Computer Vision Series

What is Computer Vision Computer vision tasks include methods for acquiring, processing, analyzing, and comprehending digital images, as well as the extraction of high-dimensional data from the actual world in order to provide numerical or symbolic information, such as, for example, in the form of judgments. In the context of this discussion, understanding refers to the process of transforming visual pictures into descriptions of the environment that are comprehensible to thinking processes and have the ability to evoke appropriate action. It is possible to interpret this picture understanding as the process of extracting symbolic information from image data by making use of models that have been created with the assistance of learning theory, geometry, physics, and computer science. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Computer vision Chapter 2: Machine vision Chapter 3: Image analysis Chapter 4: Image segmentation Chapter 5: Optical flow Chapter 6: Motion detection Chapter 7: Gesture recognition Chapter 8: Pose (computer vision) Chapter 9: Rita Cucchiara Chapter 10: Stereo cameras (II) Answering the public top questions about computer vision. (III) Real world examples for the usage of computer vision in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Computer Vision.

What is Joint Photographic Experts Group JPEG 2000, often known as JP2, is a standard format and coding scheme for picture compression. It was developed between the years 1997 and 2000 by a committee of the Joint Photographic Experts Group, which was chaired by Touradj Ebrahimi. The group's goal was to replace their original JPEG standard, which is based on a discrete cosine transform (DCT), with a wavelet-based approach that was freshly designed. Files that conform to ISO/IEC 15444-1 are designated with the extension.jp2, while files that conform to the expanded part-2 requirements, which are published as ISO/IEC 15444-2, are designated with the extension.jpx. Specifically, RFC 3745 is where the registered MIME types are defined. It is image/jp2 for the ISO/IEC 15444-1 standard. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: JPEG 2000 Chapter 2: JPEG Chapter 3: Lossy Compression Chapter 4: Image Compression Chapter 5: ICER Chapter 6: H.262/MPEG-2 Part 2 Chapter 7: MPEG-4 Part 2 Chapter 8: Image File Format Chapter 9: Motion JPEG 2000 Chapter 10: High Efficiency Image File Format (II) Answering the public top questions about joint photographic experts group. (III) Real world examples for the usage of joint photographic experts group in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Joint Photographic Experts Group.

What is Homography In the field of computer vision, any two images of the same planar surface in space are related by a homography. This has many practical applications, such as image rectification, image registration, or camera motion-rotation and translation-between two images. Once camera resectioning has been done from an estimated homography matrix, this information may be used for navigation, or to insert models of 3D objects into an image or video, so that they are rendered with the correct perspective and appear to have been part of the original scene. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Homography (computer vision) Chapter 2: Affine transformation Chapter 3: Transformation matrix Chapter 4: Image stitching Chapter 5: Line-plane intersection Chapter 6: Fundamental matrix (computer vision) Chapter 7: Camera resectioning Chapter 8: Image rectification Chapter 9: Camera matrix Chapter 10: Camera auto-calibration (II) Answering the public top questions about homography. (III) Real world examples for the usage of homography in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Homography.

What is Noise Reduction Noise reduction is the process of removing noise from a signal. Noise reduction techniques exist for audio and images. Noise reduction algorithms may distort the signal to some degree. Noise rejection is the ability of a circuit to isolate an undesired signal component from the desired signal component, as with common-mode rejection ratio. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Noise reduction Chapter 2: Dolby noise-reduction system Chapter 3: Dbx (noise reduction) Chapter 4: Digital image processing Chapter 5: Image noise Chapter 6: Wavelet Chapter 7: Difference of Gaussians Chapter 8: Bilateral filter Chapter 9: Non-local means Chapter 10: Block-matching and 3D filtering (II) Answering the public top questions about noise reduction. (III) Real world examples for the usage of noise reduction in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Noise Reduction.

What is Computer Stereo Vision Computer stereo vision is the extraction of 3D information from digital images, such as those obtained by a CCD camera. By comparing information about a scene from two vantage points, 3D information can be extracted by examining the relative positions of objects in the two panels. This is similar to the biological process of stereopsis. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Computer stereo vision Chapter 2: 3D reconstruction Chapter 3: Active contour model Chapter 4: Harris affine region detector Chapter 5: Foreground detection Chapter 6: Matrix Chernoff bound Chapter 7: Similarity Chapter 8: Structural similarity Chapter 9: Variance function Chapter 10: Fréchet inception distance (II) Answering the public top questions about computer stereo vision. (III) Real world examples for the usage of computer stereo vision in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Computer Stereo Vision.

