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This book explores image processing from several perspectives: the creative, the theoretical (mainly mathematical), and the programmatical. It explains the basic principles of image processing, drawing on key concepts and techniques from mathematics, psychology of perception, computer science, and art, and introduces computer programming as a way to get more control over image processing operations. It does so without requiring college-level mathematics or prior programming experience. The content is supported by PixelMath, a freely available software program that helps the reader understand images as both visual and mathematical objects. The first part of the book covers such topics as digital image representation, sampling, brightness and contrast, color models, geometric transformations, synthesizing images, stereograms, photomosaics, and fractals. The second part of the book introduces computer programming using an open-source version of the easy-to-learn Python language. It covers the basics of image analysis and pattern recognition, including edge detection, convolution, thresholding, contour representation, and K-nearest-neighbor classification. A chapter on computational photography explores such subjects as high-dynamic-range imaging, autofocusing, and methods for automatically inpainting to fill gaps or remove unwanted objects in a scene. Applications described include the design and implementation of an image-based game. The PixelMath software provides a "transparent" view of digital images by allowing the user to view the RGB values of pixels by zooming in on an image. PixelMath provides three interfaces: the pixel calculator; the formula page, an advanced extension of the calculator; and the Python window.
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• From the contents:Part I: Human and Computer Vision: Organization in Human Vision.- Direction, Frequency, and Velocity Preference.- Color.- Face Recognition.- Part II: Linear Tools of Vision: Discrete Images and Hilbert Spaces.- Continous Functions and Hilbert Spaces.- Finite Extension or Periodic Functions ? Fourier Coefficients.- Fourier Transform - Infinite Extension Functions.- Properties of the Fourier Transform.- Reconstruction and Approximation.- Scales and Frequency Channels.- Part III: Vision of Single Direction: Direction in 2-D.- Generalized Direction in 2-D.- Part IV: Multiple Directions in Vision: N-folded Symmetry in Images.- Part V: Grouping, Segmentation, and Region Description: Reducing the Dimension of Features.- Feature Clustering.- Image Segmentation and Region Categorization.- Region and Boundary Descriptors.- Part VI: Vision of World Geometry: Camera Parameters.- World Geometry by Measurements in Images.- References.
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This introductory textbook presents the modern signal processing concepts used in computer vision and image analysis in a systematic and mathematically coherent way. For the first time in a textbook on image processing, single direction, group direction, corners and edges, Hough transform, and motion estimation are developed in a principled way using direction tensors as the unifying concept. The topics presented include Hilbert spaces, the Fourier transform, scale analysis, direction fields, structure tensors, motion tensors, the Hough transform, grouping, and segmentation. Directional signal processing, an increasingly crucial element of computer vision for which neural circuits exist in human vision, is dealt with in depth by use of tensors. All chapters are richly illustrated, with color graphics from cover to cover; applications are studied in various fields, including biometric person authentication, texture analysis, optical character recognition, and motion estimation and tracking; and exercises help the student verify progress. Developed out of courses given by the author, this introductory textbook addresses advanced undergraduates as well as master and PhD students in computer science, engineering, mathematics, and in other disciplines where techniques from computer vision, image processing, visual computation and signal analysis are applied.
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Covering image processing from basics to advanced applications, you can learn how to master image processing and compression with this outstanding state of the art reference. From fundamentals to sophisticated applications, "Image Processing: Principles and Applications" covers multiple topics and provides a fresh perspective on future directions and innovations in the field, including: Image transformation techniques, including wavelet transformation and developments; Image enhancement and restoration, including noise modeling and filtering; Segmentation schemes, and classification and recognition of objects ; Texture and shape analysis techniques; Fuzzy set theoretical approaches in image processing, neural networks, etc.; Content based image retrieval and image mining; Biomedical image analysis and interpretation, including biometric algorithms such as face recognition and signature verification; Remotely sensed images and their applications; Principles and applications of dynamic scene analysis and moving object detection and tracking; Fundamentals of image compression, including the JPEG standard and the new JPEG2000 standard. Additional features include problems and solutions with each chapter to help you apply the theory and techniques, as well as bibliographies for researching specialized topics. With its extensive use of examples and illustrative figures, this is a superior title for students and practitioners in computer science, wireless and multimedia communications, and engineering.
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• Preface
• 1. Microscope Image Acquisition
• 2. Biological Image Processing and Enhancement
• 3. Image Analysis: Quantitative Interpretation of Chromosome Images
• 4. Pattern Recognition: Classification of Chromosomes
• 5. Three Dimensional (3D) Reconstruction from Serial Sections
• 6. 3D Analysis: Registration of 3D Biomedical Images
• 7. Model-Based Methods in Analysis of Biomedical Images
• 8. Projective Stereology in Biological Microscopy
• 9. Image Warping and Spatial Data Mapping
• Appendix
• Index.
