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010   $a3-540-47634-2
024 7 $a10.1007/BFb0017553
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035   $a(PQKBTitleCode)TC0000326175
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035   $a(DE-He213)978-3-540-47634-4
035   $a(PPN)155200135
035   $a(EXLCZ)991000000000233960
100   $a20121227d1993      u|             0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aRecognizing Planar Objects Using Invariant Image Features$b[electronic resource] /$fby Thomas H. Reiss
205   $a1st ed. 1993.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1993.
215   $a1 online resource (X, 186 p.) 
225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v676
300   $aBibliographic Level Mode of Issuance: Monograph
311   $a3-540-56713-5 
327   $aTranslation, rotation, scale and contrast invariants -- Algebraic and projective invariants -- Invariance to affine transformations -- Invariance to projective transformations -- Recognizing partially occluded objects -- Summary and conclusions.
330   $aGiven a familiar object extracted from its surroundings, we humans have little difficulty in recognizing it irrespective of its size, position and orientation in our field of view. Changes in lighting and the effects of perspective also pose no problems. How do we achieve this, and more importantly, how can we get a computer to do this? One very promising approach is to find mathematical functions of an object's image, or of an object's 3D description, that are invariant to the transformations caused by the object's motion. This book is devoted to the theory and practice of such invariant image features, so-called image invariants, for planar objects. It gives a comprehensive summary of the field, discussing methods for recognizing both occluded and partially occluded objects, and also contains a definitive treatmentof moment invariants and a tutorial introduction to algebraic invariants, which are fundamental to affine moment invariants and to many projective invariants. A number of novel invariant functions are presented and the results of numerous experiments investigating the stability of new and old invariants are discussed. The main conclusion is that moment invariants are very effective, both for partially occluded objects and for recognizing objects in grey-level images.
410  0$aLecture Notes in Computer Science,$x0302-9743 ;$v676
606   $aComputer graphics
606   $aOptical data processing
606   $aPattern recognition
606   $aGeometry
606   $aComputer Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22013
606   $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021
606   $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
606   $aGeometry$3https://scigraph.springernature.com/ontologies/product-market-codes/M21006
615  0$aComputer graphics.
615  0$aOptical data processing.
615  0$aPattern recognition.
615  0$aGeometry.
615 14$aComputer Graphics.
615 24$aImage Processing and Computer Vision.
615 24$aPattern Recognition.
615 24$aGeometry.
676   $a006.4/2
700   $aReiss$b Thomas H$4aut$4http://id.loc.gov/vocabulary/relators/aut$0714560
906   $aBOOK
912   $a996466083603316
996   $aRecognizing planar objects using invariant image features$91381885
997   $aUNISA