mathematical applications on digital image


Role of mathematical morphology in digital image processing: A review
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Image processing is a method to convert an image into digital form by performing operations on it for getting an enhanced image or to extract some useful information from it. It is a type of signal distribution in which input is image like video frame or photograph and output may be

Mathematical methods applied to digital image processing
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Digital image processing (DIP) is an important research area since it spans a variety of applications. Although over the past few decades there has been a rapid rise in this field, there still remain issues to address. Examples include image coding, image restoration, 3D

Digital Image Processing using techniques of mathematical morphology
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The cartographic products of Brazil are around 30 years outdated. The present work describes an alternative to diminish this problem by using the coupling of products of Remote Sensing and techniques of mathematical morphology (MM). The images of the

A Survey Paper on: Fuzzy Mathematical Morphology Techniques for Digital Image Processing
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Article Preview Article Preview Article Preview This paper puts across the various approaches and methods that have been proposed in the context of Fuzzy Mathematical Morphology. The underlying principles of Dilation Erosion, the structuring elements used

Mathematical methods for time series analysis and digital image processing
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This report presents the results of a comparison of denoising methods for one dimensional time series. The comparison has been carried out within the DFG Priority Program 1114 Mathematical methods for time series analysis and digital image processing . The aim of this

A Review On Role Of Mathematical Morphology In Digital Image Processing
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The geometric structures of images and signals can be systematically analyzed by using mathematical morphology. Mathematical morphology provides very useful tools and techniques for analysis of digital images. Image Operations like reduction, enlargement

Mathematical methods for time series analysis and digital image processing
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Quantification of directed signal transfer within neural networks by partial directed coherence: A novel approach to infer causal timedepending

Mathematical methods for time series analysis and digital image processing
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Quantification of directed signal transfer within neural networks by partial directed coherence: A novel approach to infer causal timedepending

Mathematical methods for time series analysis and digital image processing
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Quantification of directed signal transfer within neural networks by partial directed coherence: A novel approach to infer causal timedepending

Mathematical methods for time series analysis and digital image processing
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Nonlinear diffusion filtering and wavelet shrinkage are two methods that serve the same purpose, namely discontinuity-preserving denoising. In this chapter we give a survey on relations between both paradigms when space-discrete or fully discrete versions of

Mathematical methods for time series analysis and digital image processing
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Realization theory for linear input-output operators and frequency-domain methods for the solvability of Riccati operator equations are used for the stability and instability investigation of a class of nonlinear Volterra integral equations in some Hilbert space. The key idea is to

DFG Schwerpunktprogramm 1114 Mathematical methods for time series analysis and digital image processing
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In this paper we discuss the problem of identifiability of the intensity function of non- stationary Poisson process on the basis of the corresponding shot-noise process and a construction of non-parametric estimator for it. Some applications of these results to traffic

Mathematical methods for time series analysis and digital image processing
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This paper is concerned with generalizations and specific applications of the coorbit space theory based on group representations modulo quotients that has been developed quite recently. We show that the general theory applied to the affine Weyl Heisenberg group

DFG Schwerpunktprogramm 1114 Mathematical methods for time series analysis and digital image processing
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We introduce a method for estimating periodicity in arbitrary time series, which uses only the ordinal behavior. Our method is compared with the periodogram for short windows, changing frequency, phase jump and observational noise. For the estimation of pitch period



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