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Encoding01:19

Encoding

238
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
238
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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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...
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Related Experiment Video

Updated: Aug 31, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Semantic segmentation method of underwater images based on encoder-decoder architecture.

Jinkang Wang1, Xiaohui He1, Faming Shao1

  • 1Department of Mechanical Engineering, College of Field Engineering and Army Engineering University, PLA, Nanjing, China.

Plos One
|August 25, 2022
PubMed
Summary

This study introduces an enhanced deep learning method for underwater image semantic segmentation. The approach improves image quality and segmentation accuracy, achieving superior results on benchmark datasets.

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Area of Science:

  • Marine Biology
  • Computer Vision
  • Deep Learning

Background:

  • Underwater image quality is a significant challenge for deep learning-based semantic segmentation.
  • Low-quality images lead to blurred edges, inaccurate segmentation, and poor boundary details.

Purpose of the Study:

  • To propose an advanced semantic segmentation method for improving underwater image processing.
  • To address the limitations of traditional methods in segmenting low-quality underwater imagery.

Main Methods:

  • Image enhancement using multi-spatial transformation to improve original image quality.
  • Densely connected hybrid atrous convolution to expand receptive fields and manage resolution reduction.
  • Cascaded atrous convolutional spatial pyramid pooling for integrating multi-scale boundary features.
  • Context information aggregation decoder for fusing shallow and deep network features to reduce information loss.

Main Results:

  • The proposed method demonstrated superior segmentation integrity, location accuracy, and boundary clarity compared to state-of-the-art algorithms.
  • Achieved the highest Mean Intersection over Union (MIOU) of 68.3 and Overall Accuracy (OA) of 79.4 on benchmark datasets.
  • Exhibited low resource consumption and validated effectiveness through ablation experiments.

Conclusions:

  • The developed method significantly enhances semantic segmentation of underwater images.
  • It offers a robust solution for marine resource exploration and development by improving visual data analysis.
  • The approach effectively overcomes the challenges posed by low-quality underwater imagery.