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E-raamat: Mathematical Morphology and Its Applications to Image and Signal Processing

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Mathematical Morphology and Its Applications to Image and Signal ProcessingSuurem pilt
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A compilation of papers presented at the 1996 Int'l Symposium on Mathematical Morphology, providing a sampling from the current developments in the field of quantitative analysis of geometrical structures. The authors examine theory, connectivity, filtering, nonlinear systems related to morphology, algorithms and architectures, granulometries and texture, segmentation, imagine sequence analysis, learning and document analysis, and applications. Annotation c. by Book News, Inc., Portland, Or.

Mathematical morphology (MM) is a powerful methodology for the quantitative analysis of geometrical structures. It consists of a broad and coherent collection of theoretical concepts, nonlinear signal operators, and algorithms aiming at extracting, from images or other geometrical objects, information related to their shape and size. Its mathematical origins stem from set theory, lattice algebra, and integral and stochastic geometry.
MM was initiated in the late 1960s by G. Matheron and J. Serra at the Fontainebleau School of Mines in France. Originally it was applied to analyzing images from geological or biological specimens. However, its rich theoretical framework, algorithmic efficiency, easy implementability on special hardware, and suitability for many shape- oriented problems have propelled its widespread diffusion and adoption by many academic and industry groups in many countries as one among the dominant image analysis methodologies.
The purpose of Mathematical Morphology and its Applications to Image and Signal Processing is to provide the image analysis community with a sampling from the current developments in the theoretical (deterministic and stochastic) and computational aspects of MM and its applications to image and signal processing. The book consists of the papers presented at the ISMM'96 grouped into the following themes:
  • Theory
  • Connectivity
  • Filtering
  • Nonlinear System Related to Morphology
  • Algorithms/Architectures
  • Granulometries, Texture
  • Segmentation
  • Image Sequence Analysis
  • Learning
  • Document Analysis
  • Applications
Theory.- Metric Convexity in the Context of Mathematical Morphology.-
Support Function and Minkowski Addition of Non-Convex Sets.- Lattice
Operators Underlying Dynamic Systems.- Comparison of Multiscale Morphology
Approaches: PDE Implemented via Curve Evolution versus Chamfer Distance
Transforms.- An Attribute-Based Approach to Mathematical Morphology.-
Spatially-Variant Mathematical Morphology: Minimal Basis Representation.- The
Generalized Tailor Problem.- Discrete Random Functions: Modeling and Analysis
Using Mathematical Morphology.- Morphological Sampling of Random Closed
Sets.- Connectivity.- Connectivity on complete lattices.- Practical
Extensions of Connected Operators.- Region Adjacency Graphs and Connected
Morphological Operators.- Space Connectivity and Translation-Invariance.-
Filtering.- Morphological Filters for Dummies.- Alternating Sequential
Filters by Adaptive-Neighborhood Structuring Functions.- Quadratic
Structuring Functions in Mathematical Morphology.- MRL-Filters and their
Adaptive Optimal Design for Image Processing.- Weighted Composite
Order-Statistics Filters: Optimal Morphological Pattern Recognition.-
Nonlinear Systems Related to Morphology.- Links Between Mathematical
Morphology, Rough Sets, Fuzzy Logic and Higher Order Neural Networks.-
Grey-Scale Soft Morphological Filter Optimization by Genetic Algorithms.-
Soft Morphological Operators Based on Nonlinear Lp Mean Operators.- The
Viterbi Optimal Runlength-Constrained Approximation Nonlinear Filter.-
Algorithms, Architectures.- Recursive Morphology using Line Structuring
Elements.- A Morphological Algorithm for Linear Segment Detection.- Toward
the Optimal Decomposition of Arbitrarily Shaped Structuring Elements by Means
of a Genetic Approach.- A Data Dependent Architecture Based on Seeded Region
Growing Strategy for Advanced Morphological Operators.- Implementing
Morphological Image Operators via Trained Neural Networks.- Granulometries,
Texture.- Optimal and Adaptive Design of Reconstructive Granulometric
Filters.- Periodic Lines and Their Application to Granulometries.- Local
Grayscale Granulometries Based on Opening Trees.- Integrating Size
Information into Intensity Histogram.- Probabilistic Model of Rough Surfaces
Obtained by Electro-Erosion.- A Textural Analysis by Mathematical
Morphology.- Segmentation.- Computation of Watersheds Based on Parallel Graph
Algorithms.- Segmentation Algorithm by Multicriteria Region Merging.-
Temporal Stability in Sequence Segmentation using the Watershed Algorithm.-
The Dynamics of Minima and Contours.- A Morphological Interpolation Method
for Mosaic Images.- Image Sequence Analysis.- Multivalued Morphology and its
Application in Moving Object Segmentation and Tracking.- Mathematical
Morphology for Image Sequences using the Knowledge of Dynamics.- Motion
Picture Restoration Using Morphological Tools.- Segmentation-based
Morphological Interpolation of Partition Sequences.- Learning, Document
Analysis.- Set Operations on Closed Intervals and their Applications to the
Automatic Programming of MMachs.- Automatic Programming of MMachs for OCR.-
Morphological Preprocessing and Binarization for OCR Systems.- Adaptive
Directional Morphology with Application to Document Analysis.- Applications.-
Segmentation of 3D Pulmonary Trees Using Mathematical Morphology.- Automatic
3-Dimensional Segmentation of MR Brain Tissue using Filters by
Reconstruction.- Watershed Analysis and Relaxation Labelling: A Cooperative
Approach for the Interpretation of Cranial-MR Images Using a Statistical
Digital Atlas.- Robust Extraction of AxonFibers from Large-scale Electron
Micrograph Mosaics.- Strong Edge Features for Image Coding.- Water Depth
Determination using Mathematical Morphology.- Geometrical and Topological
Characterization of Cork Cells by Digital Image Analysis.- Author Index.
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