Nelson et al., 2010 - Google Patents
Dual-tree wavelets for estimation of locally varying and anisotropic fractal dimensionNelson et al., 2010
View PDF- Document ID
- 7270157482477278160
- Author
- Nelson J
- Kingsbury N
- Publication year
- Publication venue
- 2010 IEEE International Conference on Image Processing
External Links
Snippet
The dual-tree wavelet transform is here applied to the problem of fractal dimension estimation. The Hurst parameter of fractional Brownian surfaces is estimated using various wavelet bases. Results are given for global, local, anisotropic, and both local and …
- 238000004458 analytical method 0 description 3
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
- G06K9/629—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion of extracted features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00496—Recognising patterns in signals and combinations thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Nelson et al. | Dual-tree wavelets for estimation of locally varying and anisotropic fractal dimension | |
Bombrun et al. | Fisher distribution for texture modeling of polarimetric SAR data | |
CN103456018B (en) | Remote sensing image change detection method based on fusion and PCA kernel fuzzy clustering | |
Jiang et al. | Hybrid approach for unbiased coherence estimation for multitemporal InSAR | |
Schmitt et al. | Adaptive multilooking of airborne single-pass multi-baseline InSAR stacks | |
Yahya et al. | Subspace-based technique for speckle noise reduction in SAR images | |
CN102722892A (en) | SAR (synthetic aperture radar) image change detection method based on low-rank matrix factorization | |
Liu et al. | A hybrid method of SAR speckle reduction based on geometric-structural block and adaptive neighborhood | |
Xin et al. | Dual multi-scale filter with SSS and GW for infrared small target detection | |
Yu et al. | Remote sensing image denoising application by generalized morphological component analysis | |
Lukin et al. | Discrete cosine transform–based local adaptive filtering of images corrupted by nonstationary noise | |
Ren et al. | A global weighted least-squares optimization framework for speckle filtering of PolSAR imagery | |
Vallejos et al. | Image similarity assessment based on coefficients of spatial association | |
Qin et al. | Simulation of spatially correlated PolSAR images using inverse transform method | |
Long et al. | Statistical image modeling in the contourlet domain using contextual hidden Markov models | |
Rezazadeh et al. | Low-complexity computation of visual information fidelity in the discrete wavelet domain | |
Yasar et al. | Comparison of real and complex-valued versions of wavelet transform, curvelet transform and ridgelet transform for medical image denoising | |
Espejo et al. | Gegenbauer random fields | |
Anand et al. | Edge detection using directional filter bank | |
Tzagkarakis et al. | Covariation-based subspace-augmented MUSIC for joint sparse support recovery in impulsive environments | |
Long et al. | Contourlet image modeling with contextual hidden Markov models | |
Tomaszewska | Blind noise level detection | |
Belhadj-Aissa et al. | Contextual filtering methods based on the subbands and subspaces decomposition of complex SAR interferograms | |
Ben Abdallah et al. | A comparitive study on the perfermance of the insar phase filtering approches in the spatial and the wavelet domains | |
Parshin | Optimal signal and image processing in presence of additive fractal interference |