Pattern recognition has applications in computer vision, image segmentation, object detection, radar processing, speech recognition, and text classification, among others. There are two classification methods in pattern recognition: supervised and unsupervised classification.

This example shows how to use the 2-D normalized cross-correlation for pattern matching and target tracking. The example uses predefined or user specified target and number of similar targets to be tracked.

Jul 15, 2013 · My goal was to find the best linear fit of a data set, in which only a part of it was linear. Here is an example of what I must do : We have these two data sets, and I must make a linear trend of the linear part of the data that is at the left of the dashed line.

Convolve your signal with a 1st derivative of a Gaussian to find the step positions, similar to a Canny edge detection in 1-D. You can do that in a multi-scale approach, starting from a "large" sigma (say ~10 pixels) detect local maxima, then to a smaller sigma (~2 pixels) to converge on the right pixels where the steps are.

1. Excellent support for linear algebra and matrix operations. The ba-sic type in Matlab is a double precision matrix. The software was originally developed as a linear algebra package (Matlab stands for MATrix LABoratory) and has e cient and numerically reliable algo-rithms for matrix inversion, eigenvalues etc. 2. Visualisation facilities.

You can use this localization of predicted anomalies to help explain why an image is classified as normal or anomalous. This approach is useful for identifying patterns in false negatives and false positives. You can use these patterns to identify strategies to improve the classifier performance. Calculate Heatmap Display Range

Jun 21, 2014 · In this work we describe a MATLAB toolbox for pattern recognition and machine learning known as the PRT (Pattern Recognition Toolbox), licensed under the permissive MIT license.

Jul 12, 2017 · Two methods are proposed, one using cross correlation the other using brightness. They work with both grayscale and multiband images. You should play with the settings a bit to improve the result. Important: fillmissing requires Matlab 2016b or newer.

Pattern recognition (Template matching) using background subtraction and extraction of local and global features. Global and Local Features: Global features describe the image as a whole to the generalize the entire object.

May 5, 2016 · There is a noticeable pattern, which I tried to characterize by fitting the curve $$ f(x)=\left(\frac{a}{x-b}\right)^2 $$ through manual parameter manipulation. However, I'd like to know other ways to approach modeling or finding patterns/relationships in data that looks like this.