Sentence examples for many segmentation methods from inspiring English sources

However, many segmentation methods are very sensitive to spurious intensity variations.

Among many segmentation methods, k-means clustering is considered as one of the most popular techniques.

Many segmentation methods rely on grouping pixels with similar color into the same object, often using algorithms developed for the segmentation of still images.

These two issues present problems for many segmentation methods, and designing methods to deal with them is an active area of research in video processing.

And there are many segmentation methods using RGB color space, for example, license location [16] gets the license plate area accurately by calculating the contrast in the RGB components, reducing the calculated amount.

The challenges in segmentation include weak boundaries between close-lying axons and large intensity variations in some axons, which cause many segmentation methods to fail.

Secondly, it considers the area bounded by the lower boundary being optimized, unlike many other segmentation methods that only try to track or in other ways segment the boundary itself without any knowledge about the area that that boundary defines.

In the past, there have proposed many segmentation evaluation methods, however, segmentation evaluation is still an open topic [ 25, 26].

Many traditional segmentation methods have been improved to solve co-segmentation method, such as the Markov random field (MRF -based segMRF -basedmethodsegmentationdomethodsr-based segmentation method [5 8] and discrandomtive clustering-based segmentation method [10, 19].

Many image segmentation methods have been proposed, which includes histogram based methods, edge detection based methods, region based methods, active contour model based methods, etc. Active contour model based methods have drawn a lot of attention in the past decade because of their significant advantages.

Many tooth segmentation methods prefer to use surface curvature when identifying potential tooth boundaries, as they follow the most widely cited mesh segmentation criterion, the minima rule, which states that human perception usually divides a surface into parts along the concave discontinuity of the tangent plane [ 3, 4].