Corpus ID: 16786319

An empirical study on the effects of different types of noise in image classification tasks

  title={An empirical study on the effects of different types of noise in image classification tasks},
  author={G. B. P. D. Costa and Welinton A. Contato and Tiago S. Nazar{\'e} and J. B. Neto and M. Ponti},
Image classification is one of the main research problems in computer vision and machine learning. Since in most real-world image classification applications there is no control over how the images are captured, it is necessary to consider the possibility that these images might be affected by noise (e.g. sensor noise in a low-quality surveillance camera). In this paper we analyse the impact of three different types of noise on descriptors extracted by two widely used feature extraction methods… Expand
26 Citations
Survey of Face Detection on Low-Quality Images
  • Yuqian Zhou, Ding Liu, T. Huang
  • Computer Science
  • 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)
  • 2018
  • 30
  • PDF
Deep Convolutional Neural Networks and Noisy Images
  • 28
  • PDF
Impact of Low Resolution on Image Recognition with Deep Neural Networks: An Experimental Study
  • 12
Classification-Driven Dynamic Image Enhancement
  • 29
  • PDF
Genetic Programming-Based Discriminative Feature Learning for Low-Quality Image Classification.
  • PDF
Feature Quantization for Defending Against Distortion of Images
  • 8


Improving Non-local Video Denoising with Local Binary Patterns and Image Quantization
  • 4
  • PDF
Survey on LBP based texture descriptors for image classification
  • 234
  • PDF
Understanding how image quality affects deep neural networks
  • Samuel F. Dodge, Lina Karam
  • Computer Science
  • 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX)
  • 2016
  • 327
  • Highly Influential
  • PDF
BM3D Image Denoising with Shape-Adaptive Principal Component Analysis
  • 409
Color-to-Grayscale: Does the Method Matter in Image Recognition?
  • 196
  • PDF
Rotation-invariant texture classification using feature distributions
  • 456
  • PDF
Evaluation of noise robustness for local binary pattern descriptors in texture classification
  • 82
  • PDF
A non-local algorithm for image denoising
  • A. Buades, B. Coll, J. Morel
  • Mathematics, Computer Science
  • 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
  • 2005
  • 5,191
  • PDF