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Image processing and pattern recognition

TLDR
Alpaydin and Gurgen, Comparison of Statistical and Neural Classifiers and their Applications to Optical Character Recognition and Speech Classification and Chen and Chang, Learning Algorithms and Applications of Principal Component Analysis.
Abstract
Lampinen, Pattern Recognition. Alpaydin and Gurgen, Comparison of Statistical and Neural Classifiers and their Applications to Optical Character Recognition and Speech Classification. Sun and Nekovei, MedicalImaging. Takeda and Omatu, Paper Currency Recognition. Cordella and Stefano, Neural Network Classification Reliability: Problems and Applications. Yagi, Kobayaski, and Matsumoto, Parallel Analog Image Processing: Solving Regularization Problems with Architecture Inspired by the Vertebrate Retinal Circuit. Setiono, Algorithmic Techniques and their Applications. Chen and Chang, Learning Algorithms and Applications of Principal Component Analysis. Merat and Villalobos, Learning Evaluation and Pruning Techniques.

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Citations
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Journal ArticleDOI

Neural network-based colonoscopic diagnosis using on-line learning and differential evolution

TL;DR: Results in interpreting colonoscopy images and frames of video sequences are promising and suggest that networks trained with this strategy detect malignant regions of interest with accuracy.
Journal ArticleDOI

Sensorimotor Modulation Assessment and Brain-Computer Interface Training in Disorders of Consciousness

TL;DR: Electroencephalogram-based assessment showed that patients in a minimally conscious state may have the capacity to operate a simple BCI-based communication system, even without any detectable volitional control of movement.
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Application of Morphological Segmentation to Leaking Defect Detection in Sewer Pipelines

TL;DR: A novel method of computer vision, morphological segmentation based on edge detection (MSED), is proposed to assist inspectors in detecting pipeline defects in CCTV inspection images to assist in detecting leakage defects in sewer pipelines.
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Syntactic reasoning and pattern recognition for analysis of coronary artery images

TL;DR: A new approach to the application of structural pattern recognition methods for image understanding, based on content analysis and knowledge discovery performed on medical images, is presented in particular computer analysis and recognition of local stenoses of the coronary arteries lumen.
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Prediction of corporate financial health by Artificial Neural Network

TL;DR: This paper checks out the classification capability of Radial Basis Function Networks, Multi-Layer Perceptrons, MLPs with and without Principal Component Analysis (PCA), Self-Organizing Feature Maps (SOFM) with MLP and Support Vector Machine (SVM) neural architecture for prediction of the financial health of firms.