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Seed-based d mapping (formerly Signed differential mapping) or SDM is a statistical technique created by Joaquim Radua for meta-analysis studies assessing differences in brain activity or structure via neuroimaging techniques such as fMRI, VBM, DTI or PET. It may also refer to a specific piece of software created by the SDM Project to carry out ...
The Seed Herbarium Image Project (SHIP), [1] is an initiative of the Arnold Arboretum of Harvard University to create a web-based repository of high-resolution digital images documenting the morphology of woody plant seeds and selected fruit structures. Headquartered at the Arboretum’s Dana Greenhouse facility and coordinated and photographed ...
Region growing is a simple region-based image segmentation method. It is also classified as a pixel-based image segmentation method since it involves the selection of initial seed points. This approach to segmentation examines neighboring pixels of initial seed points and determines whether the pixel neighbors should be added to the region.
The vegetative structures of vascular plants includes the study of the shoot system, composed of stems and leaves, as well as the root system. The reproductive structures are more varied, and are usually specific to a particular group of plants, such as flowers and seeds, fern sori, and moss capsules.
Aerial photographs can be used to identify different rock types on the rock exposure and the arrangement of the geological structures. [1] [5] These structures are produced by deformation processes during tectonic movement, such as faults and folds. Geologists identify different geological features and structures based on photo-lineaments.
The left image shows a colour image, while the one in the right shows a grayscale image. The difference in the accuracy of classifying the geological structure between colour images and grayscale images is little. Exposed geological structures such as anticlines, ripple marks, and xenoliths can be identified automatically with deep learning ...
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The use of stable image structures over scales has been furthered by Ahuja [74] [75] and his co-workers into a fully automated system. A fully automatic brain segmentation algorithm based on closely related ideas of multi-scale watersheds has been presented by Undeman and Lindeberg [ 76 ] and been extensively tested in brain databases.