2DPCA Face Recognition Program with MATLAB Implementation
A MATLAB-based 2DPCA face recognition program achieving at least 10x faster processing speeds compared to traditional PCA methods while delivering superior recognition accuracy
Explore MATLAB source code curated for "运行速度" with clean implementations, documentation, and examples.
A MATLAB-based 2DPCA face recognition program achieving at least 10x faster processing speeds compared to traditional PCA methods while delivering superior recognition accuracy
Application Background: A sample-based texture synthesis image inpainting algorithm built upon the classic Criminisi algorithm, capable of restoring both grayscale and color images. Enhanced with C programming for improved execution speed, includes test images, thoroughly tested code ready for immediate execution. Key Technology: The core of Criminisi algorithm is an isophote-driven image sampling process.
Complete MATLAB implementation of FIR algorithm with excellent execution speed and comprehensive code examples
This paper addresses the computational intensity and prolonged runtime of traditional FCM algorithms by proposing an enhanced FCM approach. The method involves dividing images into window-sized sub-blocks, extracting feature vectors at the sub-block level for coarse FCM clustering, followed by pixel-level feature extraction and fine segmentation specifically for edge sub-blocks. This hierarchical segmentation strategy significantly improves processing speed and segmentation accuracy through optimized computational resource allocation.
NSGA-II is one of the most popular multi-objective genetic algorithms that reduces the complexity of non-dominated sorting genetic algorithms. It features fast execution speed, excellent solution set convergence, and serves as a benchmark for evaluating other multi-objective optimization algorithms.
My improved adaptive segmentation approach incorporates the threshold obtained from Otsu's method as the initial threshold, effectively enhancing both processing speed and segmentation performance
An improved adaptive median filter demonstrating superior noise reduction performance while maintaining high computational efficiency and preserving fine image details.
Validated implementation with optimal performance and visualization capabilities - suitable for dynamical systems analysis