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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Foreground segmentation network using transposed convolutional neural networks and up sampling for multiscale feature

Vishruth B Gowda1, M T Gopalakrishna2, J Megha3

  • 1Department of Computer Science and Engineering, SJB Institute of Technology, Bengaluru, Karnataka 560060, India; Visvesavaraya Technological University, Belgavi, Karnataka 590018, India.

Neural Networks : the Official Journal of the International Neural Network Society
|November 20, 2023
PubMed
Summary

This study introduces a novel Foreground Segmentation Network (FgSegNet) using triplet CNN and Transposed Convolutional Neural Network (TCNN) to improve moving object detection. FgSegNet significantly enhances foreground segmentation accuracy in challenging conditions.

Keywords:
CDnet2014 datasetFeature pooling moduleForeground segmentationMulti-scale feature encodingsTransposed convolutional neural network

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Foreground segmentation is crucial for separating moving objects but faces challenges from darkness, dynamic backgrounds, and camera jitter.
  • Existing detection networks struggle with complex environmental interferences, limiting their effectiveness.

Purpose of the Study:

  • To develop an advanced foreground segmentation algorithm that overcomes limitations of existing methods.
  • To enhance the accuracy and robustness of foreground segmentation in diverse and challenging environments.

Main Methods:

  • A novel Foreground Segmentation Network (FgSegNet) was developed, incorporating a triplet Convolutional Neural Network (CNN) and Transposed Convolutional Neural Network (TCNN).
  • A Features Pooling Module (FPM) was integrated to extract multi-scale features and fuse them, reducing input complexity.
  • An up-sampling network was added to match the spatial dimensions of the abstract image representation with the input image.

Main Results:

  • FgSegNet demonstrated superior performance on the CDnet2014 datasets compared to state-of-the-art algorithms.
  • Achieved an average F-Measure of 0.9804, precision of 0.9801, and recall of 0.9896.
  • The inclusion of an up-sampling network further improved the F-measure to 0.9804.

Conclusions:

  • The proposed FgSegNet effectively addresses challenges in foreground segmentation, achieving high accuracy.
  • The combination of triplet CNN, TCNN, FPM, and up-sampling significantly enhances foreground segmentation performance.
  • FgSegNet represents a significant advancement in robust foreground segmentation for computer vision applications.