MATLAB Implementation of ASM Algorithm Proposed by Cootes

This article presents a MATLAB implementation of the Active Shape Model (ASM) algorithm proposed by Cootes. Our implementation is based on Cootes' original classroom source code but has been substantially enhanced to better serve research needs. Key improvements include correction of several critical bugs in the original codebase and the addition of a streamlined sample training label preservation system. The implementation now features robust data management capabilities through MATLAB's native save/load functions, ensuring researchers can efficiently handle training datasets for image analysis, pattern recognition, and AI applications. The code architecture maintains the core ASM methodology including statistical shape modeling through Principal Component Analysis (PCA) and iterative model fitting using landmark point adjustment. We have incorporated detailed documentation and practical usage examples to assist beginners in understanding the algorithm's underlying mechanics. The training pipeline now automatically serializes shape models and landmark configurations, enabling reproducible experiments and incremental model development. Additional enhancements include optimized image preprocessing routines and more flexible parameter configuration for different application scenarios. We believe this implementation provides a solid foundation for researchers to explore ASM algorithm applications and extensions, with particular attention to computational efficiency and code maintainability. The modular design allows easy integration of custom features while preserving the algorithm's theoretical integrity.