"subset of images" is a correct and usable phrase in written English.
You could use it when discussing a collection of images that are a part of a larger set. For example, "We extracted a subset of images from our larger collection for further analysis."
Select a subset of images to mosaic using the "#" text button or keyboard stroke.
Instead, Goodfellow and co say it is acceptable to spot 98percentt of the numbers in a certain subset of images, which in this case turn out to cover around 95percentt of the total.
In this paper, we propose a more natural approach, which dynamically determines a subset of images that best describes the complete image data in the space of all previously seen images.
As demonstrated in the paper, either of two requirements must be met: (1) the phenotype of interest must be recognizable by eye or quantifiable/classifiable by image analysis, given the existing label-free images, or (2) the phenotype must be recognizable by eye or quantifiable/classifiable by image analysis in a separately stained subset of images prepared at the same time.
Such re-ranking methods are commonly used only for a subset of images because they are often computationally expensive and significantly increase the retrieval time.
The remaining subset of images are subjected to non-local means denoising, then feature-matched against the first reference LR image.
The gMAD competition can automatically select a subset of image pairs from the database, which provides the competition ranking and reveals the relative performance of the IQA models.
On the other hand, a considerable subset of image quality parameters which cultural heritage organizations require for quality assessment in mass digitization workflows are measured by technologies described in this article.
An alternative to coding and remembering the holistic stimulus pattern as a reference for contrast is that the visual system might only code a subset of image elements in memory.
Identification of homogeneous subsets of images in a macromolecular electron microscopy (EM) image data set is a critical step in single-particle analysis.
Here we describe an approach, iterative stable alignment and clustering (ISAC) that, relying on a new clustering method and on the concepts of stability and reproducibility, can extract validated, homogeneous subsets of images.
