LEADER 00819nam0-22002891i-450- 001 990007939210403321 005 20051125142208.0 035 $a000793921 035 $aFED01000793921 035 $a(Aleph)000793921FED01 035 $a000793921 100 $a20041028d1972----km-y0itay50------ba 101 0 $aeng 105 $aa---a---001yy 200 1 $aDeformation processing$fby Walter A. Backofen 210 $aReading (Mass)$cAddison-Wesley$dc1972 215 $a326 p.$cill.$d25 cm 225 1 $aAddison-Wesley series in metallurgy and materials 676 $a671.3 700 1$aBackofen,$bWalter A.$0289861 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990007939210403321 952 $a14 O-011$b1412$fDINMP 959 $aDINMP 996 $aDeformation processing$9749221 997 $aUNINA LEADER 05606nam 22007093u 450 001 9910465307503321 005 20210114232904.0 010 $a1-118-84873-X 035 $a(CKB)2560000000147408 035 $a(EBL)1659273 035 $a(SSID)ssj0001212511 035 $a(PQKBManifestationID)11788048 035 $a(PQKBTitleCode)TC0001212511 035 $a(PQKBWorkID)11210777 035 $a(PQKB)11568791 035 $a(CaSebORM)9781118848739 035 $a(MiAaPQ)EBC1659273 035 $a(EXLCZ)992560000000147408 100 $a20140407d2014|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 12$aA Practical Introduction to Computer Vision with OpenCV$b[electronic resource] 205 $a1st edition 210 $aHoboken $cWiley$d2014 215 $a1 online resource (235 p.) 300 $aDescription based upon print version of record. 311 $a1-118-84845-4 320 $aIncludes bibliographical references and index. 327 $aA Practical Introduction to Computer Vision with OpenCV; Contents; Preface; 1 Introduction; 1.1 A Difficult Problem; 1.2 The Human Vision System; 1.3 Practical Applications of Computer Vision; 1.4 The Future of Computer Vision; 1.5 Material in This Textbook; 1.6 Going Further with Computer Vision; 2 Images; 2.1 Cameras; 2.1.1 The Simple Pinhole Camera Model; 2.2 Images; 2.2.1 Sampling; 2.2.2 Quantisation; 2.3 Colour Images; 2.3.1 Red-Green-Blue (RGB) Images; 2.3.2 Cyan-Magenta-Yellow (CMY) Images; 2.3.3 YUV Images; 2.3.4 Hue Luminance Saturation (HLS) Images; 2.3.5 Other Colour Spaces 327 $a2.3.6 Some Colour Applications2.4 Noise; 2.4.1 Types of Noise; 2.4.2 Noise Models; 2.4.3 Noise Generation; 2.4.4 Noise Evaluation; 2.5 Smoothing; 2.5.1 Image Averaging; 2.5.2 Local Averaging and Gaussian Smoothing; 2.5.3 Rotating Mask; 2.5.4 Median Filter; 3 Histograms; 3.1 1D Histograms; 3.1.1 Histogram Smoothing; 3.1.2 Colour Histograms; 3.2 3D Histograms; 3.3 Histogram/Image Equalisation; 3.4 Histogram Comparison; 3.5 Back-projection; 3.6 k-means Clustering; 4 Binary Vision; 4.1 Thresholding; 4.1.1 Thresholding Problems; 4.2 Threshold Detection Methods; 4.2.1 Bimodal Histogram Analysis 327 $a4.2.2 Optimal Thresholding4.2.3 Otsu Thresholding; 4.3 Variations on Thresholding; 4.3.1 Adaptive Thresholding; 4.3.2 Band Thresholding; 4.3.3 Semi-thresholding; 4.3.4 Multispectral Thresholding; 4.4 Mathematical Morphology; 4.4.1 Dilation; 4.4.2 Erosion; 4.4.3 Opening and Closing; 4.4.4 Grey-scale and Colour Morphology; 4.5 Connectivity; 4.5.1 Connectedness: Paradoxes and Solutions; 4.5.2 Connected Components Analysis; 5 Geometric Transformations; 5.1 Problem Specification and Algorithm; 5.2 Affine Transformations; 5.2.1 Known Affine Transformations; 5.2.2 Unknown Affine Transformations 327 $a5.3 Perspective Transformations5.4 Specification of More Complex Transformations; 5.5 Interpolation; 5.5.1 Nearest Neighbour Interpolation; 5.5.2 Bilinear Interpolation; 5.5.3 Bi-Cubic Interpolation; 5.6 Modelling and Removing Distortion from Cameras; 5.6.1 Camera Distortions; 5.6.2 Camera Calibration and Removing Distortion; 6 Edges; 6.1 Edge Detection; 6.1.1 First Derivative Edge Detectors; 6.1.2 Second Derivative Edge Detectors; 6.1.3 Multispectral Edge Detection; 6.1.4 Image Sharpening; 6.2 Contour Segmentation; 6.2.1 Basic Representations of Edge Data; 6.2.2 Border Detection 327 $a6.2.3 Extracting Line Segment Representations of Edge Contours6.3 Hough Transform; 6.3.1 Hough for Lines; 6.3.2 Hough for Circles; 6.3.3 Generalised Hough; 7 Features; 7.1 Moravec Corner Detection; 7.2 Harris Corner Detection; 7.3 FAST Corner Detection; 7.4 SIFT; 7.4.1 Scale Space Extrema Detection; 7.4.2 Accurate Keypoint Location; 7.4.3 Keypoint Orientation Assignment; 7.4.4 Keypoint Descriptor; 7.4.5 Matching Keypoints; 7.4.6 Recognition; 7.5 Other Detectors; 7.5.1 Minimum Eigenvalues; 7.5.2 SURF; 8 Recognition; 8.1 Template Matching; 8.1.1 Applications; 8.1.2 Template Matching Algorithm 327 $a8.1.3 Matching Metrics 330 $aExplains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries Computer Vision is a rapidly expanding area and it is becoming progressively easier for developers to make use of this field due to the ready availability of high quality libraries (such as OpenCV 2). This text is intended to facilitate the practical use of computer vision with the goal being to bridge the gap between the theory and the practical implementation of computer vision. The book will explain how to use the re 606 $aComputer vision -- Computer programs 606 $aComputer vision 606 $aComputer vision$xComputer programs 606 $aComputer vision 606 $aEngineering & Applied Sciences$2HILCC 606 $aApplied Physics$2HILCC 608 $aElectronic books. 615 4$aComputer vision -- Computer programs. 615 4$aComputer vision. 615 0$aComputer vision$xComputer programs 615 0$aComputer vision 615 7$aEngineering & Applied Sciences 615 7$aApplied Physics 676 $a006.3/7 676 $a006.37 700 $aDawson-Howe$b Kenneth$0921570 702 $aDawson-Howe$b Kenneth 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9910465307503321 996 $aA Practical Introduction to Computer Vision with OpenCV$92067270 997 $aUNINA LEADER 00927nam a22002531i 4500 001 991003137109707536 005 20030902083901.0 008 030925s1922 it |||||||||||||||||ita 035 $ab12385074-39ule_inst 035 $aARCHE-043260$9ExL 040 $aBiblioteca Interfacoltà$bita$cA.t.i. Arché s.c.r.l. 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