Correlation Pattern Recognition

By B. V. Vijaya Kumar, Abhijit Mahalanobis, R. Juday. 2002

402 citations recorded by the source corpus.

Correlation is a robust and general technique for pattern recognition and is used in many applications, such as automatic target recognition, biometric recognition and optical character recognition. The design, analysis and use of correlation pattern recognition algorithms requires background information, including linear systems theory, random variables and processes, matrix/vector methods, detection and estimation theory, digital signal processing and optical processing. This 2005 book provides a needed review of this diverse background material and develops the signal processing theory, the pattern recognition metrics, and the practical application know-how from basic premises. It shows both digital and optical implementations. It also contains technology presented by the team that developed it and includes case studies of significant interest, such as face and fingerprint recognition. Suitable for graduate students taking courses in pattern recognition theory, whilst reaching technical levels of interest to the professional practitioner.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Representation & Self-Supervised Learning, Signal Processing

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In Computer Vision and Pattern Recognition

In Media Technology

In Representation & Self-Supervised Learning

In Signal Processing

Citations in the corpus

Meta-learning with differentiable closed-form solvers (2018, methodology): “efore any adaptation. In similar spirit, Valmadre et al. [39] propose a method to backpropagate through the solution of a closed-form problem. However, they resort to the Correlation Filter algorithm [40], whose application is limited to scenarios in which the data matrix is circulant, such as objec”
Need for Speed: A Benchmark for Higher Frame Rate Object Tracking (2017, background): “Further, CFs take advantage of intrinsic computational efficiency afforded from operating in the Fourier domain [21, 14, 3].”
A survey of palmprint recognition (2009, background): “Correlation filter is a type of classifiers, extensively studied by Vijaya Kumar and his coworkers [106].”
Long-term correlation tracking (2015, methodology): “Correlation filters have been widely used in numerous applications such as object detection and recognition [15].”
Maximum Margin Correlation Filter: A New Approach for Localization and Classification (2013, neutral): “Most of these approaches cross-correlate the template with the query image and are sometimes known as sliding window algorithms.”
Cancelable Biometrics: A review (2015, methodology): “Motivated by the success of the correlation filter-based methods in pattern recognition and computer vision applications [22], a random convolution method for generating cancelable biometric templates was proposed in [23].”
Learning Background-Aware Correlation Filters for Visual Tracking (2017, methodology): “Correlation filters are typically learned in the frequency domain, for computational efficiency [20].”
Correlation filters with limited boundaries (2014, background): “Although many variants have been proposed [15, 18, 20, 19], the approach’s central tenet is to learn a filter, that when correlated with a set of training signals, gives a desired response, e.”