We have imaged individual flagellar filaments of Escherichia coli, a motile Streptococcus sp., and Rhizobium meliloti by video-enhanced differential interference-contrast microscopy (Nomarski DIC) and computer-based image processing.
However, modern computer-based image-processing tools can aid the quantification process by reducing the vast amount of information contained in visual images to a limited amount of quantitative data relevant for a particular diagnosis.
The aim of this paper is the development of a new technological solution, for the automatic characterization of the yarn mass parameters (linear mass, diameter, and hairiness) based on image processing and computer vision techniques.
A computer-based digital image processing system was employed for quantitative analysis of the parameters.
Our study involved 9 wards from 3 hospitals and was based on Hand-in-Scan MemploysTrainer digitalthat employs dimagingimandng and computer image processing to identify all missed areas on the hand, and display them unambiguously to the healthcare worker.
In this paper we review the algorithm development and applications in high resolution shock capturing methods, level set methods, and PDE based methods in computer vision and image processing.
These include CAD, CAM, Geometric Modelling, Robotics, Computer Vision and Image Processing, Computer Graphics and Scientific Visualization.
Major topics include mathematical theory of pattern recognition, raw data representation, computer vision, image processing, machine learning, computer graphics, data and knowledge bases, neural nets, software, specialized computer architectures, applications, and related areas.
