Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Pattern Recognition with Neural Network in C++
Pattern Recognition with Neural Network in C++
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Learning-based hand sign recognition using SHOSLIF-M
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Journal of Cognitive Neuroscience
A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections
IEEE Transactions on Information Technology in Biomedicine
Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
Spermatogonium image recognition using Zernike moments
Computer Methods and Programs in Biomedicine
Multiclass detection of cells in multicontrast composite images
Computers in Biology and Medicine
Adaptive filtering and hypothesis testing: Application to cancerous cells detection
Pattern Recognition Letters
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Automatic cell recognition in bright field microscopy is an inherently difficult task due to the immense variability of cell appearance. Yet, it is essential for a high-throughput robotic system. In this paper, we employed a pixel patch decomposition method to detect cultured cells in bright field images. To increase the classification accuracy, we proposed a novel Fisher's Linear Discriminant (FLD) preprocessing approach. This technique was applied to various experimental scenarios utilizing different imaging environments and the results were compared with those for the traditional Principal Component Analysis (PCA) preprocessing. Our FLD preprocessing was shown to be more effective than PCA primarily owing to its ability to maximize the ratio of between-class scatter to within-class scatter. The optimized algorithm has sufficient accuracy and speed for practical use in robotic systems capable of automatic micromanipulation of single cells.