What is Histogram Equalization Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Histogram Equalization Chapter 2: Cumulative Distribution Function Chapter 3: Histogram Chapter 4: Random Variable Chapter 5: Order Statistic Chapter 6: HSL and HSV Chapter 7: Color Histogram Chapter 8: Continuous Uniform Distribution Chapter 9: Optical Resolution Chapter 10: Empirical Distribution Function (II) Answering the public top questions about histogram equalization. (III) Real world examples for the usage of histogram equalization in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Histogram Equalization.

What is Radon Transform In mathematics, the Radon transform is the integral transform which takes a function f defined on the plane to a function Rf defined on the (two-dimensional) space of lines in the plane, whose value at a particular line is equal to the line integral of the function over that line. The transform was introduced in 1917 by Johann Radon, who also provided a formula for the inverse transform. Radon further included formulas for the transform in three dimensions, in which the integral is taken over planes. It was later generalized to higher-dimensional Euclidean spaces and more broadly in the context of integral geometry. The complex analogue of the Radon transform is known as the Penrose transform. The Radon transform is widely applicable to tomography, the creation of an image from the projection data associated with cross-sectional scans of an object. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Radon transform Chapter 2: Fourier transform Chapter 3: Bessel function Chapter 4: Convolution theorem Chapter 5: Discrete Fourier transform Chapter 6: Fourier series Chapter 7: Integration by parts Chapter 8: Fractional Fourier transform Chapter 9: Mellin transform Chapter 10: Poisson kernel (II) Answering the public top questions about radon transform. (III) Real world examples for the usage of radon transform in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Radon Transform.

What is Color Space A color space is a particular arrangement of colors in a given space. In conjunction with color profiling, which is supported by a variety of physical devices, it is capable of supporting repeatable representations of color, regardless of whether such representations involve an analog or a digital representation. It is possible for a color space to be arbitrary, in which case the colors that are physically realized are allocated to a set of physical color swatches that have matching color names, or it can be structured with mathematical precision. The concept of a "color space" is a helpful conceptual tool that may be utilized to gain a knowledge of the color capabilities of a certain digital file or device. In the process of attempting to replicate color on a different device, color spaces can indicate whether or not shadow/highlight detail and color saturation can be preserved, as well as the degree to which either of these aspects will be diminished. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Color space Chapter 2: RGB color model Chapter 3: CMYK color model Chapter 4: RGB color spaces Chapter 5: HSL and HSV Chapter 6: Chromaticity Chapter 7: CIELAB color space Chapter 8: Gamut Chapter 9: Grayscale Chapter 10: Adobe RGB color space (II) Answering the public top questions about color space. (III) Real world examples for the usage of color space in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Color Space.

What is Underwater Computer Vision Underwater computer vision is a subfield of computer vision. In recent years, with the development of underwater vehicles, the need to be able to record and process huge amounts of information has become increasingly important. Applications range from inspection of underwater structures for the offshore industry to the identification and counting of fishes for biological research. However, no matter how big the impact of this technology can be to industry and research, it still is in a very early stage of development compared to traditional computer vision. One reason for this is that, the moment the camera goes into the water, a whole new set of challenges appear. On one hand, cameras have to be made waterproof, marine corrosion deteriorates materials quickly and access and modifications to experimental setups are costly, both in time and resources. On the other hand, the physical properties of the water make light behave differently, changing the appearance of a same object with variations of depth, organic material, currents, temperature etc. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Underwater computer vision Chapter 2: Computer vision Chapter 3: Hydrographic survey Chapter 4: Autonomous underwater vehicle Chapter 5: Monterey Bay Aquarium Research Institute Chapter 6: Unmanned underwater vehicle Chapter 7: Noise reduction Chapter 8: Underwater vision Chapter 9: Video post-processing Chapter 10: Image quality (II) Answering the public top questions about underwater computer vision. (III) Real world examples for the usage of underwater computer vision in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Underwater Computer Vision.

