An empirical study on the effects of different types of noise in image classification tasks.
Gabriel B. Paranhos da CostaWelinton A. ContatoTiago S. NazaréJoão do E. S. Batista NetoMoacir PontiPublished in: CoRR (2016)
Keyphrases
- image noise
- image data
- multiscale
- background noise
- geometric distortions
- test images
- image content
- image structure
- image pixels
- image representation
- input image
- image retrieval
- single image
- image edges
- high resolution
- edge detection
- segmentation method
- high contrast
- random noise
- median filtering
- image analysis
- image segmentation
- additive noise
- transformed image
- image features
- pixel values
- image collections
- region of interest
- salt pepper
- segmentation algorithm
- image degradation
- imaging process
- image regions
- low signal to noise ratio
- degraded images
- template matching
- noisy data
- low frequency
- digital images
- noise free
- pixel intensities
- feature points
- noise sensitivity
- noise level