A Theory of Shape Identification

Recent years have seen dramatic progress in shape recognition algorithms applied to ever-growing image databases. They have been applied to image stitching, stereo vision, image mosaics, solid object recognition and video or web image retrieval. More fundamentally, the ability of humans and animals to detect and recognize shapes is one of the enigmas of perception. The book describes a complete method that starts from a query image and an image database and yields a list of the images in the database containing shapes present in the query image. A false alarm number is associated to each detection. Many experiments will show that familiar simple shapes or images can reliably be identified with false alarm numbers ranging from 10^-5 to less than 10^-300. Technically speaking, there are two main issues. The first is extracting invariant shape descriptors from digital images. Indeed, a shape can be seen from various angles and distances and in various lights.

Table des matières

Extracting Image boundaries.- Extracting Meaningful Curves from Images.- Level Line Invariant Descriptors.- Robust Shape Directions.- Invariant Level Line Encoding.- Recognizing Level Lines.- A Contrario Decision: the LLD Method.- Meaningful Matches: Experiments on LLD and MSER.- Grouping Shape Elements.- Hierarchical Clustering and Validity Assessment.- Grouping Spatially Coherent Meaningful Matches.- Experimental Results.- The SIFT Method.- The SIFT Method.- Securing SIFT with A Contrario Techniques.

Résumé

Recent years have seen dramatic progress in shape recognition algorithms applied to ever-growing image databases. They have been applied to image stitching, stereo vision, image mosaics, solid object recognition and video or web image retrieval. More fundamentally, the ability of humans and animals to detect and recognize shapes is one of the enigmas of perception. The book describes a complete method that starts from a query image and an image database and yields a list of the images in the database containing shapes present in the query image. A false alarm number is associated to each detection. Many experiments will show that familiar simple shapes or images can reliably be identified with false alarm numbers ranging from 10^-5 to less than 10^-300. Technically speaking, there are two main issues. The first is extracting invariant shape descriptors from digital images. Indeed, a shape can be seen from various angles and distances and in various lights.

Détails du produit

• Auteurs: Frédéric Cao, José-Luis Lisani, Jean-Michel Morel, Pablo Musé, Frédéric Sur
• Edition: Springer, Berlin
• Langues: Anglais
• Format d'édition: Livre de poche
• Sortie: 28.08.2008
• EAN: 9783540684800
• ISBN: 978-3-540-68480-0
• Pages: 264
• Dimensions: 158 mm x 239 mm x 15 mm
• Poids: 430 g
• Illustrations: XII, 264 p. 171 illus., 12 illus. in color.
• Thème: Lecture Notes in Mathematics
• Catégories: Sciences naturelles, médecine, informatique, technique > Mathématiques > Géométrie; Spieltheorie, B, Informationsvisualisierung, Künstliche Intelligenz, Artificial Intelligence, Maschinelles Sehen, Bildverstehen, Bildverarbeitung, Mathematics, geometry, Computer Vision, Visualization, Image Processing and Computer Vision, Game Theory, Economics, Social and Behav. Sciences, Mathematics and Statistics, Philosophy of Mathematics, Computer Imaging, Vision, Pattern Recognition and Graphics, game theory, Combinatorics & graph theory, Data and Information Visualization, Optical data processing, Image processing