Automating the Analysis of Spatial Grids - Lakshmanan, Valliappa

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Résumé :
Automated Analysis of Spatial Grids: Motivation and Challenges.-
-Geographic Information Systems.-
-GIS Operations.-
-Need for Automation.-
-Spatial Grids.-
-Challenges in Automated Analysis.-
-Spatial Data Mining Algorithms.-
Geospatial grids.-
-Representation.-
-Linearity of data values.-
-Instrument geometry.-
-Gridding point observations.-
-Rasterization.-
-Example Applications.-
Data Structures for Spatial Grids.-
-Array.-
-Pixels.-
-Level set.-
-Topographical surface.-
-Markov chain.-
-Matrix.-
-Parametric approximation.-
-Relational structure.-
-Applications.-
Global and Local Image Statistics.-
-Types of statistics.-
-Distances.-
-Distance transform.-
-Probability Functions.-
-Local measures.-
Neighborhood and Window Operations.-
-Preprocessing.-
-Window operations.-
-Median filter.-
-Morphological operations.-
-Skeletonization.-
-Frequency Domain Convolution.-
Identifying Objects.-
-Object identification.-
-Region growing.-
-Region properties.-
-Hysteresis.-
-Active contours.-
-Watershed Transform.-
-Enhanced watershed.-
-Contiguity-enhanced Clustering.-
-Choosing an object-identification technique.-
Change and Motion Estimation.-
-Estimating change.-
-Optical Flow.-
-Object-tracking.-
-Choosing a change or motion estimation technique.-
Data Mining Attributes from Spatial Grids.-
-Data Mining.-
-A Fuzzy Logic Application.-
-Supervised learning models.-
-Clustering.-
-Example Applications.
...

Biographie:
Automated Analysis of Spatial Grids: Motivation and Challenges.-
-Geographic Information Systems.- -GIS Operations.- -Need for Automation.- -Spatial Grids.- -Challenges in Automated Analysis.- -Spatial Data Mining Algorithms.- Geospatial grids.- -Representation.- -Linearity of data values.- -Instrument geometry.- -Gridding point observations.- -Rasterization.- -Example Applications.- Data Structures for Spatial Grids.- -Array.- -Pixels.- -Level set.- -Topographical surface.- -Markov chain.- -Matrix.- -Parametric approximation.- -Relational structure.- -Applications.- Global and Local Image Statistics.- -Types of statistics.- -Distances.- -Distance transform.- -Probability Functions.- -Local measures.- Neighborhood and Window Operations.- -Preprocessing.- -Window operations.- -Median filter.- -Morphological operations.- -Skeletonization.- -Frequency Domain Convolution.- Identifying Objects.- -Object identification.- -Region growing.- -Region properties.- -Hysteresis.- -Active contours.- -Watershed Transform.- -Enhanced watershed.- -Contiguity-enhanced Clustering.- -Choosing an object-identification technique.- Change and Motion Estimation.- -Estimating change.- -Optical Flow.- -Object-tracking.- -Choosing a change or motion estimation technique.- Data Mining Attributes from Spatial Grids.- -Data Mining.- -A Fuzzy Logic Application.- -Supervised learning models.- -Clustering.- -Example Applications....

Sommaire:
Dr. Valliappa Lakshmanan is an expert in machine intelligence R&D for meteorological applications, and in designing and developing large-scale software systems. He is skilled in communicating technical and non-technical material to diverse audiences. He has studied at the Indian Institute of Technology in Madras, the Ohio State University in Columbus and the University of Oklahoma. Dr. Lakshmanan is currently employed as a Research Scientist at CIMMS, being the technical lead on several software projects and research groups. He also develops automated real-time pattern recognition algorithms and visualization techniques for weather phenomena. He has (co-)written many journal articles....