Search results for "Function representation"

showing 4 items of 4 documents

Representation and estimation of spectral reflectances using projection on PCA and wavelet bases

2008

In this article, we deal with the problem of spectral reflectance function representation and estimation in the context of multispectral imaging. Because the reconstruction of such functions is an inverse problem, slight variations in input data completely skew the expected results. Therefore, stabilizing the reconstruction process is necessary. To do this, we propose to use wavelets as basis functions, and we compare those with Fourier and PCA bases. We present the idea and compare these three methods, which belong to the class of linear models. The PCA method is training-set dependent and confirms its robustness when applied to reflectance estimation of the training sets. Fourier and wave…

Mathematical optimizationbusiness.industryGeneral Chemical EngineeringMultispectral imageHuman Factors and ErgonomicsBasis functionPattern recognitionGeneral ChemistryInverse problemsymbols.namesakeWaveletFourier transformRobustness (computer science)Principal component analysissymbolsFunction representationArtificial intelligencebusinessMathematicsColor Research & Application
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The Kolmogorov Spline Network for Image Processing

2011

In 1900, Hilbert stated that high order equations cannot be solved by sums and compositions of bivariate functions. In 1957, Kolmogorov proved this hypothesis wrong and presented his superposition theorem (KST) that allowed for writing every multivariate functions as sums and compositions of univariate functions. Sprecher has proposed in (Sprecher, 1996) and (Sprecher, 1997) an algorithm for exact univariate function reconstruction. Sprecher explicitly describes construction methods for univariate functions and introduces fundamental notions for the theorem comprehension (such as tilage). Köppen has presented applications of this algorithm to image processing in (Köppen, 2002) and (Köppen &…

Multivariate statisticsUnivariateImage processing02 engineering and technologyBivariate analysisSuperposition theoremAlgebra03 medical and health sciencesSpline (mathematics)0302 clinical medicineImage processing[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingmultivariate function representationThin plate spline030217 neurology & neurosurgeryImage compressionMathematics
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New Representations for Multidimensional Functions Based on Kolmogorov Superposition Theorem. Applications on Image Processing

2012

Mastering the sorting of the data in signal (nD) can lead to multiple applications like new compression, transmission, watermarking, encryption methods and even new processing methods for image. Some authors in the past decades have proposed to use these approaches for image compression, indexing, median filtering, mathematical morphology, encryption. A mathematical rigorous way for doing such a study has been introduced by Andrei Nikolaievitch Kolmogorov (1903-1987) in 1957 and recent results have provided constructive ways and practical algorithms for implementing the Kolmogorov theorem. We propose in this paper to present those algorithms and some preliminary results obtained by our team…

Theoretical computer science[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processingbusiness.industry[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingSortingimage progressive transmissionImage processingimage encryption[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingMathematical morphologyEncryptionimage watermarkingimage compressionImage (mathematics)multi-variables function representation[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingKolmogorov superposition theoremMedian filterbusinessDigital watermarkingAlgorithm[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingImage compressionMathematics[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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Loop-free Gray code algorithm for the e-restricted growth functions

2011

The subject of Gray codes algorithms for the set partitions of {1,2,...,n} had been covered in several works. The first Gray code for that set was introduced by Knuth (1975) [5], later, Ruskey presented a modified version of [email protected]?s algorithm with distance two, Ehrlich (1973) [3] introduced a loop-free algorithm for the set of partitions of {1,2,...,n}, Ruskey and Savage (1994) [9] generalized [email protected]?s results and give two Gray codes for the set of partitions of {1,2,...,n}, and recently, Mansour et al. (2008) [7] gave another Gray code and loop-free generating algorithm for that set by adopting plane tree techniques. In this paper, we introduce the set of e-restricte…

Discrete mathematicsPrefix codeGeneralizationOrder (ring theory)Computer Science ApplicationsTheoretical Computer ScienceCombinatoricsSet (abstract data type)Gray codeTree (descriptive set theory)Signal ProcessingFunction representationRepresentation (mathematics)AlgorithmInformation SystemsMathematicsInformation Processing Letters
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