Spatial Feature Extraction
Spatial Feature Extraction - Related Videos
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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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This article introduces a protocol for using DeepSpaceDB, a dynamic, interactive database for spatial transcriptomics, offering analysis workflows and examples to explore tissue organization and disease-related gene expression.
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Spatial Molecular Imaging of the Glycome Using Mass Spectrometry
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Cited by 1 •
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This protocol details the key steps to enable rigorous and reproducible measurement of both glycogen and N-linked glycans using mass spectrometry imaging.
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Spatial Cueing
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University Attention refers to the limited human ability to select some information for processing at the expense of other stimuli in the environment. Attention operates in all sensory modalities: vision, hearing, touch, even taste and smell. It is most often studied in the visual domain though. A common way to study visual attention is with a spatial cueing paradigm. This paradigm allows researchers to measure the consequences of focusing...
10:37
Spatial Separation of Molecular Conformers and Clusters
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Cited by 3 •
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We present a technique that allows the spatial separation of different conformers or clusters present in a molecular beam. An electrostatic deflector is used to separate species by their mass-to-dipole moment ratio, leading to the production of gas-phase ensembles of a single conformer or cluster stoichiometry.
05:42
Visual Search for Features and Conjunctions
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University How do people find objects in cluttered visual scenes? Think, for example, of looking for keys on a messy desk, finding the ripest-looking fruit at the grocery store, locating your car when you can’t quite remember where you parked it, or finding an old friend at an airport exit gate. Clearly, an understanding of visual perception is going to play a role in any answers, and more specifically, an understanding of visual attention...
FAQs
Geometric relationships capture more than an individual point, edge, or landmark. They describe how locations, shapes, and neighboring elements are arranged, allowing an algorithm to distinguish patterns that may look similar when considered separately. In engineering systems, these relationships can make descriptors more useful for comparing objects, recognizing structures, or evaluating whether measured geometry matches an expected arrangement.
Different objectives require different forms of information. Edge and landmark descriptors can emphasize identifiable boundaries or reference locations, while geometric measurements can represent shape and size. Local neighborhoods and coordinate relationships instead describe how nearby observations fit together. Selecting among these representations determines which patterns remain visible to later comparison, classification, inspection, or navigation algorithms.
The available information is shaped by the data source and by the patterns that matter to the task. Images, point clouds, maps, and sensor measurements can expose different combinations of location, shape, arrangement, and neighborhood relationships. An engineering method therefore needs to focus on the spatial characteristics relevant to its intended decision rather than treating every measurement as equally informative.
A typical workflow begins with raw spatial observations, such as an image, point cloud, map, or sensor measurement. The method then identifies informative patterns, measures geometric properties or encodes spatial relationships, and produces descriptors that algorithms can compare or classify. Those descriptors can subsequently support a downstream engineering decision, including recognition, inspection, navigation, monitoring, or modeling.
For dimensional inspection, extracted features can represent measurable geometric properties and the locations or relationships of relevant structures. An algorithm can compare these descriptors with the information required by the inspection task, helping engineers analyze whether observed spatial characteristics meet an intended condition. The approach reduces complex measurements to representations that are easier to evaluate systematically.
The technique is useful when a system must interpret the arrangement of objects or measurements in its surroundings or construct a meaningful spatial representation. In robot navigation, descriptors can support analysis of landmarks and local relationships. In spatial modeling, they help encode patterns from maps or sensor data, providing representations for monitoring, automation, and engineering system design.