Natasa Sladoje220426.16
Malm, P.350.92
ewert bengtsson413525.36