Instance Segmentation
Image segmentation
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s). When applied to a stack of images, typical in medical imaging, the resulting contours after image segmentation can be used to create 3D reconstructions with the help of geometry reconstruction algorithms like marching cubes.
Read more about 'Image segmentation' at: WikipediaWikipedia contributors. "Image segmentation." Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, Dec. 21, 2024.
Other applications related to Instance Segmentation in Helmholtz Imaging CONNECT:
Instance Segmentation in Helmholtz Imaging CONNECT:
ObiWan-Microbi: OMERO-based integrated Workflow for annotating Microbes in the Cloud
ObiWan-Microbi is a toolkit collection for semi-automated segmentation and annotation of (biological) time-lapse image data in the cloud. It utilizes an OMERO image server for …