What is Tone Mapping Tone mapping is a technique used in image processing and computer graphics to map one set of colors to another to approximate the appearance of high-dynamic-range (HDR) images in a medium that has a more limited dynamic range. Print-outs, CRT or LCD monitors, and projectors all have a limited dynamic range that is inadequate to reproduce the full range of light intensities present in natural scenes. Tone mapping addresses the problem of strong contrast reduction from the scene radiance to the displayable range while preserving the image details and color appearance important to appreciate the original scene content. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Tone_mapping Chapter 2: Gamma_correction Chapter 3: Multi-exposure_HDR_capture Chapter 4: High-dynamic-range_rendering Chapter 5: Shadow_and_highlight_enhancement Chapter 6: High_dynamic_range Chapter 7: Tone_reproduction Chapter 8: Luminance_HDR Chapter 9: Aurora_HDR Chapter 10: EasyHDR (II) Answering the public top questions about tone mapping. (III) Real world examples for the usage of tone mapping in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Tone Mapping.

What is Affine Transformation In Euclidean geometry, an affine transformation or affinity is a geometric transformation that preserves lines and parallelism, but not necessarily Euclidean distances and angles. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Affine Transformation Chapter 2: Linear Map Chapter 3: Translation (Geometry) Chapter 4: Affine Group Chapter 5: Affine Space Chapter 6: Transformation Matrix Chapter 7: Barycentric Coordinate System Chapter 8: Real Coordinate Space Chapter 9: Eigenvalues and Eigenvectors Chapter 10: Eigendecomposition of a Matrix (II) Answering the public top questions about affine transformation. (III) Real world examples for the usage of affine transformation in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Affine Transformation.

What is Image Compression When applied to digital photographs, image compression is a form of data compression that helps to reduce the amount of money that is required for their storage or transmission. It is possible for algorithms to make use of visual perception and the statistical aspects of picture data in order to provide higher outcomes when compared to generic data compression approaches that are utilized for other types of digital data. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Image compression Chapter 2: Data compression Chapter 3: JPEG Chapter 4: Lossy compression Chapter 5: Lossless compression Chapter 6: PNG Chapter 7: Transform coding Chapter 8: Discrete cosine transform Chapter 9: JPEG 2000 Chapter 10: Compression artifact (II) Answering the public top questions about image compression. (III) Real world examples for the usage of image compression in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Image Compression.

What is Image Histogram An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. It plots the number of pixels for each tonal value. By looking at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at a glance. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Image histogram Chapter 2: Histogram Chapter 3: Color histogram Chapter 4: Thresholding (image processing) Chapter 5: Histogram equalization Chapter 6: Adaptive histogram equalization Chapter 7: Histogram matching Chapter 8: Tone mapping Chapter 9: Error diffusion Chapter 10: Graph cuts in computer vision (II) Answering the public top questions about image histogram. (III) Real world examples for the usage of image histogram in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Image Histogram.

What is Color Profile A set of data that, according to the standards that have been adopted by the International Color Consortium (ICC), characterizes a color input or output device or a color space is referred to as an ICC profile. This profile is the basis for color management. By providing a mapping between the device source or target color space and a profile connection space (PCS), profiles are able to provide a description of the color characteristics that are associated with a certain device or viewing requirement. Either CIELAB (L*a*b*) or CIEXYZ is considered to be this PCS. There are two ways to specify mappings: either through the use of tables, which are then subjected to interpolation, or by a sequence of parameters concerning transformations. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: ICC profile Chapter 2: Color management Chapter 3: RGB color model Chapter 4: CMYK color model Chapter 5: CIELAB color space Chapter 6: Adobe RGB color space Chapter 7: Color space Chapter 8: Prepress Chapter 9: JPEG File Interchange Format Chapter 10: TIFF (II) Answering the public top questions about color profile. (III) Real world examples for the usage of color profile in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Color Profile.

What is Inpainting Inpainting is a conservation process where damaged, deteriorated, or missing parts of an artwork are filled in to present a complete image. This process is commonly used in image restoration. It can be applied to both physical and digital art mediums such as oil or acrylic paintings, chemical photographic prints, sculptures, or digital images and video. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Inpainting Chapter 2: Texture synthesis Chapter 3: Conservation and restoration of cultural property Chapter 4: Historic paint analysis Chapter 5: Conservation science (cultural property) Chapter 6: Conservation and restoration of paintings Chapter 7: Conservation and restoration of panel paintings Chapter 8: Conservation and restoration of Pompeian frescoes Chapter 9: Conservation and restoration of ancient Greek pottery Chapter 10: Conservation-restoration of Thomas Eakins' The Gross Clinic (II) Answering the public top questions about inpainting. (III) Real world examples for the usage of inpainting in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Inpainting.

