Pattern Recognition from Satellite Altitudes

Darling, Eugene M.; Joseph, Robin

doi:10.1109/tssc.1968.300186

Cited by 43 publications

(20 citation statements)

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“…All four-direction features are used. Contrary to the good classification results on only four classes of 80 samples in [22], all groups of features performed poorly here. With only 35% classification accuracy, the result of using all three feature groups together is much worse than any single feature group.…”

Section: Classification Experimentscontrasting

confidence: 99%

“…Similar texture measures were used by Darling and Joseph [22] for analyzing satellite images of clouds. Rosenfeld and Troy [95] and Haralick et al [48] first proposed co-occurrence matrices for arbitrary spatial distances and angular directions.…”

Section: Spatial Gray-level Dependence Methods (Sgldm)mentioning

confidence: 99%

“…Table 5.1 shows the classification results using the traditional run-length features directly on the Brodatz images. Similar to [23], the feature groups tested are the original five features of Galloway [42], the two features of Chu et al [16], and the four new features of Dasarathy and Holder [22]. All four-direction features are used.…”

Section: Classification Experimentsmentioning

confidence: 99%

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Transform texture classification

Tang¹

1996

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of a texture classification system: texture image transform, feature extraction/selection, and classification. A unique theoretical investigation of texture analysis, drawing on an extensive survey of existing approaches, defines the interrelations among 11 types of texture analysis methods. A novel unification of the different methods defines a framework of transformation and representation in which three major classes of transform matrices capture texture information of increasing coherence length or correlation distance: the spatial domain method (co-occurrence method), the micro-structural method (run-length method), and the frequency multi-channel method (Fourier spectrum method).A more concise vector representation of a selected transform matrix is then needed for input to a classifier. Unlike traditional methods, which use various special functions to describe the properties of each transform matrix, a new approach directly applies a principle component analysis technique to the transform matrix. The Karhunen-Loeve Transfonn (KLT) extracts a vector of dominant features, optimally preserving texture information in the matrix. This approach is made possible by the introduction of a novel Multi-level Dominant Eigenvector Estimation (MDEE) algorithm, which reduces the computational complexity of the standard KLT by several orders of magnitude. The statistical Bhattacharyya distance measure is then used to rank dominant features according to their discrimination power.Experimental results of applying the new algorithin to the threei.lap$fQrnl matrix classes show a strong increase in performance by texture analysis methods traditionally considered to be least efficient For example, the power spectrum and run-length methods now rank among the best. Using the same MDEE algorithm, the three extracted feature vectors are then combined into a more complete description of texture images. The same approach is also used for a study of object recognition, where the combined vector also include granulometric; object-boundary, and moment-invariant features.In most classification experiments, a simple statistical Gaussian classifier is used. The plankton object recognition experiments use a Learning Vector Quantization (LVQ) neu-. ral-net classifier to achieve superior performance on the highly non-uniform plankton database. By introducing a new parallel LVQ learning scheme, the speed of network training is dramatically increased. Tests show a 95% classification accuracy on six plankton taxa taken from nearly 2,000 images. This result is comparable with what a trained biologist can accomplish by traditional manual techniques, making possible for the first time a fully automated, at-sea approach to real-time mapping of plankton populations. -Cindy Sullivan: for re-typing some of the lost files and many other helps.-Beven Grant and Larry Flick: for all the administrative helps.-Dezhang Chu: for being a true acoustic expert.-Everyone at the DSL for a friendly and helpful environment.Everyone at MITIWHOI who has made my life h...

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Section: Classification Experimentscontrasting

confidence: 99%

Section: Spatial Gray-level Dependence Methods (Sgldm)mentioning

confidence: 99%

Section: Classification Experimentsmentioning

confidence: 99%

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Transform texture classification

Tang¹

1996

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“…An inherent property of all surfaces, "texture" provides us with a vocabulary to describe the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Texture analysis was initially developed for the assessment of aerial photographs, 3,4 with the first reported applications to medical image interpretation appearing shortly thereafter. [5][6][7] Although initially slow to build clinical interest, there has been a sort of "texture renaissance" during the past decade, coincident with the steep increase in computational and digital storage capability, as well as a growing comfort with (and demand for) automatic or semiautomatic image analysis tools.…”

mentioning

confidence: 99%

Texture Analysis: A Review of Neurologic MR Imaging Applications

Kassner¹,

Thornhill²

2010

AJNR Am J Neuroradiol

352

265

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SUMMARY:Texture analysis describes a variety of image-analysis techniques that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Texture analysis may be particularly well-suited for lesion segmentation and characterization and for the longitudinal monitoring of disease or recovery. We begin this review by outlining the general procedure for performing texture analysis, identifying some potential pitfalls and strategies for avoiding them. We then provide an overview of some intriguing neuro-MR imaging applications of texture analysis, particularly in the characterization of brain tumors, prediction of seizures in epilepsy, and a host of applications to MS.ABBREVIATIONS: ABSV ϭ absolute gradient value; AIS ϭ acute ischemic stroke; ANN ϭ artificial neural network; CIS ϭ clinically isolated syndrome; d ϭ distance; DCE ϭ dynamic contrastenhanced; FCD ϭ focal cortical dysplasia; FLAIR ϭ fluid-attenuated inversion recovery; f x ϭ second-order gray-level co-occurrence feature number "x"; GLCM ϭ gray-level co-occurrence matrix; GM ϭ gray matter; HT ϭ hemorrhagic transformation; LDA ϭ linear discriminant analysis; MGL ϭ mean gray level; MGR ϭ mean gradient; MS ϭ multiple sclerosis; MTR ϭ magnetization transfer ratio; NAWM ϭ normal-appearing white matter; N g ϭ number of gray levels; PCA ϭ principal components analysis; PPMS ϭ primary-progressive MS; RLM ϭ run-length matrix; ROC ϭ receiver-operator characteristic; ROI ϭ region of interest; RRMS ϭ relapsing-remitting MS; rtPA ϭ recombinant tissue plasminogen activator; SPMS ϭ secondary-progressive MS; SVM ϭ support vector machine; ϭ direction; VGL ϭ variance of gray levels; VGR ϭ variance gradient; WM ϭ white matter

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“…Computer based methods of texture analysis were originally developed for use in satellite application, geological surveys, remote sensing and other related applications [48][49][50][51]. A wide range of techniques are in existence.…”

Section: Iris Verification Using Texturementioning

confidence: 99%

Personal Verification Using Two Level Fusion Schemes Based on Ear and Iris Biometrics

Khursheed¹

2016

IJCDS

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Increased threats to conventional personnel verification methods, have given rise to verification methods based on biometrics. This paper presents a novel approach for personal verification based on fusion of two biometric modalities: Ear and Iris. Fusion has been done in two levels. In level one right iris and left iris features have been fused using texture based Grey Level Concurrence Matrix (GLCM) and in level two these features have been fused with AR coefficients taken from ear substructure. It has been found that this two level fusion of features improves the recognition rate of a person to the extent of 100%. The biometric modalities of ear and iris have been chosen on the basis of their desirable properties like uniqueness, universality, permanency and acceptability.

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Pattern Recognition from Satellite Altitudes

Cited by 43 publications

References 1 publication

Transform texture classification

Transform texture classification

Texture Analysis: A Review of Neurologic MR Imaging Applications

Personal Verification Using Two Level Fusion Schemes Based on Ear and Iris Biometrics

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