Explore MATLAB source code curated for "分块" with clean implementations, documentation, and examples.
This study addresses the sparse reconstruction problem in compressed sensing by introducing a block-based strategy to the original Matching Pursuit (MP) algorithm, where the atom dictionary is partitioned into blocks to evaluate algorithm performance through systematic testing.
MATLAB implementation of Modular PCA algorithm using Yale Face Database as example. The code performs image partitioning, PCA feature extraction on sub-images, projection coefficient calculation, and nearest neighbor classification with detailed code structure explanation.
Image segmentation using red line division to achieve image matrix blocking - a highly simplified and efficient implementation approach
This program provides MATLAB source code for separating pedestrians from background using pre-extracted HOG features. The implementation includes both blocked and non-blocked feature extraction approaches, where blocking involves dividing images into smaller segments for more detailed feature representation. Classification is performed using minimum Euclidean distance method for enhanced pedestrian detection accuracy.
Image transformation techniques involving 8x8 block partitioning, Discrete Cosine Transform (DCT) for compression encoding, and Inverse DCT (IDCT) for decompression, with code implementation insights
Improving Signal Reconstruction Accuracy in Block Compressed Sensing via Directional-Enhanced Sparse Representation Methods with Implementation Approaches
A color-based image retrieval system featuring dual image databases and implementing a block-based image retrieval approach for enhanced matching accuracy.
This implementation demonstrates image compression using 8x8 block-based Discrete Cosine Transform (DCT) and reconstruction via Inverse DCT (IDCT): DCT serves as a separable transform for image compression encoding, while IDCT handles decompression. The process involves dividing images into blocks, applying mathematical transformations, and reconstructing with minimal quality loss.
Implementation of 8x8 Block-Based DCT Transform Coding in MATLAB Programming Environment
Performing block partitioning, Discrete Cosine Transform (DCT), and quantization on test images, representing the quantized coefficients in matrix form, with implementation insights using key functions.