DoG (Difference of Gaussian) Filter Operator

The DoG (Difference of Gaussian) filter operator is primarily used for edge feature extraction and serves as preprocessing for segmentation in pattern recognition. Its main parameters are the variances of two Gaussian functions, and designing appropriate variance values enables different performance characteristics for various image features. In implementation, the algorithm involves applying two Gaussian blur filters with different standard deviations and subtracting the results to enhance edge detection.

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MATLAB Implementation of Backpropagation Neural Network Algorithm

Backpropagation Neural Network Algorithm for Pattern Recognition of Equipment Fault Types - Transformer Fault Identification Case Study with Simulation Data

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Parzen Window Classification in Pattern Recognition with Simulation Example

A simple simulation of Parzen window classification in pattern recognition, where female.txt and male.txt serve as training samples and test.txt contains test samples. This implementation demonstrates excellent classification performance and provides valuable insights for beginners learning pattern recognition, including practical code structure and algorithm implementation details.

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Bayesian Decision Theory for Pattern Recognition Applications

Implementation of fundamental Bayesian decision theory algorithms in MATLAB for pattern recognition systems, providing core probabilistic classification functionality

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Face Feature Extraction Using LDA and PCA Pattern Recognition Methods

This MATLAB program framework implements face feature extraction using LDA (Linear Discriminant Analysis) and PCA (Principal Component Analysis) pattern recognition methods, offering excellent performance for facial recognition applications.

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Image Segmentation, Pattern Recognition, and Text Recognition with MATLAB Code Implementation

Comprehensive MATLAB programs and code examples covering image segmentation, pattern recognition, and text recognition algorithms, including beginner-friendly implementations with detailed explanations of key functions and processing techniques.

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QR Code Pattern Recognition

QR two-dimensional barcode pattern recognition effectively identifies barcode pattern information through image processing algorithms

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Application of RBF Neural Networks in Pattern Recognition

RBF Neural Networks for Pattern Recognition with Implementation Insights

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Pattern Recognition Classification with C-Means Algorithm, HK Algorithm, and Perceptron Algorithm

Pattern recognition classification implemented using C-means clustering algorithm, Ho-Kashyap (HK) algorithm, and perceptron algorithm with code implementation details

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An Excellent Pattern Recognition Assignment with Key Algorithms Implementation

A comprehensive pattern recognition assignment covering linear classifiers, minimum risk Bayesian classifiers, supervised learning hierarchical clustering analysis, K-L transform for feature extraction, and support vector machines with implementation approaches

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