Image enhancement

• Image denoising
  Image denoising
  Image denoising refers to the recovery of a digital image that has been contaminated by additive white Gaussian noise .-Technical description:...
  
  
• Image histogram
  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...
  
  
• Histogram equalization
  Histogram equalization
  Histogram equalization is a method in image processing of contrast adjustment using the image's histogram.-Overview:This method usually increases the global contrast of many images, especially when the usable data of the image is represented by close contrast values. Through this adjustment, the...
  
  
• Tone mapping
  Tone mapping
  Tone mapping is a technique used in image processing and computer graphics to map one set of colors to another in order to approximate the appearance of high dynamic range images in a medium that has a more limited dynamic range...
  
  
• Retinex
• Gamma correction
  Gamma correction
  Gamma correction, gamma nonlinearity, gamma encoding, or often simply gamma, is the name of a nonlinear operation used to code and decode luminance or tristimulus values in video or still image systems...
  
  
• Anisotropic Diffusion
  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...
  
   (Perona-Malik equation)

Transformations

• Affine transform
• Projective transform
• Hough transform
  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...
  
  
• Radon transform
  Radon transform
  thumb|right|Radon transform of the [[indicator function]] of two squares shown in the image below. Lighter regions indicate larger function values. Black indicates zero.thumb|right|Original function is equal to one on the white region and zero on the dark region....
  
  
• Walsh–Hadamard transform

Filtering, Fourier and wavelet transforms and image compression

• Image compression
  Image compression
  The objective of image compression is to reduce irrelevance and redundancy of the image data in order to be able to store or transmit data in an efficient form.- Lossy and lossless compression :...
  
  
• Filter bank
  Filter bank
  In signal processing, a filter bank is an array of band-pass filters that separates the input signal into multiple components, each one carrying a single frequency subband of the original signal. One application of a filter bank is a graphic equalizer, which can attenuate the components...
  
  
• Gabor filter
  Gabor filter
  In image processing, a Gabor filter, named after Dennis Gabor, is a linear filter used for edge detection. Frequency and orientation representations of Gabor filters are similar to those of the human visual system, and they have been found to be particularly appropriate for texture representation...
  
  
• JPEG 2000
  JPEG 2000
  JPEG 2000 is an image compression standard and coding system. It was created by the Joint Photographic Experts Group committee in 2000 with the intention of superseding their original discrete cosine transform-based JPEG standard with a newly designed, wavelet-based method...
  
  
• Adaptive filtering

Color vision

• Visual perception
  Visual perception
  Visual perception is the ability to interpret information and surroundings from the effects of visible light reaching the eye. The resulting perception is also known as eyesight, sight, or vision...
  
  
• Human visual system model
  Human Visual System Model
  A human visual system model is used by image processing, video processing and computer vision experts to deal with biological and psychological processes that are not yet fully understood. Such a model is used to simplify the behaviours of what is a very complex system...
  
  
• Color matching function
• Color space
  Color space
  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...
  
  
• Color appearance model
• Color management system
• Color mapping
  Color mapping
  Color mapping is a function that maps the colors of one image to the colors of another image. A color mapping may be referred to as the algorithm that results in the mapping function or the algorithm that transforms the image colors...
  
  
• Color profile

Feature extraction

• Active contour
  Active contour
  Active contour model, also called snakes, is a framework for delineating an object outline from a possibly noisy 2D image.This framework attempts to minimize an energy associated to the current contour as a sum of an internal and external energy:...
  
  
• Blob detection
  Blob detection
  In the area of computer vision, blob detection refers to visual modules that are aimed at detecting points and/or regions in the image that differ in properties like brightness or color compared to the surrounding...
  
  
• Canny edge detector
• Contour detection
• Edge detection
  Edge detection
  Edge detection is a fundamental tool in image processing and computer vision, particularly in the areas of feature detection and feature extraction, which aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities...
  
  
• Edge linking
• Harris corner detector
• Random sample consensus
  RANSAC
  RANSAC is an abbreviation for "RANdom SAmple Consensus". It is an iterative method to estimate parameters of a mathematical model from a set of observed data which contains outliers. It is a non-deterministic algorithm in the sense that it produces a reasonable result only with a certain...
  
   (RANSAC)
• Scale-invariant feature transform
  Scale-invariant feature transform
  Scale-invariant feature transform is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999....
  
   (SIFT)

Pose estimation

• Bundle adjustment
  Bundle adjustment
  Given a set of images depicting a number of 3D points from different viewpoints, bundle adjustment can be defined as the problem of simultaneously refining the 3D coordinates describing the scene geometry as well as the parameters of the relative motion and the optical characteristics of the camera...
  
  
• Articulated body pose estimation
  Articulated body pose estimation
  Articulated body pose estimation, in computer vision, is the study of algorithms and systems that recover the pose of an articulated body, which consists of joints and rigid parts using image-based observations...
  
