The Essential Guide to Image Processing

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Description	Book Information
The Essential Guide to Image Processing covers a wide range of image processing topics, from basic image representation, to filtering techniques, to advanced topics like image quality assessment with algorithms such as the structural similarity (SSIM) algorithm. Included with the book are a collection of interactive examples on CD that provide the user hands-on experience with practical real-world examples of the theory and algorithms presented in the text. These examples were built and distributed using National Instruments LabVIEW with the Vision Development Module. Use these tools to access and modify the source code installed with the image processing examples, or to create your own custom application. Examples included on the books accompanying CD: Mod 1-Introduction Quantize Sample Mod 2-Binary Image Processing Complement Histogram Morphology Skeleton Threshold Mod 3-Histogram and Point Operations Histoshape Image Difference Interpolate LinearPoint Mod 4-Discrete Fourier Transform, Sampling Theorem DFT Direction Digital-Sine FFT Important Functions MaskDFT Mod 5-Linear Filtering, Enhancement, and Restoration AverageFilter BandPassFilter Deblur Deconvolution Pseudo Inverse GaussianFilter HighPassFilter IdealLowPassFilter Noise WienerFilter Mod 6-Nonlinear Image Filtering GrayScale Morphology Homomorphic Filtering Peak and Valley Detection Trimmed Mean Filter Mod 7-Digital Image Coding and Compression Block Truncation Coding Entropy Reduction JPEG Compression Mod 8-Edge Detection and Template Matching Anisotropic Diffusion Filter Canny Edge Detector Continuity Thresholded Laplacian of Gaussian Edge Detect Laplacian of Gaussian Laplacian SSIM Template Matching Thresholded Laplacian of Gaussian Thresholded Laplacian Mod 9-Hough Transform Hough Transform
	Authors and Organization: Al Bovik, University of Texas
	Publisher: Elsevier (Academic Press)
	Copyright Year: 2009
	Edition: 2
	ISBN-10: 0-123744-57-1
	ISBN-13: 978-0-123744-57-9
	Summary of How NI Technology is Used: The included examples were written with LabVIEW and the Vision Development Module.
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