What is Human Visual System Model Experts in image processing, video processing, and computer vision make use of a human visual system model in order to deal with biological and psychological processes that are not yet completely understood. An example of such a model is utilized in order to simplify the behaviors of a system that is extremely complex. Whenever there is an improvement in our understanding of the actual visual system, the model is updated. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Human visual system model Chapter 2: Data compression Chapter 3: Image compression Chapter 4: Transform coding Chapter 5: Optical illusion Chapter 6: Chroma subsampling Chapter 7: Compression artifact Chapter 8: Grayscale Chapter 9: Tone mapping Chapter 10: Color appearance model (II) Answering the public top questions about human visual system model. (III) Real world examples for the usage of human visual system model in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Human Visual System Model.

What is Anisotropic Diffusion In image processing and computer vision, anisotropic diffusion, also called Perona-Malik diffusion, is a technique aiming at reducing image noise without removing significant parts of the image content, typically edges, lines or other details that are important for the interpretation of the image. Anisotropic diffusion resembles the process that creates a scale space, where an image generates a parameterized family of successively more and more blurred images based on a diffusion process. Each of the resulting images in this family are given as a convolution between the image and a 2D isotropic Gaussian filter, where the width of the filter increases with the parameter. This diffusion process is a linear and space-invariant transformation of the original image. Anisotropic diffusion is a generalization of this diffusion process: it produces a family of parameterized images, but each resulting image is a combination between the original image and a filter that depends on the local content of the original image. As a consequence, anisotropic diffusion is a non-linear and space-variant transformation of the original image. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Anisotropic diffusion Chapter 2: Fick's laws of diffusion Chapter 3: Diffusion equation Chapter 4: Heat equation Chapter 5: Navier-Stokes equations Chapter 6: Total variation Chapter 7: Divergence Chapter 8: Laplace operator Chapter 9: Curl (mathematics) Chapter 10: Divergence theorem (II) Answering the public top questions about anisotropic diffusion. (III) Real world examples for the usage of anisotropic diffusion in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Anisotropic Diffusion.

What is Color Management System A color appearance model, often known as a CAM, is a mathematical model that aims to capture the perceptual elements of human color vision. This model is used to describe viewing settings in which the appearance of a color does not coincide with the corresponding actual measurement of the stimulus source. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Color management Chapter 2: RGB color model Chapter 3: CMYK color model Chapter 4: Gamma correction Chapter 5: Web colors Chapter 6: CIELAB color space Chapter 7: Gamut Chapter 8: sRGB Chapter 9: Adobe RGB color space Chapter 10: Color calibration (II) Answering the public top questions about color management system. (III) Real world examples for the usage of color management system in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Color Management System.

What is Filter Bank A filter bank is an array of bandpass filters that is used in signal processing. Its purpose is to divide the input signal into several components, each of which carries a sub-band of the original signal. Attenuating the components in a new way and recombining them into a modified version of the original signal is one of the applications of a filter bank. A graphic equalizer is one example of this type of application. The result of analysis is referred to as a subband signal, and it contains as many subbands as there are filters in the filter bank. The process of decomposition that is carried out by the filter bank is referred to as analysis. Synthesis is the term used to describe the process of reconstruction, which refers to the act of reconstructing a complete signal that was produced by the filtering process. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Filter Bank Chapter 2: Discrete Fourier Transform Chapter 3: Digital Filter Chapter 4: Wavelet Chapter 5: Modified Discrete Cosine Transform Chapter 6: Finite Impulse Response Chapter 7: Daubechies Wavelet Chapter 8: Discrete Wavelet Transform Chapter 9: Discrete-Time Fourier Transform Chapter 10: Downsampling (Signal Processing) (II) Answering the public top questions about filter bank. (III) Real world examples for the usage of filter bank in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Filter Bank.

What is Color Mapping This function is known as image color transfer, and it is responsible for mapping (transforming) the colors of one image (the source) to the colors of another image (the target). It is possible to refer to a color mapping as either the algorithm that produces the mapping function or the method that alters the colors of the image. The process of modifying a picture is frequently referred to as color transfer or, when grayscale photos are involved, brightness transfer function (BTF). Additionally, it may also be referred to as photometric camera calibration or radiometric camera calibration. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Image Color Transfer Chapter 2: Gamma Correction Chapter 3: Color Management Chapter 4: Color Histogram Chapter 5: Shader Chapter 6: Tone Mapping Chapter 7: Image Histogram Chapter 8: Color Calibration Chapter 9: Color Quantization Chapter 10: Image Rectification (II) Answering the public top questions about color mapping. (III) Real world examples for the usage of color mapping in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Color Mapping.

