Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.typeArtigo de eventopt_BR
dc.titleMultiscale Methods For Image Processing: The Wavelet And The Scale-space Approachespt_BR
dc.contributor.authorDorini L.B.pt_BR
dc.contributor.authorLeite N.J.pt_BR
unicamp.authorLeite, N.J., Institute of Computing, Unicamp - University of Campinas, Campinas, Brazilpt_BR, L.B., Department of Informatics, UTFPR - Federal University of Technology, Paraná, Curitiba, Brazilpt
dc.description.abstractMultiscale approaches have been largely considered in several signal processing applications. They play an important role when designing automatic methods to cope with real world measurements where, in most of the cases, there is no prior information about which would be the appropriate scale. The basic idea behind a multiscale analysis is to embed the original signal into a family of derived signals, thus allowing the analysis of different representation levels and, further, the choice of the ones exhibiting the interest features. This paper presents a brief survey of two broadly used multiscale formulations, namely, wavelets and scale-space filtering. We present the basic definitions and some possible applications of these approaches in image processing. © 2009 IEEE.en
dc.relation.ispartofTutorials of SIBGRAPI 2009 - 22nd Brazilian Symposium on Computer Graphics and Image Processingpt_BR
dc.identifier.citationTutorials Of Sibgrapi 2009 - 22nd Brazilian Symposium On Computer Graphics And Image Processing. , v. , n. , p. 31 - 44, 2009.pt_BR
dc.description.sponsorship1Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt_BR
dc.description.provenanceMade available in DSpace on 2015-06-26T13:34:25Z (GMT). No. of bitstreams: 1 2-s2.0-77949644641.pdf: 700448 bytes, checksum: 507c0c99fab83acfe78545b68e1c9751 (MD5) Previous issue date: 2009en
dc.description.provenanceMade available in DSpace on 2015-11-26T15:33:14Z (GMT). No. of bitstreams: 2 2-s2.0-77949644641.pdf: 700448 bytes, checksum: 507c0c99fab83acfe78545b68e1c9751 (MD5) 2-s2.0-77949644641.pdf.txt: 64578 bytes, checksum: 1dd98b2499ce234b956f420754cd72d3 (MD5) Previous issue date: 2009en
dc.description.referenceLindeberg, T., (1994) Scale-space theory in computer vision, , Kluwer Academic Publisherspt_BR
dc.description.referenceJackway, P., Morphological scale-space with application to three-dimensional object recognition, (1994), Ph.D. dissertation, Queensland University if TechnologyStrang, G., Nguyen, G., (1996) Wavelets and Filter Banks, , Wellesley Cambridge Presspt_BR
dc.description.referenceVelho, L., Gomes, J., Goldenstein, S., (1997) Wavelets: Teoria, Software e Aplicações, , IMPApt_BR
dc.description.referenceDaubechies, I., Ten Lectures on Wavelets (1992) C B M S - N S F Regional Conference Series in Applied Mathematics, , Soc for Industrial & Applied Mathpt_BR
dc.description.referenceWitkin, A.P., Scale-space filtering: A new approach to multiscale description (1984) Image Understanding, pp. 79-95. , Ablexpt_BR
dc.description.referenceBosworth, J., Acton, S., Morphological scale-space in image processing (2003) Digital Signal Processing, 13, pp. 338-367pt_BR
dc.description.referenceIijima, T., Basic theory of pattern normalization for the case of a typical one-dimensional pattern (1962) Bull. Electrotech. Lab, pp. 368-388pt_BR
dc.description.referenceJackway, P.T., Deriche, M., Scale-space properties of the multiscale morphological dilation-erosion (1996) IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, pp. 38-51pt_BR
dc.description.referenceHeijmans, H., van den Boomgaard, R., Algebraic framework for linear and morphological scale-spaces (2002) Journal of Visual Communication and Image Representation, 13, pp. 269-301pt_BR
dc.description.referenceBabaud, J., Baudin, W.A.P.M., Duda, R., Uniqueness of the gaussian kernel for scale-space filtering (1986) IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, pp. 15-25pt_BR
dc.description.referenceLindeberg, T., Discrete scale-space theory and the scale-space primal sketch, (1991), Ph.D. dissertation, Computational Vision and Active Perception Laboratory CVAP, Royal Institute of TechnologyLifshitz, L.M., Pizer, S.M., A multiresolution hierarchical approach to image segmentation based on intensity extrema (1990) IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (4), pp. 529-540pt_BR
dc.description.referenceKoenderink, J., The structure of images (1984) Biological Cybernetics, 50 (5), pp. 363-370pt_BR
