LEADER 00977nam a22002531i 4500 001 991001533709707536 005 20031119154604.0 008 040407s1963 fr |||||||||||||||||fre 035 $ab12784709-39ule_inst 035 $aARCHE-076433$9ExL 040 $aDip.to Scienze Storiche$bita$cA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l. 082 04$a307.336 100 1 $aGirard, Raphaël$0484577 245 14$aLes Indiens de l'Amazonie péruvienne /$cRaphaël Girard ; traduit de l'espagnol par R. Siret 260 $aParis :$bPayot,$c1963 300 $a308 p. ill. ;$c22 cm 440 0$aBibliothèque scientifique 650 4$aIndiani del Sud America 907 $a.b12784709$b02-04-14$c16-04-04 912 $a991001533709707536 945 $aLE009 STOR.96-26$g1$i2009000365667$lle009$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i13329029$z16-04-04 996 $aIndiens de l'Amazonie péruvienne$9292885 997 $aUNISALENTO 998 $ale009$b16-04-04$cm$da $e-$ffre$gfr $h4$i1 LEADER 01732nas 2200529-a 450 001 996202381503316 005 20240413021633.0 035 $a(CKB)963018278154 035 $a(CONSER)---94649633- 035 $a(EXLCZ)99963018278154 100 $a19931022b19uu2006 --- a 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aIndustrial paint & powder 210 $aCarol Stream, IL $cHitchcock Pub. Co.$d©1993- 215 $a1 online resource 300 $aTitle from cover. 311 $aPrint version: Industrial paint & powder. (DLC) 94649633 (DLC)sn 93005289 (OCoLC)29180638 1073-4651 517 3 $aIndustrial paint and powder 517 3 $aPaint & powder 517 3 $aPaint and powder 531 $aINDUSTRIAL PAINT AND POWDER COATINGS MANUFACTURING AND APPLICATION 531 $aIND PAINT POWDER 531 $aINDUSTRIAL PAINT AND POWDER 531 0 $aInd. paint powder 606 $aPainting, Industrial$vPeriodicals 606 $aPlastic coating$vPeriodicals 606 $aPlastic powders$vPeriodicals 606 $aPainting, Industrial$2fast$3(OCoLC)fst01050886 606 $aPlastic coating$2fast$3(OCoLC)fst01066448 606 $aPlastic powders$2fast$3(OCoLC)fst01066501 608 $aPeriodicals.$2fast 608 $aPeriodicals.$2lcgft 615 0$aPainting, Industrial 615 0$aPlastic coating 615 0$aPlastic powders 615 7$aPainting, Industrial. 615 7$aPlastic coating. 615 7$aPlastic powders. 676 $a667/.9/05 906 $aJOURNAL 912 $a996202381503316 920 $aexl_impl conversion 996 $aIndustrial paint & powder$92181858 997 $aUNISA LEADER 04494nam 22006015 450 001 9910337576903321 005 20200630071138.0 010 $a3-030-04040-2 024 7 $a10.1007/978-3-030-04040-6 035 $a(CKB)4100000007335016 035 $a(DE-He213)978-3-030-04040-6 035 $a(MiAaPQ)EBC5628152 035 $a(PPN)233798854 035 $a(EXLCZ)994100000007335016 100 $a20190101d2019 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHierarchical Perceptual Grouping for Object Recognition $eTheoretical Views and Gestalt-Law Applications /$fby Eckart Michaelsen, Jochen Meidow 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XI, 195 p. 100 illus., 9 illus. in color.) 225 1 $aAdvances in Computer Vision and Pattern Recognition,$x2191-6586 311 $a3-030-04039-9 327 $aIntroduction -- Reflection Symmetry -- Good Continuation in Rows or Frieze Symmetry -- Rotational Symmetry -- Closure ? Hierarchies of Gestalten -- Search -- Illusions -- Prolongation in Good Continuation -- Parallelism and Rectangularity -- Lattice Gestalten -- Primitive Extraction -- Knowledge and Gestalt Interaction -- Learning -- Appendix A: General Adjustment Model with Constraints. 330 $aThis unique text/reference presents a unified approach to the formulation of Gestalt laws for perceptual grouping, and the construction of nested hierarchies by aggregation utilizing these laws. The book also describes the extraction of such constructions from noisy images showing man-made objects and clutter. Each Gestalt operation is introduced in a separate, self-contained chapter, together with application examples and a brief literature review. These are then brought together in an algebraic closure chapter, followed by chapters that connect the method to the data ? i.e., the extraction of primitives from images, cooperation with machine-readable knowledge, and cooperation with machine learning. Topics and features: Offers the first unified approach to nested hierarchical perceptual grouping Presents a review of all relevant Gestalt laws in a single source Covers reflection symmetry, frieze symmetry, rotational symmetry, parallelism and rectangular settings, contour prolongation, and lattices Describes the problem from all theoretical viewpoints, including syntactic, probabilistic, and algebraic perspectives Discusses issues important to practical application, such as primitive extraction and any-time search Provides an appendix detailing a general adjustment model with constraints This work offers new insights and proposes novel methods to advance the field of machine vision, which will be of great benefit to students, researchers, and engineers active in this area. Dr.-Ing. Eckart Michaelsen is a researcher at the Object Recognition Department of Fraunhofer IOSB, Ettlingen, Germany. Dr.-Ing. Jochen Meidow is a researcher at the Scene Analysis Department of the same institution. 410 0$aAdvances in Computer Vision and Pattern Recognition,$x2191-6586 606 $aPattern perception 606 $aRemote sensing 606 $aArchitecture 606 $aGroup theory 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aRemote Sensing/Photogrammetry$3https://scigraph.springernature.com/ontologies/product-market-codes/J13010 606 $aArchitecture, general$3https://scigraph.springernature.com/ontologies/product-market-codes/K0000X 606 $aGroup Theory and Generalizations$3https://scigraph.springernature.com/ontologies/product-market-codes/M11078 615 0$aPattern perception. 615 0$aRemote sensing. 615 0$aArchitecture. 615 0$aGroup theory. 615 14$aPattern Recognition. 615 24$aRemote Sensing/Photogrammetry. 615 24$aArchitecture, general. 615 24$aGroup Theory and Generalizations. 676 $a006.4 676 $a006.4 700 $aMichaelsen$b Eckart$4aut$4http://id.loc.gov/vocabulary/relators/aut$01060300 702 $aMeidow$b Jochen$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910337576903321 996 $aHierarchical Perceptual Grouping for Object Recognition$92512317 997 $aUNINA