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Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Relative Motion Analysis using Rotating Axes01:25

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Related Experiment Video

Updated: Oct 11, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
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Dermoscopic image retrieval based on rotation-invariance deep hashing.

Yilan Zhang1, Fengying Xie1, Xuedong Song2

  • 1Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China.

Medical Image Analysis
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for dermoscopic image retrieval, enhancing diagnostic accuracy for skin conditions. The developed system utilizes convolutional neural networks (CNNs) and a unique spatial attention module for improved feature extraction and rotation invariance.

Keywords:
Attention mechanismCauchy distributionConvolutional neural networkDeep hashingImage retrieval

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Computer Science

Background:

  • Dermoscopic image retrieval aids dermatologists by providing access to similar cases and diagnostic reports.
  • Accurate retrieval is crucial for efficient and reliable skin disease diagnosis.

Purpose of the Study:

  • To develop an advanced dermoscopic image retrieval algorithm using convolutional neural networks (CNNs) and hash coding.
  • To improve the accuracy and robustness of image retrieval for complex skin lesion morphologies.

Main Methods:

  • A hybrid dilated convolution spatial attention module was designed to focus on relevant image features.
  • A Cauchy rotation invariance loss function was proposed to address the lack of a main direction in skin lesions.
  • The algorithm was implemented using CNNs and hash coding for efficient retrieval.

Main Results:

  • The proposed spatial attention module effectively highlights critical information while suppressing irrelevant details.
  • The Cauchy rotation invariance loss function enabled the CNNs to achieve rotation invariance.
  • Experiments demonstrated superior performance of the rotation-invariance deep hashing network with the spatial attention module in dermoscopic image retrieval tasks.

Conclusions:

  • The developed algorithm, incorporating a novel spatial attention module and rotation invariance loss function, significantly enhances dermoscopic image retrieval.
  • This technology offers a valuable tool for dermatologists, improving diagnostic support and efficiency.