Pixel Labeling
Pixel Labeling - Related Videos
08:41
Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
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Cited by 11 •
2012
Lensfree optical tomography is a three-dimensional microscopy technique that offers a spatial resolution of <1 μm × <1 μm × <3 μm in x, y and z dimensions, respectively, over a large imaging-volume of 15-100 mm3, which can be particularly useful for integration with lab-on-a-chip platforms.
09:19
Simultaneous Label-Free Autofluorescence Multi-Harmonic Microscopy
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Cited by 3 •
2025
This protocol presents a step-by-step guide for the Simultaneous Label-free Autofluorescence Multi-harmonic (SLAM) microscopic technique, including details on how to generate the laser light source, prepare a tissue sample, conduct imaging, and analyze the data. SLAM advances nonlinear microscopy by measuring four complementary label-free contrasts to investigate the tissue microenvironment.
08:28
Metabolic Labeling
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2023
Metabolic labeling is used to probe the biochemical transformations and modifications that occur in a cell. This is accomplished by using chemical analogs that mimic the structure of natural biomolecules. Cells utilize analogs in their endogenous biochemical processes, producing compounds that are labeled. The label allows for the incorporation of detection and affinity tags, which can then be used to elucidate metabolic pathways using other biochemical analytical techniques, such as SDS-PAGE...
10:18
CRISPR/Cas12a Multiplex Genome Editing of Saccharomyces cerevisiae and the Creation of Yeast Pixel Art
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Cited by 11 •
2019
The CRISPR/Cas12a system in combination with a single crRNA array enables efficient multiplex editing of the S. cerevisiae genome at multiple loci simultaneously. This is demonstrated by constructing carotenoid producing yeast strains which are subsequently used to create yeast pixel art.
12:19
ReAsH/FlAsH Labeling and Image Analysis of Tetracysteine Sensor Proteins in Cells
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Cited by 10 •
2011
The biarsenical dyes FlAsH and ReAsH bind specifically to tetracysteine motifs in proteins and can selectively label proteins in live cells. Recently this labeling strategy has been used to develop sensors for different protein conformations or oligomeric states. We describe the labeling approach and methods to quantitatively analyze binding.
FAQs
The classification decision can draw on spectral reflectance, color, texture, and spatial context. Spectral reflectance helps distinguish materials with different responses to light, while color and texture add visual information about surface appearance. Spatial context contributes information about neighboring pixels and surrounding patterns, helping separate regions that may look similar when viewed as isolated pixels.
Supervised classification and machine learning represent two supported approaches for assigning environmental categories. Supervised methods use predefined classes such as vegetation, water, soil, or built surfaces, whereas machine-learning algorithms learn classification patterns from image information. The chosen approach affects how pixel values and contextual features are used to produce a structured land-cover map.
A pixel’s value may not provide enough information to distinguish neighboring land-cover types that share similar spectral or color characteristics. Spatial context adds the arrangement and relationship of nearby pixels, allowing the analysis to consider broader patterns and textures. This can improve separation among vegetation, soil, water, and built surfaces within complex environmental scenes.
Researchers begin with satellite, aerial, or ground-based imagery and identify the environmental categories relevant to the investigation. An algorithm then evaluates pixel information, including spectral, color, texture, or spatial features, and assigns pixels to those categories. The output is a labeled image that can be examined as a map of land-cover regions and landscape features.
The method is useful when researchers need to map or compare environmental features across large areas. Supported applications include land-use mapping, habitat assessment, crop monitoring, forest monitoring, and detection of environmental change. By converting imagery into categorized regions, it supports measurement of landscape patterns and evaluation of changing ecosystem conditions over time.
A labeled image provides a structured representation of where categories such as water, vegetation, soil, and built surfaces occur. Researchers can use that representation to quantify landscape patterns, assess habitat-related features, and monitor crops or forests. Comparing labeled imagery can also reveal environmental change, making the technique relevant to regional assessment from satellite, aerial, or ground observations.