|
Mean-shift Algorithm
Mean shift is a non-parametric feature-space mathematical analysis technique for locating the maxima of a density function, a so-called mode-seeking algorithm. Application domains include cluster analysis in computer vision and image processing. History The mean shift procedure is usually credited to work by Fukunaga and Hostetler in 1975. It is, however, reminiscent of earlier work by Schnell in 1964. Overview Mean shift is a procedure for locating the maxima—the modes—of a density function given discrete data sampled from that function. This is an iterative method, and we start with an initial estimate x . Let a kernel function K(x_i - x) be given. This function determines the weight of nearby points for re-estimation of the mean. Typically a Gaussian kernel on the distance to the current estimate is used, K(x_i - x) = e^ . The weighted mean of the density in the window determined by K is : m(x) = \frac where N(x) is the neighborhood of x , a set of p ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Non-parametric
Nonparametric statistics is a type of statistical analysis that makes minimal assumptions about the underlying distribution of the data being studied. Often these models are infinite-dimensional, rather than finite dimensional, as in parametric statistics. Nonparametric statistics can be used for descriptive statistics or statistical inference. Nonparametric tests are often used when the assumptions of parametric tests are evidently violated. Definitions The term "nonparametric statistics" has been defined imprecisely in the following two ways, among others: The first meaning of ''nonparametric'' involves techniques that do not rely on data belonging to any particular parametric family of probability distributions. These include, among others: * Methods which are ''distribution-free'', which do not rely on assumptions that the data are drawn from a given parametric family of probability distributions. * Statistics defined to be a function on a sample, without dependency on a pa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
K-means
''k''-means clustering is a method of vector quantization, originally from signal processing, that aims to partition of a set, partition ''n'' observations into ''k'' clusters in which each observation belongs to the cluster (statistics), cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. ''k''-means clustering minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances. For instance, better Euclidean solutions can be found using k-medians clustering, ''k''-medians and k-medoids, ''k''-medoids. The problem is computationally difficult (NP-hardness, NP-hard); however, efficient heuristic algorithms converge quickly to a local optimum. These are usually similar to the ex ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Kernel Density Estimation
In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on '' kernels'' as weights. KDE answers a fundamental data smoothing problem where inferences about the population are made based on a finite data sample. In some fields such as signal processing and econometrics it is also termed the Parzen–Rosenblatt window method, after Emanuel Parzen and Murray Rosenblatt, who are usually credited with independently creating it in its current form. One of the famous applications of kernel density estimation is in estimating the class-conditional marginal densities of data when using a naive Bayes classifier, which can improve its prediction accuracy. Definition Let be independent and identically distributed samples drawn from some univariate distribution with an unknown density at any given point . We are in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
OPTICS Algorithm
Ordering points to identify the clustering structure (OPTICS) is an algorithm for finding density-based Cluster analysis, clusters in spatial data. It was presented in 1999 by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jörg Sander. Its basic idea is similar to DBSCAN, but it addresses one of DBSCAN's major weaknesses: the problem of detecting meaningful clusters in data of varying density. To do so, the points of the database are (linearly) ordered such that spatially closest points become neighbors in the ordering. Additionally, a special distance is stored for each point that represents the density that must be accepted for a cluster so that both points belong to the same cluster. This is represented as a dendrogram. Basic idea Like DBSCAN, OPTICS requires two parameters: , which describes the maximum distance (radius) to consider, and , describing the number of points required to form a cluster. A point is a ''core point'' if at least points are found within it ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
DBSCAN
Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu in 1996. It is a Cluster analysis#Density-based clustering, density-based clustering non-parametric algorithm: given a set of points in some space, it groups together points that are closely packed (points with many Fixed-radius near neighbors, nearby neighbors), and marks as outliers points that lie alone in low-density regions (those whose nearest neighbors are too far away). DBSCAN is one of the most commonly used and cited clustering algorithms. In 2014, the algorithm was awarded the Test of Time Award (an award given to algorithms which have received substantial attention in theory and practice) at the leading data mining conference, ACM SIGKDD. , the follow-up paper "DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN" appears in the list of the 8 most downloaded articles of the presti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Scikit-learn
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, ''k''-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Scikit-learn is a NumFOCUS fiscally sponsored project. Overview The scikit-learn project started as scikits.learn, a Google Summer of Code project by French data scientist David Cournapeau. The name of the project derives from its role as a "scientific toolkit for machine learning", originally developed and distributed as a third-party extension to SciPy. The original codebase was later rewritten by other developers. In 2010, contributors Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort and Vincent Michel, from the French Institute for Research in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Orfeo Toolbox
