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Upsampling

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

Downsampling

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

A Novel Upsampling and Context Convolution for Image Semantic Segmentation.

Khwaja Monib Sediqi¹, Hyo Jong Lee¹

¹Division of Computer Science and Engineering, CAIIT, Jeonbuk National University, Jeonju 54896, Korea.

Sensors (Basel, Switzerland)

|April 3, 2021

Summary

This study introduces a new semantic segmentation method using dense upsampling and local context convolutions to improve spatial information preservation and object boundary delineation in computer vision tasks like autonomous driving.

Keywords:

computer vision convolutional neural networks deep learning pixel-wise classification semantic segmentation

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Area of Science:

Computer Vision
Deep Learning
Image Segmentation

Background:

Semantic segmentation is crucial for robot vision and autonomous driving, providing object details.
Current encoder-decoder networks for semantic segmentation lose spatial information and context.

Purpose of the Study:

To propose a novel semantic segmentation method that preserves spatial information and enhances context awareness.
To achieve precise object boundary delineation for improved scene understanding.

Main Methods:

A novel dense upsampling convolution method utilizing a guided filter to retain image spatial information.
A novel local context convolution method for dense coverage of objects, including large-scale ones.

Main Results:

The proposed method effectively preserves spatial information and delineates object boundaries with high accuracy.
Achieved new record pixel accuracies: 82.86% on ADE20K and 81.62% on Pascal-Context.

Conclusions:

The novel approach offers significant improvements over state-of-the-art semantic segmentation methods.
Demonstrates superior performance in preserving spatial details and defining object boundaries.