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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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The Ideal Transformer01:26

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In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's tangential...
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Transformers in Distribution System01:27

Transformers in Distribution System

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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Transformers with Off-Nominal Turns Ratios01:25

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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Related Experiment Video

Updated: Feb 25, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

8.2K

Two-Stream Transformer Networks for Video-Based Face Alignment.

Hao Liu, Jiwen Lu, Jianjiang Feng

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 8, 2017
    PubMed
    Summary
    This summary is machine-generated.

    We introduce a two-stream transformer network (TSTN) for video face alignment. This method enhances accuracy by analyzing both spatial appearance and temporal consistency across video frames.

    Related Experiment Videos

    Last Updated: Feb 25, 2026

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    8.2K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Conventional image-based face alignment methods struggle to model temporal dependencies in videos.
    • Facial landmarks exhibit consistent movements across consecutive video frames, a crucial factor for accurate alignment.

    Purpose of the Study:

    • To propose a novel two-stream transformer network (TSTN) for improved video-based face alignment.
    • To capture complementary spatial and temporal information for robust facial landmark detection.

    Main Methods:

    • Developed a two-stream architecture to process videos for face alignment.
    • Spatial stream focuses on transforming facial images to landmark positions while preserving holistic shape.
    • Temporal stream encodes video input into active appearance codes, leveraging temporal consistency for shape refinement.

    Main Results:

    • The TSTN approach demonstrated highly competitive performance on benchmark video-based face alignment datasets.
    • Experimental results indicate superior accuracy compared to existing state-of-the-art methods.

    Conclusions:

    • The proposed TSTN effectively integrates spatial and temporal information for accurate video face alignment.
    • TSTN offers a promising advancement in analyzing dynamic facial data for landmark detection.