Kerekes et al., 2004 - Google Patents
Improved modeling of background distributions in an end-to-end spectral imaging system modelKerekes et al., 2004
View PDF- Document ID
- 17488520917649957543
- Author
- Kerekes J
- Manolakis D
- Publication year
- Publication venue
- IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium
External Links
Snippet
Previously, an analytical end-to-end spectral imaging system model has been developed. The model is constructed around the propagation of spectral statistics from the scene, through the sensor, and processing transformations to lead to prediction of a performance …
- 238000000701 chemical imaging 0 title abstract description 7
Classifications
-
- 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
- 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
- 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
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- 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
- G06K9/00771—Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity
-
- 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
- G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Albregtsen | Statistical texture measures computed from gray level coocurrence matrices | |
Tolpekin et al. | Quantification of the effects of land-cover-class spectral separability on the accuracy of Markov-random-field-based superresolution mapping | |
Stefanou et al. | A method for assessing spectral image utility | |
Manolakis et al. | Statistics of hyperspectral imaging data | |
Liu et al. | Detection and recognition of security detection object based on YOLO9000 | |
Manolakis et al. | The remarkable success of adaptive cosine estimator in hyperspectral target detection | |
Li et al. | Towards automation of palynology 2: the use of texture measures and neural network analysis for automated identification of optical images of pollen grains | |
Boshra et al. | Predicting performance of object recognition | |
Imbiriba et al. | Nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images | |
Yavariabdi et al. | Change detection in multispectral landsat images using multiobjective evolutionary algorithm | |
CN103093243A (en) | High resolution panchromatic remote sensing image cloud discriminating method | |
Paulson et al. | Articulation study for SAR ATR baseline algorithm | |
Ilina et al. | Robustness study of a deep convolutional neural network for vehicle detection in aerial imagery | |
Kerekes et al. | Improved modeling of background distributions in an end-to-end spectral imaging system model | |
Manolakis et al. | Non gaussian models for hyperspectral algorithm design and assessment | |
Kowkabi et al. | Hybrid preprocessing algorithm for endmember extraction using clustering, over-segmentation, and local entropy criterion | |
Stefanou et al. | Image-derived prediction of spectral image utility for target detection applications | |
Pralon et al. | Spherical symmetry of complex stochastic models in multivariate high-resolution PolSAR images | |
Thepade et al. | Machine learning based land use identification of aerial images with fusion of thepade sbtc and triangle thresholding | |
Manolakis | Realistic matched filter performance prediction for hyperspectral target detection | |
Bandstra et al. | Correlations between panoramic imagery and gamma-ray background in an urban area | |
West et al. | Comparative evaluation of background characterization techniques for hyperspectral unstructured matched filter target detection | |
Peña et al. | Unmixing low-ratio endmembers in hyperspectral images through Gaussian synapse ANNs | |
Plaza et al. | Automated generation of semi-labeled training samples for nonlinear neural network-based abundance estimation in hyperspectral data | |
Davis | Using multiple robust parameter design techniques to improve hyperspectral anomaly detection algorithm performance |