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A wide range of books on image processing and analysis provide comprehensive descriptions of mathematics and algorithms for image processing practitioners, or introductory material for engineering students. This volume is different in addressing the topic from the point of view of the "user". Standard algorithms, procedures and rules of thumb are explained in the context of successful application to biological or medical images. Early chapters cover the basic topics of image acquisition, processing, analysis and pattern recognition. Much of the explanation is in the form of protocols, which should equip the user in the biological or earth sciences with the background for informed use of image processing software, and sufficient knowledge to write their own programmes if they feel moved to do so. More advanced techniques in the use of explicit models and analysis of 3D images are covered in later chapters, also with reference to specific applications. The coverage of these is not exhaustive, but may inspire the reader to consider applying image analysis to problems beyond those tackled by commercial packages.
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This resource examines the rapid, powerful developments in graphic formation, covering the array of concepts and techniques in high-end imaging software. With balanced, complete descriptions, the text outlines frequency space methods with extensive mathematical presentation and spatial domain processing; compares different algorithms; and requires only a modest technical background in mathematics or computers. This edition contains a new chapter on surface imaging, a text-enhancing CD, and updated references.
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• Preface
• Acknowledgments
• 1. Introduction to electronic image processing. 1.1. Historical background; 1.2. Applications of image processing; 1.3. Introduction to visual perception; 1.4. Image formation; 1.5. Sampling and quantization; 1.6. Image neighbors and distances; 1.7. Typical image processing systems
• 2. Transforms used in electronic image processing. 2.1. The Fourier series; 2.2. The one-dimensional Fourier transform; 2.3. The two-dimensional Fourier transform; 2.4. Important functions relating to the Fourier transform; 2.5. The discrete Fourier transform; 2.6. Example and properties of the discrete Fourier transform; 2.7. Computation of the discrete Fourier transform; 2.8. Other image transforms
• 3. Image enhancement by point operations. 3.1. An overview of point processing; 3.2. Constant and nonlinear operations; 3.3. Operations between images; 3.4. Histogram techniques
• 4. Spatial filtering and fourier frequency methods. 4.1. Various types of noise that appear in images; 4.2. Spatial filtering; 4.3. Spatial frequency filtering; 4.4. Image restoration.

• 5. Nonlinear image processing techniques. 5.1. Nonlinear spatial filters based on order statistics; 5.2. Nonlinear mean filters; 5.3. Adaptive filters; 5.4. The homomorphic filter
• 6. Color image processing. 6.1. Color fundamentals; 6.2. Color models; 6.3. Examples of color image processing; 6.4. Pseudocoloring and color displays
• 7. Image geometry and morphological filters. 7.1. Spatial interpolation; 7.2. Image geometry; 7.3. Binary morphology: dilation and erosion; 7.4. Binary morphology: opening, closing, edge detection, and skeletonization; 7.5. Binary morphology: hit-miss, thinning, thickening, and pruning; 7.6. Binary morphology: granulometries and the pattern spectrum; 7.7. Graylevel morphology
• 8. Image segmentation and representation. 8.1. Image thresholding; 8.2. Edge, line, and point detection; 8.3. Region based segmentation; 8.4. Image representation
• 9. Image compression. 9.1. Compression fundamentals; 9.2. Error-free compression methods; 9.3. Lossy compression methods
• Bibliography
• Index.

This book is directed to practicing engineers and scientists who need to understand the fundamentals of image processing theory and algorithms to perform their technical tasks. It is intended to fill the gap between existing high-level texts dedicated to specialists in the field and the need for a more practical, fundamental text on image processing. A variety of example images are used to enhance reader understanding of how particular image processing algorithms work.
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• 1. Introduction.
• 2. The Image Plane.
• 3. Two Dimensional Impulses.
• 4. The Two Dimensional Fourier Transform.
• 5. Two Dimensional Convolution.
• 6. The Convolution Theorem.
• 7. Sampling and Interpolation in Two Dimensions.
• 8. Digital Operations.
• 9. Rotational Symmetry and the Two Dimensional Fourier Transform.
• 10. Imaging by Convolution.
• 11. Diffraction Theory of Sensors.
• 12. Indirect Imaging and Interferometry.
• 13. Restoration of Images.
• 14. The Projection-Slice Theorem.
