Texture Classification using Spectral Decomposition Presenter: Cheong Hee Park Advisor: Victoria Interrante
Overview Goal: Visualization of multivariate data set in a planar 2D using principal perceptual features of texture. • Step1: Classify textures into meaningful categories. • Classification by directionality • Classification by regularity • Structural grouping • Step2: Synthesize a series of textures to convey values of multivariate data.
Review of texture analysis and data visualization • Discrete Fourier Transform • Classification by directionality • Classification by regularity • Classification by Structure • Future work
Visualization of Magnetic fieldusing orientation, size and contrast Using Visual Texture for Information Display - Colin Ware and William Knight (1995)
Display over a 3D surface using height, density and regularity Building Perceptual Textures to Visualize Multidimensional Datasets (C. Healey, J. Enns, 1998 )
Harnessing natural textures for multivariate visualization (Victoria Interrante) farms(percent) in 1992 percent change of farms from 1987 to 1992
What is texture? • An image composed of uniform or non-uniform repetition of natural or artificial patterns • Methods used for texture analysis • Autocorrelation • Co-occurrence based method • Parametric models of texture • Gray level run length • Spectral decomposition
Principal features of texture • Directionality:directional vs non-directional • Coarseness: coarse vs fine • Contrast: high contrast vs low contrast • Regularity:regular vs irregular (periodicity, randomness) • Line likeness: line-like vs blob-like • Roughness: rough vs smooth
Toward a texture naming system: identifying relevant dimensions of texture(A.R.Rao, G.L.Lohse, 1996) Marble-like Lace-like Directional, Locally-oriented Random, Non granular, Somewhat repetitive Non-random, Repetitive, non-directional <-> directional random Random, granular
Texture features corresponding to visual perception -Tamura, Mori and Yamawaki psychological measurement of directionality (by human subjects using pair comparison method) computational measurement of directionality (using local vertical and horizontal directional operators)
Modeling spatial and temporal textures - Fang Liu • Decomposition of texture into three components based on Wold theory: harmonic(periodicity), evanescent(directionality), indeterministic(random). • Measured deterministic energy from harmonic and evanescent components, and indeterministic energy from indeterministic component.
DFT deterministic indeterministic • Used energy measurements for texture modeling and image retrieval
Discrete Fourier Transform Given an image y(m,n), DFT IDFT
Y(l,k) in a frequency domain represents the response of cosine and sine filters.
Freq uency Hanning window DFT filtering
Directionality 10 f 0 --------- 17 f 0 Directionality = (K; number of columns)
Instead of two processes FFT and local window interpolation, apply global sinusoidal filters directly to the texture
Directionality from direct filtering
- Psychological experiment by Tamura • Ours(by interpolation) • (by direct filtering) • - computational experiment by Tamura Q: How can we judge which method is better ?
Pattern regularity as a visual keyD. Chetverikov using autocorrelation of gray intensities
Regularity (A: overlapping area) dominant direction }i Regularity = max f – min f i height/2
Directionality Regularity (by direct filtering)
Structural grouping Absolute Difference (L1 norm)
Future workHow to map attributes of multivariate data to texture perceptual dimensions independently? • What perceptual features of texture are most orthogonal? -- Minimize interference when they are combined for display of multivariate data. • Mapping should be continuous within an attribute and make maximum distinction between attributes.