dc.description.referenceFlorack, L., Non-linear scale-spaces isomorphic to the linear case with applications to scalar, vector and multispectral images (2001) International Journal of Computer Vision, 42 (1-2), pp. 39-53pt_BR
dc.description.referencePerona, P., Malik, J., Scale-space and edge detection using anisotropic diffusion (1990) IEEE Transactions on Pattern Analysis and Machine Intelligence, 42 (12), pp. 629-639pt_BR
dc.description.referenceMatheron, G., (1975) Random Sets and Integral Geometry, , John Wiley and Sonspt_BR
dc.description.referenceSerra, J., (1982) Image Analysis and Mathematical Morphology, , Academic Presspt_BR
dc.description.reference(1988) Image Analysis and Mathematical Morphology, volume 2: Theoretical Advances, , Academic Presspt_BR
dc.description.referenceSoille, P., (2003) Morphological Image Analysis: Principles and Applications, , Springer-Verlagpt_BR
dc.description.referenceHeijmans, H., (1994) Morphological Image Operators, , Academic Presspt_BR
dc.description.referenceLeite, N.J., Teixeira, M.D., An idempotent scale-space approach for morphological segmentation (2000) Mathematical Morphology and its Applications to Image and Signal Processing, pp. 291-300. , Kluwer Academic Publisherspt_BR
dc.description.referenceDorini, L.B., Leite, N.J., A scale-space toggle operator for morphological segmentation (2007) 8th ISMM, pp. 101-112pt_BR
dc.description.referenceKramer, H.P., Bruckner, J.B., Iterations of a non-linear transformation for enhancement of digital images (1975) Pattern Recognition, 7, pp. 53-58pt_BR
dc.description.referenceBernsen, J., Dynamic thresholding of grey-level images (1986) International Conference on Pattern Recognition, pp. 1251-1255pt_BR
dc.description.referenceSerra, J., Vicent, L., An overview of morphological filtering (1992) Circuits, Systems and Signal Processing, 11 (1), pp. 47-108pt_BR
dc.description.referenceMaragos, P., Meyer, F., A pde approach to nonlinear image simplification via levelings andreconstruction filters (2000) International Conference on Image Processing, pp. 938-941pt_BR
dc.description.referenceS. Beucher and F. Meyer, Mathematical Morphology in Image Processing. Marcel Dekker, 1993, ch. The morphological approach to segmentation: the watershed transformation, pp. 433-451Dorini, L.B., Leite, N.J., A multiscale operator for document image binarization (2009) 4th International Conference on Computer Vision Theory and Applications, pp. 34-39pt_BR
dc.description.referenceDorini, L.E.B., Simões, N.C., Leite, N.J., A scale-dependent morphological approach to motion segmentation (2007) IWSSIP, pp. 122-125pt_BR
dc.description.referenceSalembier, P., Serra, J., Flat zones filtering, connected operators, and filters by reconstruction (1995) IEEE Transactions on Image Processing, pp. 1153-1160pt_BR
dc.description.referenceSerra, J., Connections for sets and functions (2000) Fundamenta Informaticae, 41, pp. 147-186pt_BR
dc.description.referenceMeyer, F., Levelings, image simplification filters for segmentation (2004) Journal of Mathematical Imaging and Vision, 20 (1-2), pp. 59-72pt_BR
dc.description.referenceVicent, L., Morphological area openings and closings for grayscale images (1992) NATOWorkshoppt_BR
dc.description.referenceMallat, S., (1998) A wavelet tour of signal processing, , Academic Press Inc, San Diego, CApt_BR
dc.description.referenceStarck, J., Murtagh, F., Bijaoui, A., (1997) Image processing and data analysis: The multiscale approach, , IMPApt_BR
dc.description.referenceGoswami, J.C., Chan, A.K., (1999) Fundamentals of Wavelets, , John Wiley and Sonspt_BR
dc.description.referenceA. Cohen, I. Daubechies, and F. J.C., Biorthogonal bases of compactly supported wavelets, in Communications in Pure and Applied Mathematics, 1992, pp. 485-560Holschneider, M., Kronland-Martinet, R., Morlet, J., Grossmann, A., A real-time algorithm for signal analysis with the help of the wavelet transform (1989) Wavelets, Time-Frequency Methods and Phase Space, pp. 286-297. , J. M. Combes, A. Grossmann, and P. Tchamitchian, Eds. Springer-Verlagpt_BR
dc.description.referenceStollnitz, E.J., DeRose, T.D., Salesin, D.H., Wavelets for computer graphics: A primer, part 1 (1995), 15 (3), pp. 76-84. , MayKuntuu, I., Lepisto, L., Rauhamaa, J., Visa, A., Multiscale fourier descriptors for defect image retrieval (2006) Pattern Recognition Letters, 27, pp. 123-132pt_BR
dc.description.referenceGarcia, C., Zikos, G., Tziritas, G., A wavelet-based framework for face recognition (1998) ECCV, pp. 84-92pt_BR
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-77949644641.pdf684.03 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.