The Radon Cumulative Distribution Transform and Its Application to Image Classification

Autor: Se Rim Park, Soheil Kolouri, Gustavo K. Rohde
Rok vydání: 2016
Předmět:
FOS: Computer and information sciences
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Feature extraction
Computer Science - Computer Vision and Pattern Recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image processing
02 engineering and technology
Article
Pattern Recognition
Automated
Image (mathematics)
law.invention
symbols.namesake
Wavelet
law
Image Processing
Computer-Assisted
0202 electrical engineering
electronic engineering
information engineering
Humans
Linear separability
Radon transform
Contextual image classification
business.industry
Signal Processing
Computer-Assisted
020206 networking & telecommunications
Pattern recognition
Computer Graphics and Computer-Aided Design
Invertible matrix
Fourier transform
Face
symbols
020201 artificial intelligence & image processing
Artificial intelligence
business
Algorithms
Software
Zdroj: IEEE Transactions on Image Processing. 25:920-934
ISSN: 1941-0042
1057-7149
DOI: 10.1109/tip.2015.2509419
Popis: Invertible image representation methods (transforms) are routinely employed as low-level image processing operations based on which feature extraction and recognition algorithms are developed. Most transforms in current use (e.g., Fourier, wavelet, and so on) are linear transforms and, by themselves, are unable to substantially simplify the representation of image classes for classification. Here, we describe a nonlinear, invertible, low-level image processing transform based on combining the well-known Radon transform for image data, and the 1D cumulative distribution transform proposed earlier. We describe a few of the properties of this new transform, and with both theoretical and experimental results show that it can often render certain problems linearly separable in a transform space.
Databáze: OpenAIRE
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