• 15. Computed Tomography.
• 16. Synthetic Aperture Radar.
• 17. Random Images and Fractals. Index.
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• The image plane
• the dimensional impulses
• the two dimensional fourier transform
• two dimensional convolution
• the convolution theorem
• sampling and interpolation in two dimensions
• digital operations
• rotational symmetry and the two dimensional fourier transform
• imaging by convolution
• diffraction theory of sensors
• indirect imaging and interferometry
• restoration of images
• the projection-slice theorem
• computed tornography
• synthetic aperture radar
• random images and fractals.
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Exploring the idea that a two-dimensional image is much the same as a mathematical function of two variables, this study considers a variety of phenomena that are susceptible to mathematical reasoning. It examines the quantitative phenomena that have a wide application in either continuous or discrete situations and discusses specific images associated with techniques such as diffraction, tomography, inferometry and range-Doppler mapping. The book should be accessible to those with mathematical backgrounds but requires no previous experience with transform methods.
(source: Nielsen Book Data)

• Part 1 Acquiring images: human reliance on images for information
• using video cameras to acquire images
• electronics and bandwidth limitations
• high resolution imaging
• colour imaging
• multiple images
• imaging requirements. Part 2 Correcting imaging defects: noisy images
• neighbourhood averaging
• neighbourhood ranking
• other neighbourhood noise reduction methods
• maximum entropy
• contrast expansion
• non-uniform illumination
• fitting a background function
• rank levelling
• non-planar views
• computer graphics
• geometrical distortion
• alignment. Part 3 Image enhancement (processing in the spatial domain): contrast manipulation
• histogram equalization
• Laplacian
• derivatives
• the Sobel and Kirsch operators
• rank operations
• texture
• fractal analysis
• implementation notes
• image math
• subtracting images
• multiplication and division. Part 4 Processing images in frequency space: some necessary mathematical preliminaries
• measuring images in the frequency domain
• filtering images
• convolution and correlation
• conclusion
• for further reading. Part 5 Segmentation and thresholding: thresholding
• multi-band images
• two-dimensional thresholds
• multiband thresholding
• thresholding from texture
• multiple thresholding criteria
• textural orientation
• accuracy and reproducibility
• including position information
• selective histograms
• boundary lines
• contours
• image representation
• other segmentation methods
• the general classification problem. Part 6 Processing binary images: Boolean operations
• combining Boolean operations
• masks
• from Pixels to features
• Boolean logic with features
• erosion and dilation
• opening and closing
• isotropy
• measurements using erosion and dilation
• extension to grey scale images
• the coefficient and depth parameters
• examples of use
• the cluster
• skeletonization
• boundary lines and thickening
• Euclidean distance map
• watershed segmentation
• ultimate eroded points
• fractal dimension measurement
• medial axis transform
• cluster analysis. Part 7 Tomography: basics of reconstruction
• algebraic reconstruction methods
• maximum entropy
• defects in reconstructed images
• imaging geometrics
• three-dimensional tomography
• high resolution tomography. Part 8 3D imaging: sources of 3D data
• serial sections
• optical sectioning
• sequential removal
• stereo
• 3D data sets
• slicing the data set
• arbitrary section planes
• the use of colour
• volumetric display
• stereo viewing
• special display hardware
• emission
• reflection
• surfaces
• multiply connected surfaces
• image processing in 3D
• measurements on 3D images
• conclusions.
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"The Image Processing Handbook" presents an extensive collection of image processing tools that should prove invaluable in maximizing the quality and usefulness of visual information. This book covers methods used for enhancing the visual appearance of images and preparing images for measurement. It should help readers to understand and apply methods provided in packaged software or enable them to programme those additions that may be necessary for particular applications. The aqcuisition of grey-scale and colour images and methods for correcting imaging defects such as geometric distortion and nonuniform illumination are demonstrated. The book details the selective enhancement of images, such as edge finding, noise suppression and texture extraction by grey scale processing and frequency space processing operations. Segmentation of images and the processing of binary images using morphological and Boolean operations are shown, and three-dimensional imaging using tomographic reconstruction, serial sections and volumetric and surface rendering methods are illustrated.
(source: Nielsen Book Data)

This book addresses one of the central problems in computer-aided image analysis; how the shape of structures in an image should be represented to best facilitate quantitative analysis. To accomplish this task requires an understanding of what shape is and how it should be extracted from grey-scale images. In Professor Gauch's book a new shape description for grey-scale images called the intensity axis of symmetry (IAS) and an associated curvature-based description called vertex curves are presented.
(source: Nielsen Book Data)