Sentence examples for interest selection from inspiring English sources

Common approaches to facial expression recognition of static images are designed considering three main parts: (1) region of interest selection, (2) feature extraction, and (3) image classification.

In Tables 3, 4 and 5, performance results of our method with region of interest selection and comparison with the given method in [35] can be seen for dense, urban and sunny datasets, respectively.

It can be used to perform many standard tasks such as displaying detector images and waveforms, displaying data as histograms, strip charts, scatter plots, etc., and performing averaging, filtering, and other generic manipulations of the data including region of interest selection, masking, projections, integration, contrast calculation, and hit finding.

This algorithm is envisioned to be generally useful in XANES analysis of biological samples and any STXM system where external noise source contributes variations in the intensity of the incoming beam or where automated region of interest selection would be desired.

This region of interest selection was chosen because of the relatively close proximity of the two kinetochore clusters.

Reactions that were not labeled and reactions that were labeled but for other reasons were considered to be of potential interest (selection made by one pharmacist [LH] and one physician [EP]) were analyed on a case by case basis.

Figure 5 Point-of-interest selection.

The latter includes region-of-interest selection, 2D Fast Fourier Transform for range-Doppler map extraction, peak detection and alarm decision logic.

In this case the time increment is due, first, to the need to use more starting points for the algorithm in order to avoid false matches and secondly because the choice of these points is not sufficiently suitable (given the simplicity of the points-of-interest selection algorithm), which implies, in both cases, an additional number of iterations.

The areas of the white matter and of the IGL were each determined by user-assisted region-of-interest selection and thresholding.

Flow cytometry presents an advantageous application of this system, as it is significantly higher throughput and more unbiased than region-of-interest selection or cell segmentation in microscopy.