What is Color Model A color model is an abstract mathematical model describing the way colors can be represented as tuples of numbers, typically as three or four values or color components. When this model is associated with a precise description of how the components are to be interpreted, taking account of visual perception, the resulting set of colors is called "color space." How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Color Model Chapter 2: Hue Chapter 3: Munsell Color System Chapter 4: RGB Color Spaces Chapter 5: HSL and HSV Chapter 6: Chromaticity Chapter 7: CIELAB Color Space Chapter 8: Chromatic Adaptation Chapter 9: Gamut Chapter 10: Dominant Wavelength (II) Answering the public top questions about color model. (III) Real world examples for the usage of color model in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Color Model.

What is Retinex Color constancy is an example of subjective constancy and a feature of the human color perception system which ensures that the perceived color of objects remains relatively constant under varying illumination conditions. A green apple for instance looks green to us at midday, when the main illumination is white sunlight, and also at sunset, when the main illumination is red. This helps us identify objects. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Color Constancy Chapter 2: Color Chapter 3: Color Vision Chapter 4: Visual System Chapter 5: Chromatic Adaptation Chapter 6: Afterimage Chapter 7: Trichromacy Chapter 8: Cone Cell Chapter 9: Visual Acuity Chapter 10: Opponent Process (II) Answering the public top questions about retinex. (III) Real world examples for the usage of retinex in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Retinex.

What is Random Sample Consensus Random sample consensus, also known as RANSAC, is an iterative method that is used to estimate the parameters of a mathematical model based on a collection of observed data that includes outliers. This method is used in situations where the outliers are permitted to have no impact on the values of the estimates. The conclusion is that it is also possible to view it as a tool for detecting outliers. An algorithm is considered to be non-deterministic if it is able to generate a suitable result only with a certain probability, and this likelihood increases as the number of iterations that are permitted via the method increases. In 1981, Fischler and Bolles, who were working at SRI International, were the ones who initially published the algorithm. In order to solve the Location Determination Problem (LDP), which is a problem in which the objective is to find the points in space that project onto an image and then convert those points into a set of landmarks with known positions, they utilized RANSAC. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Random sample consensus Chapter 2: Estimator Chapter 3: Least squares Chapter 4: Outlier Chapter 5: Cross-validation (statistics) Chapter 6: Errors and residuals Chapter 7: Mixture model Chapter 8: Robust statistics Chapter 9: Image stitching Chapter 10: Resampling (statistics) (II) Answering the public top questions about random sample consensus. (III) Real world examples for the usage of random sample consensus in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Random Sample Consensus.

What is Hough Transform The Hough transform is a feature extraction technique used in image analysis, computer vision, and digital image processing. The purpose of the technique is to find imperfect instances of objects within a certain class of shapes by a voting procedure. This voting procedure is carried out in a parameter space, from which object candidates are obtained as local maxima in a so-called accumulator space that is explicitly constructed by the algorithm for computing the Hough transform. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Hough transform Chapter 2: Generalised Hough transform Chapter 3: Randomized Hough transform Chapter 4: Circle Hough Transform Chapter 5: Line detection Chapter 6: 3D projection Chapter 7: Parametric equation Chapter 8: Equation Chapter 9: Ellipse Chapter 10: Cissoid (II) Answering the public top questions about hough transform. (III) Real world examples for the usage of hough transform in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Hough Transform.

What is Harris Corner Detector The Harris corner detector is a corner detection operator that is commonly used in computer vision algorithms to extract corners and infer features of an image. It was first introduced by Chris Harris and Mike Stephens in 1988 upon the improvement of Moravec's corner detector. Compared to its predecessor, Harris' corner detector takes the differential of the corner score into account with reference to direction directly, instead of using shifting patches for every 45 degree angles, and has been proved to be more accurate in distinguishing between edges and corners. Since then, it has been improved and adopted in many algorithms to preprocess images for subsequent applications. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Harris corner detector Chapter 2: Corner detection Chapter 3: Structure tensor Chapter 4: Harris affine region detector Chapter 5: Lucas-Kanade method Chapter 6: Hessian matrix Chapter 7: Geometric feature learning Chapter 8: Tensor density Chapter 9: Mehrotra predictor-corrector method Chapter 10: Discrete Laplace operator (II) Answering the public top questions about harris corner detector. (III) Real world examples for the usage of harris corner detector in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Harris Corner Detector.

