Image Annotation
Image Annotation - Related Videos
08:09
Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
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Cited by 8 •
2012
Combination of genomics, co-expression gene analysis and the identification of target compounds via metabolism give gene functional annotation.
03:36
Genome Annotation and Assembly
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2021
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
06:16
mirMachine: A One-Stop Shop for Plant miRNA Annotation
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Cited by 11 •
2021
Herein, we present a new and fully automated miRNA pipeline, mirMachine that 1) can identify known and novel miRNAs more accurately and 2) is fully automated and freely available. Users can now execute a short submission script to run the fully automated mirMachine pipeline.
10:23
Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
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Cited by 2 •
2023
Presented here is a detailed protocol for the conduction of three-dimensional cephalometric analysis with the use of human cone beam computed tomography scans.
11:41
Rapid Analysis and Exploration of Fluorescence Microscopy Images
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Cited by 3 •
2014
Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.
FAQs
The labels connect visual patterns with known objects, regions, or environmental features. During model development, these examples provide the reference information needed for a system to learn from imagery rather than treating every pixel or scene as undifferentiated data. The same labeled examples can also support evaluation by allowing predicted features to be compared with established annotations.
Each format represents visual information at a different level of spatial detail. Bounding boxes identify an object’s surrounding area, points mark specific locations, polygons outline regions, and pixel-level masks provide detailed boundaries across an image. Choosing among them affects how precisely objects, habitats, land-cover areas, or other environmental features are represented in the resulting dataset.
Consistent labels make examples comparable across photographs, satellite scenes, and other visual data. When similar objects or regions receive structured treatment, the dataset can support more reproducible spatial analysis and provide a clearer reference for machine-learning evaluation. In environmental studies, this consistency is especially relevant when assessments span large areas or multiple kinds of imagery.
Comparison with annotations shows how closely a system’s predictions correspond to identified objects, regions, and environmental features. This creates a basis for assessing whether visual patterns were interpreted appropriately rather than relying only on the model’s output. The resulting evidence can inform applications such as land-cover mapping, habitat monitoring, and detection of environmental change.
A typical workflow begins with selecting photographs, satellite scenes, or other visual data relevant to the environmental question. Annotators then identify the target objects, regions, or features and mark them with suitable boxes, polygons, points, or masks. The structured labels are assembled into a dataset that can support machine-learning development and prediction evaluation.
It is useful when researchers need visual data organized for spatial analysis at scale. Annotated imagery can support land-cover mapping, habitat and species monitoring, crop and forest assessment, and detection of changes such as deforestation or water pollution. These labels help turn varied visual scenes into structured evidence that can be analyzed more quickly and reproducibly.