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    Computer Vision

    Computer Vision

    - Principles, Algorithms, Applications, Learning

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    • Format
    • Bog, hardback
    • Engelsk

    Beskrivelse

    Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject. See an interview with the author explaining his approach to teaching and learning computer vision - http://scitechconnect.elsevier.com/computer-vision/

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    Detaljer
    • SprogEngelsk
    • Sidetal900
    • Udgivelsesdato14-11-2017
    • ISBN139780128092842
    • Forlag Academic Press Inc
    • FormatHardback
    Størrelse og vægt
  • Vægt1990 g
    • coffee cup img
    10 cm
    book img
    19,1 cm
    23,5 cm

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    Erosion Computer vision Cluster analysis Collision avoidance Graph Matching Distance functions Edge detection Flexibility Crossing number Automated Visual Inspection Dilation Convolutions Connectedness Closing Circle Detection Energy minimization Accuracy Gaussian mixture models 3-D reconstruction Active contour models Background Modeling Hough transform Deep learning networks Camera Calibration Artificial neural network Epipolar geometry Cost Function 3-D vision Fundamental Matrix 3-point problem Adaptive thresholding Ade's eigenfilter approach Affine invariant detector 3-D geometry Appraisal of the Laws and Ade approaches Ambiguities of interpretation Apparent centers of symmetric objects Binary and grayscale images Boosting with multiple classes Autocorrelation Canny operator Boundary tracking Binary shape analysis Centroidal profiles Basic approaches to texture analysis Boosting for face detection Boundary length measures Boundary pattern analysis Color filters Chord-tangent method Connected components analysis Binocular images CNN architecture design Cross Ratio Color histograms Convolutional neural networks (CNNs) Deep Learning Methods Circular operators Differential gradient operators Correcting radial distortions Detection sensitivity Comparison of detectors Eight-point algorithm Ellipse detection Deconvolution and visualization Definition of texture Chamfer matching Entropy-based thresholding Extrinsic Parameters False positives and false negatives Facial feature detection Differential invariant Formula for curvature κ Face detection and recognition Generalized Hough transform (GHT) Frontalization Computational load Focus of expansion Corner orientation Error-reject tradeoff Correspondence problem Help from convexity Histogram concavity analysis Full perspective projection Diameter bisection method Harris operator Help from symmetry Hessian-based corner detector Egomotion Epipolar Line Essential Matrix Foreground Detection Ground Plane Generalization to grayscale processing Graylevel co-occurrence matrices Histogram-based image segmentation HOG operator Global valley approach to thresholding

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