Open AccessBook
Geometric Description of Images as Topographic Maps
Vicent Caselles,Pascal Monasse +1 more
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TLDR
This volume discusses the basic geometric contents of an image and presents a tree data structure to handle those contents efficiently and examines grain filters, morphological operators simplifying these geometric contents, and several applications to image comparison and registration and to edge and corner detection.Abstract:
This volume discusses the basic geometric contents of an image and presents a tree data structure to handle those contents efficiently. The nodes of the tree are derived from connected components of level sets of the intensity, while the edges represent inclusion information. Grain filters, morphological operators simplifying these geometric contents, are analyzed and several applications to image comparison and registration, and to edge and corner detection, are presented.The mathematically inclined reader may be most interested in Chapters 2 to 6, which generalize the topological Morse description to continuous or semicontinuous functions, while mathematical morphologists may more closely consider grain filters in Chapter 3. Computer scientists will find algorithmic considerations in Chapters 6 and 7, the full justification of which may be found in Chapters 2 and 4 respectively. Lastly, all readers can learn more about the motivation for this work in the image processing applications presented in Chapter 8.read more
Citations
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Journal ArticleDOI
A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles
TL;DR: The main objective of this survey paper is to recall the concept of the APs along with all its modifications and generalizations with special emphasis on remote sensing image classification and summarize the important aspects of its efficient utilization while also listing potential future works.
Journal ArticleDOI
Classification of Remote Sensing Optical and LiDAR Data Using Extended Attribute Profiles
Mattia Pedergnana,Prashanth Reddy Marpu,M. Dalla Mura,Jon Atli Benediktsson,Lorenzo Bruzzone +4 more
TL;DR: This paper proposes a technique performing a classification of the features extracted with EAPs computed on both optical and LiDAR images, leading to a fusion of the spectral, spatial and elevation data.
Journal ArticleDOI
Shape-based Invariant Texture Indexing
TL;DR: A new texture analysis scheme is introduced, which is invariant to local geometric and radiometric changes, and the obtained experimental results outperform the current state of the art in locally invariant texture analysis.
Journal ArticleDOI
Automatic Framework for Spectral–Spatial Classification Based on Supervised Feature Extraction and Morphological Attribute Profiles
TL;DR: A framework for automatic spectral-spatial classification of hyperspectral images is proposed and, in order to reduce the redundancy of features and address the so-called curse of dimensionality, different supervised feature extraction techniques are considered.
Book ChapterDOI
A quasi-linear algorithm to compute the tree of shapes of n-D images
TL;DR: A simple-to-write algorithm to compute the tree of shapes is proposed that works for nD images and has a quasi-linear complexity when data quantization is low, typically 12 bits or less.