LEADER 05463nam 22008535 450 001 996466380403316 005 20200706131821.0 010 $a3-540-68481-6 024 7 $a10.1007/978-3-540-68481-7 035 $a(CKB)1000000000437226 035 $a(SSID)ssj0000320394 035 $a(PQKBManifestationID)11286215 035 $a(PQKBTitleCode)TC0000320394 035 $a(PQKBWorkID)10257805 035 $a(PQKB)11209664 035 $a(DE-He213)978-3-540-68481-7 035 $a(MiAaPQ)EBC3063188 035 $a(MiAaPQ)EBC6283122 035 $a(PPN)128124458 035 $a(EXLCZ)991000000000437226 100 $a20100301d2008 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 12$aA Theory of Shape Identification$b[electronic resource] /$fby Frédéric Cao, José-Luis Lisani, Jean-Michel Morel, Pablo Musé, Frédéric Sur 205 $a1st ed. 2008. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2008. 215 $a1 online resource (XII, 264 p. 171 illus., 12 illus. in color.) 225 1 $aLecture Notes in Mathematics,$x0075-8434 ;$v1948 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-68480-8 320 $aIncludes bibliographical references and index. 327 $aExtracting 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. 330 $aRecent years have seen dramatic progress in shape recognition algorithms applied to ever-growing image databases. They have been applied to image stitching, stereo vision, image mosaics, solid object recognition and video or web image retrieval. More fundamentally, the ability of humans and animals to detect and recognize shapes is one of the enigmas of perception. The book describes a complete method that starts from a query image and an image database and yields a list of the images in the database containing shapes present in the query image. A false alarm number is associated to each detection. Many experiments will show that familiar simple shapes or images can reliably be identified with false alarm numbers ranging from 10-5 to less than 10-300. Technically speaking, there are two main issues. The first is extracting invariant shape descriptors from digital images. The second is deciding whether two shape descriptors are identifiable as the same shape or not. A perceptual principle, the Helmholtz principle, is the cornerstone of this decision. These decisions rely on elementary stochastic geometry and compute a false alarm number. The lower this number, the more secure the identification. The description of the processes, the many experiments on digital images and the simple proofs of mathematical correctness are interlaced so as to make a reading accessible to various audiences, such as students, engineers, and researchers. 410 0$aLecture Notes in Mathematics,$x0075-8434 ;$v1948 606 $aGeometry 606 $aMathematics 606 $aVisualization 606 $aOptical data processing 606 $aArtificial intelligence 606 $aGame theory 606 $aGeometry$3https://scigraph.springernature.com/ontologies/product-market-codes/M21006 606 $aVisualization$3https://scigraph.springernature.com/ontologies/product-market-codes/M14034 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 606 $aGame Theory, Economics, Social and Behav. 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