In computer science, Orfeo Toolbox (OTB) is a software library for processing images from Earth observation satellites. OTB was initiated by the French space agency (CNES) in 2006. The software is released under a free licence; a number of contributors outside CNES are taking part in development and integrating into other projects. The library was originally targeted at high resolution images acquired by the Orfeo constellation: Pléiades and Cosmo-Skymed, but it also handles other sensors. Purpose OTB provides: * Image access: read/write access for most remote sensing image formats (using GDAL), meta-data access, visualization * Data access: vector data access (shapefile, kml), DEM model, lidar data * Filtering: blurring, denoising, enhancement for optical or radar data * Feature extraction: texture computations including Haralick, SFS, Pantex, Edge density, points of interest, alignments, lines, SIFT, SURF * Image segmentation: region growing, watershed, level set ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
OpenCV
OpenCV (Open Source Computer Vision Library) is a Library (computing), library of programming functions mainly for Real-time computing, real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage, then Itseez (which was later acquired by Intel). The library is cross-platform and licensed as free and open-source software under Apache License 2. Starting in 2011, OpenCV features GPU acceleration for real-time operations. History Officially launched in 1999, the OpenCV project was initially an Intel Research Lablets, Intel Research initiative to advance central processing unit, CPU-intensive applications, part of a series of projects including Real-time computing, real-time ray tracing (graphics), ray tracing and 3D Display, 3D display walls. The main contributors to the project included a number of optimization experts in Intel Russia, as well as Intel's Performance Library Team. In the early days of OpenCV, the goals of the project were describ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mlpack
mlpack is a free, open-source and header-only software library for machine learning and artificial intelligence written in C++, built on top of the Armadillo library and thensmallennumerical optimization library. mlpack has an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. mlpack has also a light deployment infrastructure with minimum dependencies, making it perfect for embedded systems and low resource devices. Its intended target users are scientists and engineers. It is open-source software distributed under the BSD license, making it useful for developing both open source and proprietary software. Releases 1.0.11 and before were released under the LGPL license. The project is supported by the Georgia Institute of Technology and contributions from ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
ImageJ
ImageJ is a Java (programming language), Java-based image processing program developed at the National Institutes of Health and the Laboratory for Optical and Computational Instrumentation (LOCI, University of Wisconsin). Its first version, ImageJ 1.x, is developed in the public domain, while ImageJ2 and the related projects SciJava, ImgLib2, and SCIFIO are licensed with a permissive BSD licenses, BSD-2 license. ImageJ was designed with an open architecture that provides extensibility via Java plug-in (computing), plugins and recordable macros. Custom acquisition, analysis and processing plugins can be developed using ImageJ's built-in editor and a Java compiler. User-written plugins make it possible to solve many image processing and analysis problems, from three-dimensional live-cell imaging to radiology, radiological image processing, multiple imaging system data comparisons to automated hematology systems. ImageJ's plugin architecture and built-in development environment has mad ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
ELKI
ELKI (''Environment for Developing KDD-Applications Supported by Index-Structures'') is a data mining (KDD, knowledge discovery in databases) software framework developed for use in research and teaching. It was originally created by the database systems research unit at the Ludwig Maximilian University of Munich, Germany, led by Professor Hans-Peter Kriegel. The project has continued at the Technical University of Dortmund, Germany. It aims at allowing the development and evaluation of advanced data mining algorithms and their interaction with database index structures. Description The ELKI framework is written in Java and built around a modular architecture. Most currently included algorithms perform clustering, outlier detection, and database indexes. The object-oriented architecture allows the combination of arbitrary algorithms, data types, distance functions, indexes, and evaluation measures. The Java just-in-time compiler optimizes all combinations to a simila ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gary Bradski
Gary Bradski is an American scientist, engineer, entrepreneur, and author. He co-founded Industrial Perception, a company that developed perception applications for industrial robotic application (since acquired by Google in 2012 ) and has worked on the OpenCV Computer Vision library, as well as published a book on that library. Education *Ph.D., Cognitive and Neural Systems (mathematical modeling of biological perception) May, 1994,Boston University Center for Adaptive Systems. *BS degree in EECS from U.C. Berkeley The OpenCV Library The OpenCV Library is a Computer Vision Software Library. Learning OpenCV Originally published in 2006, the book Learning OpenCV (O'Reilly) serves as an introduction to the library and its use. An updated version of the book], which covers OpenCV 3, was published by O'Reilly Media in 2016. Publications Bradski has published a wide variety of articles in computer science on the topics of computer vision and optimization. The following are his ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
