Sentence examples for relative images from inspiring English sources

TL-based classification methods are designed to achieve high classification accuracy with relatively small number of labeled samples from the new image (target domain) by efficient reuse of the training data from the previous different but relative images (source domain) [5 7].

The first step is to merge and scale these various images, that is, to identify which peaks appear in two or more images (merging) and to scale the relative images so that they have a consistent intensity scale.

Experimental results using synthetic and real images with known and unknown distortion conditions show the robustness and feasibility of the proposed metric in providing relative image sharpness.

In this subset, the percent differences in bone density and micro-architecture measures tended to increase as the relative image quality decreased.

This study was designed to assess relative image quality, interpretive confidence, and diagnostic accuracy by use of contemporary technology and protocols.

It was reported that the relative image contrast of chemically different regions depended sensitively on rsp and probe tips [1, 14, 24, 25].

The previous representation characterizes the patches local appearance, but we can also incorporate cues relating to the typical absolute or relative image positions of the various content classes.

In the absence of absolute quantitative SPECT characterization, relative image intensity ratios were constructed to quantify both magnitude and volumetric extent of liver function.

In the feature matching tasks which form an integral part of visual tracking or SLAM (Simultaneous Localisation And Mapping), there are invariably priors available on the absolute and/or relative image locations of features of interest.

Based on the pretreatment of these visible images, such as smoothing, denoising, image color space and frequency domain transformation, the relative image fractal eigenvalues were extracted by using a box-counting dimension method, and then the linear regression model and FCM clustering algorithm were developed.

Images were processed using ImageJ and Adobe Photoshop, preserving relative image intensities within a series.