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Computed Tomography01:10

Computed Tomography

4.7K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
4.7K
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

117
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
117
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

107
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
107
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
811
Reducing Line Loss01:18

Reducing Line Loss

185
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
185
Fischer Projections02:18

Fischer Projections

13.5K
Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines.
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Related Experiment Video

Updated: Aug 15, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

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Foveated 3D range geometry compression via loss-tolerant variable precision depth encoding.

Broderick S Schwartz, Matthew G Finley, Tyler Bell

    Applied Optics
    |January 6, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel feature-driven compression method for 3D range geometry, prioritizing precision in regions of interest. This approach significantly reduces file sizes while maintaining data integrity for applications like telepresence.

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    Area of Science:

    • Computer Vision
    • Data Compression
    • 3D Imaging

    Background:

    • 3D range geometry acquisition is advancing, leading to increased data volumes.
    • Existing compression methods strain storage and transmission capacities.
    • Need for efficient compression that balances precision and file size.

    Purpose of the Study:

    • To propose a feature-driven compression method for 3D range geometry.
    • To enable higher precision in regions of interest while compressing other data more aggressively.
    • To support both lossless and lossy compression for greater file-size savings.

    Main Methods:

    • Encoding 3D data into 2D RGB images using sinusoidal modulation.
    • Implementing a feature-driven approach to identify and prioritize regions of interest (e.g., faces).
    • Utilizing a foveated encoding distribution for variable precision.

    Main Results:

    • Achieved a 26% reduction in compressed file size compared to fixed-precision encoding.
    • Demonstrated competitive RMS reconstruction error in the region of interest (0.56 mm vs. 0.54 mm).
    • Successfully applied to a depth scan of a bust, prioritizing facial region precision.

    Conclusions:

    • The proposed feature-driven compression method offers significant file-size reductions for 3D range geometry.
    • It effectively balances precision requirements across different regions within a scan.
    • This technique is valuable for applications demanding efficient storage and transmission of high-precision 3D data.