What is Adaptive Filter A system that has a linear filter and possesses a transfer function that is controlled by variable parameters as well as a means to alter those parameters in accordance with an optimization technique is commonly referred to as an adaptive filter. The vast majority of adaptive filters are digital filters. This is due to the complexity of the optimization techniques. Some applications necessitate the utilization of adaptive filters due to the fact that some parameters of the desired processing operation are either unknown in advance or are frequently subject to change. Refining the transfer function of the closed loop adaptive filter is accomplished by the utilization of feedback in the form of an error signal. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Adaptive filter Chapter 2: Signal-to-noise ratio Chapter 3: Additive white Gaussian noise Chapter 4: Linear elasticity Chapter 5: Sliding mode control Chapter 6: Array processing Chapter 7: Autoregressive model Chapter 8: Least mean squares filter Chapter 9: Recursive least squares filter Chapter 10: ADALINE (II) Answering the public top questions about adaptive filter. (III) Real world examples for the usage of adaptive filter in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Adaptive Filter.

What is Hadamard Transform The Hadamard transform is an example of a generalized class of Fourier transforms. It performs an orthogonal, symmetric, involutive, linear operation on 2m real numbers. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Hadamard transform Chapter 2: Discrete Fourier transform Chapter 3: Fast Walsh-Hadamard transform Chapter 4: Quantum Fourier transform Chapter 5: Bracket notation Chapter 6: Pauli matrices Chapter 7: Quantum logic gate Chapter 8: Controlled NOT gate Chapter 9: Generalizations of Pauli matrices Chapter 10: Spherical basis (II) Answering the public top questions about hadamard transform. (III) Real world examples for the usage of hadamard transform in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Hadamard Transform.

What is Gamma Correction Gamma correction, often known as gamma, is a nonlinear process that is utilized in video or still image systems for the purpose of encoding and decoding luminance or tristimulus values. One of the most straightforward ways to define gamma correction is using the power-law statement that is presented below: How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Gamma correction Chapter 2: RGB color model Chapter 3: Grayscale Chapter 4: sRGB Chapter 5: Adobe RGB color space Chapter 6: Tone mapping Chapter 7: Rec. 709 Chapter 8: Rec. 2020 Chapter 9: Standard-dynamic-range video Chapter 10: Hybrid log-gamma (II) Answering the public top questions about gamma correction. (III) Real world examples for the usage of gamma correction in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Gamma Correction.

What is Edge Detection Edge detection is a collection of mathematical techniques that are aimed at recognizing edges, which are defined as curves in a digital image at which the brightness of the image changes abruptly or, more formally, contains discontinuities. The difficulty of discovering discontinuities in one-dimensional signals is referred to as step detection, and the problem of finding signal discontinuities over time is referred to as change detection. Both of these techniques are used to find discontinuities in signals. The method of edge detection is an essential tool in the fields of image processing, machine vision, and computer vision, notably in the areas of feature detection and feature extraction. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Edge detection Chapter 2: Digital image processing Chapter 3: Sobel operator Chapter 4: Roberts cross Chapter 5: Canny edge detector Chapter 6: Marr-Hildreth algorithm Chapter 7: Scale-invariant feature transform Chapter 8: Discrete Laplace operator Chapter 9: Scale space Chapter 10: Prewitt operator (II) Answering the public top questions about edge detection. (III) Real world examples for the usage of edge detection in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Edge Detection.

What is Color Matching Function The color spaces designated by the CIE in 1931 are the first quantitative relationships that have been delineated between the distributions of wavelengths in the electromagnetic visible spectrum and the colors that are physiologically seen by humans in their color vision. When it comes to color management, the mathematical relationships that define these color spaces are key tools. This is especially true when working with color inks, lighted displays, and recording devices like digital cameras. In 1931, the "Commission Internationale de l'éclairage," which is translated into English as the International Commission on Illumination, was the organization that was responsible for designing the system. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: CIE 1931 color space Chapter 2: Luminous efficiency function Chapter 3: Color balance Chapter 4: Planckian locus Chapter 5: Standard Reference Method Chapter 6: Relative luminance Chapter 7: CIECAM02 Chapter 8: Standard illuminant Chapter 9: CIE 1960 color space Chapter 10: OSA-UCS (II) Answering the public top questions about color matching function. (III) Real world examples for the usage of color matching function in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Color Matching Function.