Explore MATLAB source code curated for "迭代过程" with clean implementations, documentation, and examples.
A semi-supervised image segmentation algorithm based on region fusion. The algorithm begins with manual initialization of foreground and background segmentation markers, then iteratively performs region fusion operations to identify regions with maximum similarity, ultimately achieving accurate object segmentation. Key implementation aspects include similarity metric computation and hierarchical region merging strategies.
The CA algorithm effectively partitions numerical attributes into optimized intervals by integrating the advantages of both hierarchical clustering and partition-based clustering approaches. For varying initial cluster counts, this algorithm dynamically adjusts the number of classes during iteration. Classes with poor competitiveness - those with cardinality below a specified threshold - are progressively eliminated. The final output yields an optimized number of clusters that accurately reflects the actual data distribution pattern, making it particularly suitable for preprocessing continuous variables in machine learning pipelines through automated binning procedures.
Mean shift image segmentation implementation with three key simplifications: 1) Single iteration of mean shift process (for simplicity) 2) Neighborhood limitation using 2*hr radius during pixel translation 3) Exclusive use of Gaussian kernel for range weighting despite method selection option. Code modifications suggested for enhanced functionality.
Implementation of the Jacobian matrix iterative solution process using numerical methods, including simulation results and computational algorithm analysis