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ZFIQ (Zebrafish Image Quantitator) (Liu et al., 2008) is another toolkit, which provides a set of image analysis tools for quantitative, reproducible and accurate interpretation of zebrafish imaging data.
Such a process starts from a Training Phase that creates a set of image filter sequences.
The virtual sensor is trained on a diverse set of image data, using features extracted from grey level images.
This article presents stress orientation data derived from an extensive set of image logs throughout the Norwegian continental shelf.
Our method is based on an HS defined on the set of image pixels.
A set of image sequences from multiple views are applied to evaluate performance.
Hence each dataset (or set of image sequences) was only initialized once.
The next set of image descriptors uses the segmentation-based fractal texture analysis (SFTA) [18].
The first step in the modeling process deals with the selection of an accurate set of image metrics that are used as the input data of the model.
For this purpose, scanning electron microscope (SEM) images of failure section were captured at backscattered electron mode (BEC) and analyzed via a consecutive set of image processing algorithms.
Using a recently introduced cell cycle marker, we present a set of image analysis tools for automated cell phase analysis of live cells over extended time periods.
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