Digital Image Processing
Digital Image Processing - Related Videos
10:55
Digital Microfluidics for Automated Proteomic Processing
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Cited by 30 •
2009
Digital Microfluidics is a technique characterized by the manipulation of discrete droplets (~nL - mL) on an array of electrodes by the application of electrical fields. It is well-suited for carrying out rapid, sequential, miniaturized automated biochemical assays. Here, we report a platform capable of automating several proteomic processing steps.
11:34
High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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Cited by 9 •
2013
This video describes the fundamentals of digital fringe projection techniques, which provide dense 3D measurements of dynamically changing surfaces. It also demonstrates the design and operation of a high-speed binary defocusing system based on these techniques.
09:12
Calcified Artery Preparation and Processing with Preserved Morphology and RNA for Digital Spatial Profiling
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2026
This protocol includes a step-by-step workflow for calcified vascular specimens: tissue handling, decalcification, RNA validation, calcification level detection, and region-of-interest selection strategies on the Nanostring GeoMx Digital Spatial Profiler (DSP). The goal is to present a comprehensive method for preserving vascular tissue morphology and RNA for reliable spatial transcriptomic analysis.
08:45
Shrinkage of Dental Composite in Simulated Cavity Measured with Digital Image Correlation
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Cited by 12 •
2014
In order to understand the spatial development of polymerization shrinkage stress in dental resin-composite restorations, Digital Image Correlation was used to provide full-field displacement/strain measurement of restored model glass cavities by correlating images of the restoration taken before and after polymerization.
09:29
Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens
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Cited by 19 •
2016
We describe the use of digital image correlation to characterize the local surface strain field on vascular tissue samples subjected to uniaxial tensile testing. These measurements facilitate precise quantification of the sample mechanical response and the generation of constitutive stress-strain relations.
FAQs
Convolution applies a structured operation across neighboring pixels, making it useful for filtering and enhancement. By combining each pixel with information from its local surroundings, processing can address image noise or emphasize relevant visual patterns. This mechanism supports engineering tasks such as correcting degraded imagery before later measurement, segmentation, or feature-extraction stages.
Frequency-domain analysis examines image information according to spatial frequency, while direct processing operates on pixel values or local neighborhoods. This distinction provides another way to study image content and perform enhancement. Engineers may select frequency-based or statistical methods when the image-processing task requires a different representation of visual information than ordinary spatial operations provide.
Segmentation separates an image into meaningful regions, while feature extraction identifies measurable characteristics within those regions. Together, these steps convert improved visual data into information that can support analysis or automated decisions. In engineering, they can help isolate physical features for inspection, measurement, or subsequent control processes without relying only on the complete image.
Geometric transformations alter the spatial arrangement of image information, allowing processing to address image distortion or differences in position and shape. Their value depends on how accurately the transformation represents the imaging condition. In engineering workflows, this correction can improve the consistency of images used for physical measurements, comparison, inspection, or monitoring.
A typical workflow begins with an acquired image, followed by correction or enhancement of noise and distortion. The system can then apply filtering, geometric transformation, segmentation, and feature extraction as appropriate to the task. The resulting measurements or extracted information support interpretation, automated decisions, or control, with the sequence adapted to the image and engineering objective.
Digital Image Processing supports medical imaging, remote sensing, robotics, machine vision, and industrial quality assurance. In these settings, processing can improve interpretation of visual data, enable noncontact measurements, and support reliable monitoring of complex systems. The same general operations are adapted to the requirements of inspection, sensing, navigation, measurement, or system oversight.
Processed images can provide improved visual quality, corrected information, measurable physical features, or signals suitable for automated decisions. These outcomes help engineers interpret complex scenes and assess systems without necessarily making direct physical contact. Depending on the application, the results may contribute to inspection, monitoring, measurement, or control rather than serving only as enhanced visual displays.