2DKD: a toolkit for content-based local image search.

ABSTRACT:

Background

Direct comparison of 2D images is computationally inefficient due to the need for translation, rotation, and scaling of the images to evaluate their similarity. In many biological applications, such as digital pathology and cryo-EM, often identifying specific local regions of images is of particular interest. Therefore, finding invariant descriptors that can efficiently retrieve local image patches or subimages becomes necessary.

Results

We present a software package called Two-Dimensional Krawtchouk Descriptors that allows to perform local subimage search in 2D images. The new toolkit uses only a small number of invariant descriptors per image for efficient local image retrieval. This enables querying an image and comparing similar patterns locally across a poten

SUBMITTER: DeVille JS 

PROVIDER: S-EPMC7011505 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature