Automated pollen identification using microscopic imaging and texture analysis

Automated pollen identification using microscopic imaging and texture analysis.

Pollen identification is required in different scenarios such as prevention of allergic reactions, climate analysis or apiculture. However, it is a time-consuming task since experts are required to recognize each pollen grain through the microscope. In this study, we performed an exhaustive assessment on the utility of texture analysis for automated characterisation of pollen samples. A databas...

Pollen texture identification using neural networks

The importance of research leading to the automation of pollen identification is briefly outlined. A new technique, neural network analysis, is briefly introduced, and then applied to the determination of light microscope images of pollen grains. The results are compared with some previously published statistical classifiers. Although both types of classifiers may work, the neural network is ap...

Writer Identification Using Texture Analysis

In this paper, we describe a new method to identify the writer of handwritten documents. There are many methods for signature verification or writer identification, but most of them require segmentation or connected component analysis. They are the kinds of content dependent identification methods as signature verification requires the writer to write the same text (e.g. his name). In our new m...

Automated human identification using ear imaging

This paper investigates a new approach for the automated human identification using 2D ear imaging. We present a completely automated approach for the robust segmentation of curved region of interest using morphological operators and Fourier descriptors. We also investigate new feature extraction approach for ear identification using localized orientation information and also examine local gray...

Identification and analysis of pollen