LEADER 05310nam 22006975 450 001 9910789220103321 005 20220126105158.0 010 $a1-4613-9777-4 024 7 $a10.1007/978-1-4613-9777-9 035 $a(CKB)3400000000093667 035 $a(SSID)ssj0000935118 035 $a(PQKBManifestationID)11575536 035 $a(PQKBTitleCode)TC0000935118 035 $a(PQKBWorkID)10949571 035 $a(PQKB)10908766 035 $a(DE-He213)978-1-4613-9777-9 035 $a(MiAaPQ)EBC3079950 035 $a(EXLCZ)993400000000093667 100 $a20121227d1990 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 12$aA taxonomy for texture description and identification /$fA. Ravishankar Rao 205 $a1st ed. 1990. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d1990. 215 $a1 online resource (XXIII, 198 p.) 225 1 $aSpringer Series in Perception Engineering 300 $a"With 79 Illustrations." 311 $a1-4613-9779-0 320 $aIncludes bibliographical references and index. 327 $a1 Introduction -- 1.1 Scope of the book -- 1.2 Importance of texture -- 1.3 Potential applications of this research -- 1.4 Issues in automated process control involving computer vision -- 1.5 A taxonomy for texture -- 1.6 Outline -- 2 Computing oriented texture fields -- 2.1 Introduction -- 2.2 Background -- 2.3 Oriented Texture Fields -- 2.4 Experimental Methods -- 2.5 Experimental Results -- 2.6 Analyzing texture at different scales -- 2.7 Processing of the intrinsic images -- 2.8 Conclusions -- 3 The analysis of oriented textures through phase portraits -- 3.1 Introduction -- 3.2 Background -- 3.3 Geometric theory of differential equations -- 3.4 Experimental Methods -- 3.5 Experimental Results -- 3.6 Experiments with noise addition -- 3.7 A related model from fluid flow analysis -- 3.8 Discussion -- 3.9 Conclusion -- 4 Analyzing strongly ordered textures -- 4.1 Introduction -- 4.2 Extraction of primitives -- 4.3 Extracting structure from primitives -- 4.4 Models for strongly ordered textures -- 4.5 Symbolic descriptions: models from petrography -- 4.6 Frieze groups and wallpaper groups -- 4.7 Implications for computer vision -- 4.8 Summary -- 5 Disordered textures -- 5.1 Statistical measures for disordered textures -- 5.2 Describing disordered textures by means of the fractal dimension -- 5.3 Computing the fractal dimension -- 5.4 Experimental Results -- 5.5 Conclusion -- 6 Compositional textures -- 6.1 Introduction -- 6.2 Primitive textures -- 6.3 A Parametrized symbol set -- 6.4 Three types of composition -- 6.5 Linear combination (transparent overlap) -- 6.6 Functional composition -- 6.7 Opaque overlap -- 6.8 Definition of texture -- 6.9 A complete taxonomy for texture -- 6.10 Implementing the taxonomy -- 6.11 Conclusion -- 7 Conclusion -- 7.1 Summary of results -- 7.2 Contributions -- 7.3 Future Work -- B Region Refinement -- C Preparation of the manuscript -- Permissions. 330 $aA central issue in computer vision is the problem of signal to symbol transformation. In the case of texture, which is an important visual cue, this problem has hitherto received very little attention. This book presents a solution to the signal to symbol transformation problem for texture. The symbolic de- scription scheme consists of a novel taxonomy for textures, and is based on appropriate mathematical models for different kinds of texture. The taxonomy classifies textures into the broad classes of disordered, strongly ordered, weakly ordered and compositional. Disordered textures are described by statistical mea- sures, strongly ordered textures by the placement of primitives, and weakly ordered textures by an orientation field. Compositional textures are created from these three classes of texture by using certain rules of composition. The unifying theme of this book is to provide standardized symbolic descriptions that serve as a descriptive vocabulary for textures. The algorithms developed in the book have been applied to a wide variety of textured images arising in semiconductor wafer inspection, flow visualization and lumber processing. The taxonomy for texture can serve as a scheme for the identification and description of surface flaws and defects occurring in a wide range of practical applications. 410 0$aSpringer Series in Perception Engineering 606 $aComputer vision 606 $aComputer simulation 606 $aComputers 606 $aSoftware engineering 606 $aComputer Vision 606 $aComputer Modelling 606 $aComputer Hardware 606 $aSoftware Engineering 615 0$aComputer vision. 615 0$aComputer simulation. 615 0$aComputers. 615 0$aSoftware engineering. 615 14$aComputer Vision. 615 24$aComputer Modelling. 615 24$aComputer Hardware. 615 24$aSoftware Engineering. 676 $a006.6 676 $a006.37 700 $aRavishankar Rao$b A.$0961283 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910789220103321 996 $aA taxonomy for texture description and identification$93840230 997 $aUNINA