Explore MATLAB source code curated for "特征提取" with clean implementations, documentation, and examples.
SOM neural networks perform feature extraction and pattern classification, particularly effective for high-dimensional feature spaces. Implementation typically involves competitive learning algorithms, neighborhood functions, and weight adaptation mechanisms.
This implementation calculates and outputs Hu's seven invariant moments, which are fundamental for image feature extraction. The code includes computation of central moments and normalization processes to achieve scale, rotation, and translation invariance.
This program implements feature extraction for input digit images, generating corresponding vector representations to facilitate subsequent recognition tasks. The implementation involves key computer vision algorithms for dimensionality reduction and pattern identification, typically utilizing techniques like HOG (Histogram of Oriented Gradients) or pixel intensity normalization to convert spatial image data into meaningful numerical features.
Linear Discriminant Analysis (LDA) is a widely-used linear classification method for feature extraction, but its direct application to ear recognition faces dimensionality and small sample size problems. Researchers have developed multiple solutions to address these challenges, implementing various LDA-based ear recognition approaches. This article provides theoretical comparisons and experimental validation of four methods: Fisherears, DLDA, VDLDA, and VDFLDA, with implementation insights and performance analysis demonstrating VDFLDA's superiority.
The code in the file primarily implements functionalities including obstacle removal for occluded persons in images, image upscaling and enhancement, image denoising (mainly using wavelet denoising, wavelet decomposition denoising, and threshold-based denoising), facial feature extraction, height feature extraction, and finally person identification.
Feature Extraction for Input Images Using Gabor Wavelet Transform with Implementation Details
An order analysis toolkit package designed for non-stationary signal feature extraction, providing efficient algorithms and implementations for frequency-amplitude characteristic analysis.
Source code for PCA feature extraction, particularly useful for face recognition applications with algorithm implementation details
Content-based feature extraction for image recognition - segmenting images using k-means clustering to obtain shape information and other key features
This code implements fingerprint recognition through preprocessing, feature extraction, and classification algorithms using image processing and machine learning techniques.