Whether obtained by microscopes, space probes, or the human eye, the same basic tools can be applied to acquire, process, and analyze the data contained in images. Ideal for self study, The Image Processing Handbook, Sixth Edition, first published in 1992, raises the bar once again as the gold-standard reference on this subject. Using extensive new illustrations and diagrams, it offers a logically organized exploration of the important relationship between 2D images and the 3D structures they reveal. Provides Hundreds of Visual Examples in FULL COLOR! The author focuses on helping readers visualize and compare processing and measurement operations and how they are typically combined in fields ranging from microscopy and astronomy to real-world scientific, industrial, and forensic applications. Presenting methods in the order in which they would be applied in a typical workflow—from acquisition to interpretation—this book compares a wide range of algorithms used to: Improve the appearance, printing, and transmission of an image Prepare images for measurement of the features and structures they reveal Isolate objects and structures, and measure their size, shape, color, and position Correct defects and deal with limitations in images Enhance visual content and interpretation of details This handbook avoids dense mathematics, instead using new practical examples that better convey essential principles of image processing. This approach is more useful to develop readers’ grasp of how and why to apply processing techniques and ultimately process the mathematical foundations behind them. Much more than just an arbitrary collection of algorithms, this is the rare book that goes beyond mere image improvement, presenting a wide range of powerful example images that illustrate techniques involved in color processing and enhancement. Applying his 50-year experience as a scientist, educator, and industrial consultant, John Russ offers the benefit of his image processing expertise for fields ranging from astronomy and biomedical research to food science and forensics. His valuable insights and guidance continue to make this handbook a must-have reference.

Table of Contents:
Acquiring Images Human reliance on images for information Video cameras CCD cameras Camera artifacts and limitations Color cameras Camera resolution CMOS cameras Focusing Electronics and bandwidth limitations Pixels Gray scale resolution Noise High depth images Color imaging Digital camera limitations Color spaces Color correction Color displays Image types Range imaging Multiple images Stereoscopy Imaging requirements Human Vision What we see and why Recognition Technical specs Acuity What the eye tells the brain Spatial comparisons Local to global hierarchies It’s about time The third dimension How versus What Seeing what isn’t there, and vice versa Image compression A world of light Size matters Shape (whatever that means) Context Arrangements must be made Seeing is believing Printing and Storage Printing Dots on paper Color printing Printing hardware Film recorders Other presentation tools File storage Storage media Magnetic recording Databases for images Browsing and thumbnails Lossless coding Reduced color palettes JPEG compression Wavelet compression Fractal compression Digital movies Correcting Imaging Defects Contrast expansion Noisy images Neighborhood averaging Neighborhood ranking Other neighborhood noise reduction methods Defect removal, maximum entropy and maximum likelihood Nonuniform illumination Fitting a background function Rank leveling Color images Non-planar views Computer graphics Geometric distortion Alignment Interpolation Morphing Image Enhancement in the Spatial Domain Contrast manipulation Histogram equalization Local equalization Laplacian Derivatives Finding edges with gradients More edge detectors Texture Fractal analysis Implementation notes Image math Subtracting images Multiplication and division Principal Components Analysis Other image combinations Processing images in Frequency Space About frequency space The Fourier transform Fourier transforms of simple functions Frequencies and orientations Preferred orientation Texture and fractals Isolating periodic noise Selective masks and filters Selection of periodic information Convolution Deconvolution Noise and Wiener deconvolution Template matching and correlation Autocorrelation Segmentation and Thresholding Thresholding Automatic settings Multiband images Two-dimensional thresholds Multiband thresholding Thresholding from texture Multiple thresholding criteria Textural orientation Region boundaries Conditional histograms Boundary lines Contours Image representation Other segmentation methods The general classification problem Processing Binary Images Boolean operations Combining Boolean operations Masks From pixels to features Boolean logic with features Selecting features by location Double thresholding Erosion and dilation Opening and closing Isotropy Measurements using erosion and dilation Extension to gray scale images Morphology neighborhood parameters Examples of use Euclidean distance map Watershed segmentation Ultimate eroded points Skeletons Boundary lines and thickening Combining skeleton and EDM Global Image Measurements Global measurements and stereology Surface area ASTM Grain Size Multiple types of surfaces Length Thickness Sampling strategies Determining number Curvature, connectivity and the Disector Anisotropy and gradients Size distributions Classical stereology (unfolding) Feature-Specific Measurements Brightness measurements Determining location Orientation Neighbor relationships Alignment Counting Special counting procedures Feature size Circles and ellipses Caliper dimensions Perimeter Characterizing Shape Describing shape Dimensionless ratios Fractal dimension Harmonic analysis Topology Three dimensions Feature Recognition and Classification Template matching and cross-correlation Parametric description Decision points Multidimensional classification Learning systems kNN and cluster analysis Expert systems Neural nets Syntactical models Tomographic Imaging More than two dimensions Volume imaging vs. sections Basics of reconstruction Algebraic reconstruction methods Maximum entropy Defects in reconstructed images Beam hardening Imaging geometries Three-dimensional tomography High resolution tomography 3D Image Visualization Sources of 3D data Serial sections Optical sectioning Sequential removal Stereo measurement 3D data sets Slicing the data set Arbitrary section planes The use of color Volumetric display Stereo viewing Special display hardware Ray tracing Reflection Surfaces Multiply connected surfaces Image processing in 3D Measurements on 3D images Imaging Surfaces Producing surfaces Imaging surfaces by physical contact Noncontacting measurements Microscopy of surfaces Surface composition imaging Processing of range images Processing of composition maps Data presentation and visualization Rendering and visualization Analysis of surface data Profile measurements The Birmingham measurement suite Topographic analysis and fractal dimensions