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Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
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Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Subspace learning from image gradient orientations.

Georgios Tzimiropoulos1, Stefanos Zafeiriou, Maja Pantic

  • 1School of Computer Science, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, United Kingdom, and also with the Department of Computing, Imperial College Longdon, London SW7 2AZ, United Kingdom. gtzimiropoulos@lincoln.ac.uk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 25, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces Image Gradient Orientations (IGO) subspace learning for object recognition. IGO methods improve robustness to noise and achieve state-of-the-art performance in face recognition tasks.

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13:44

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Published on: August 30, 2013

Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Traditional subspace learning methods struggle with noisy image data.
  • Pixel intensity-based approaches are unreliable due to non-Gaussian noise.

Purpose of the Study:

  • Introduce Image Gradient Orientations (IGO) subspace learning for robust object recognition.
  • Address limitations of traditional pixel intensity-based subspace learning.

Main Methods:

  • Utilize image gradient orientations instead of pixel intensities.
  • Employ a cosine-based distance measure.
  • Formulate and analyze Principal Component Analysis of image gradient orientations (IGO-PCA).
  • Derive IGO versions of Linear Discriminant Analysis (LDA), Locally Linear Embedding (LLE), and Laplacian Eigenmaps (LE).

Main Results:

  • IGO subspace learning demonstrates improved reliability in estimating low-dimensional subspaces.
  • Algorithms significantly outperform Gabor features and Local Binary Patterns.
  • Achieve state-of-the-art performance in illumination and occlusion-robust face recognition.

Conclusions:

  • Image Gradient Orientations (IGO) subspace learning offers a robust alternative for appearance-based object recognition.
  • IGO methods are computationally efficient, comparable to intensity-based counterparts.
  • The approach provides a significant advancement for challenging recognition tasks.