   (BoPoE)
• Direct linear transformation (DLT)
• Epipolar geometry
  Epipolar geometry
  Epipolar geometry is the geometry of stereo vision. When two cameras view a 3D scene from two distinct positions, there are a number of geometric relations between the 3D points and their projections onto the 2D images that lead to constraints between the image points...
  
  
• Fundamental matrix
• Pinhole camera model
  Pinhole camera model
  The pinhole camera model describes the mathematical relationship between the coordinates of a 3D point and its projection onto the image plane of an ideal pinhole camera, where the camera aperture is described as a point and no lenses are used to focus light...
  
  
• Projective geometry
  Projective geometry
  In mathematics, projective geometry is the study of geometric properties that are invariant under projective transformations. This means that, compared to elementary geometry, projective geometry has a different setting, projective space, and a selective set of basic geometric concepts...
  
  
• Trifocal tensor
  Trifocal tensor
  In computer vision, the trifocal tensor is a 3×3×3 array of numbers that incorporates all projective geometric relationshipsamong three views...
  
  

Registration

• Active appearance model
  Active Appearance Model
  An active appearance model is a computer vision algorithm for matching a statistical model of object shape and appearance to a new image. They are built during a training phase...
  
   (AAM)
• Cross correlation
• Geometric hashing
  Geometric hashing
  In computer science, geometric hashing is originally a method for efficiently finding two-dimensional objects represented by discrete points that have undergone an affine transformation, though extensions exist to some other object representations and transformations. In an off-line step, the...
  
  
• Graph cut segmentation
• Least squares estimation
  Least squares
  The method of least squares is a standard approach to the approximate solution of overdetermined systems, i.e., sets of equations in which there are more equations than unknowns. "Least squares" means that the overall solution minimizes the sum of the squares of the errors made in solving every...
  
  
• Image pyramid
  Pyramid (image processing)
  Pyramid or pyramid representation is a type of multi-scale signal representation developed by the computer vision, image processing and signal processing communities, in which a signal or an image is subject to repeated smoothing and subsampling...
  
  
• Image segmentation
• Level set method
  Level set method
  The level set method is a numerical technique for tracking interfaces and shapes. The advantage of the level set method is that one can perform numerical computations involving curves and surfaces on a fixed Cartesian grid without having to parameterize these objects...
  
  
• Markov random fields
• Medial axis
  Medial axis
  The medial axis of an object is the set of all points having more than one closest point on the object's boundary. Originally referred to as the topological skeleton, it was introduced by Blum as a tool for biological shape recognition....
  
  
• Motion field
  Motion field
  In computer vision the motion field is an ideal representation of 3D motion as it is projected onto a camera image. Given a simplified camera model, each point in the image is the projection of some point in the 3D scene but the position of the projection of a fixed point in space can vary with...
  
  
• Motion vector
  Motion vector
  In video compression, a motion vector is the key element in the motion estimation process. It is used to represent a macroblock in a picture based on the position of this macroblock in another picture, called the reference picture....
  
  
• Multispectral imaging
• Normalized cut segmentation
• Optical flow
  Optical flow
  Optical flow or optic flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer and the scene. The concept of optical flow was first studied in the 1940s and ultimately published by American psychologist James J....
  
  
• Particle filter
  Particle filter
  In statistics, particle filters, also known as Sequential Monte Carlo methods , are sophisticated model estimation techniques based on simulation...
  
  ing
• Scale space
  Scale space
  Scale-space theory is a framework for multi-scale signal representation developed by the computer vision, image processing and signal processing communities with complementary motivations from physics and biological vision...
  
  

Visual Recognition

• Object Recognition
  Object recognition
  Object recognition in computer vision is the task of finding a given object in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes / scale...
  
  
• Scale-invariant feature transform
  Scale-invariant feature transform
  Scale-invariant feature transform is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999....
  
   (SIFT)
• Gesture Recognition
  Gesture recognition
  Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Current focuses in the field include emotion...
  
  
• Bag of words model in computer vision
  Bag of words model in computer vision
  This is an article introducing the "Bag of words model" in computer vision, especially for object categorization. From now, the "BoW" model refers to the BoW model in computer vision unless explicitly declared. This technique is also known as "Bag of Features model".Before introducing the BoW...
  
  
• Kadir–Brady saliency detector
• Eigenface
  Eigenface
  Eigenfaces are a set of eigenvectors used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Sirovich and Kirby and used by Matthew Turk and Alex Pentland in face classification. It is considered the first successful example...
  
  

See also

• List of computer graphics and descriptive geometry topics
• Virtual Design and Construction
  Virtual Design and Construction
  Virtual Design and Construction is the management of integrated multi-disciplinary performance models of design-construction projects, including the product , work processes and organization of the design - construction - operation team in order to support explicit and public business objectives...