Active Growth-Based Edge Connection Algorithm

This is an active growth-based edge connection algorithm that I developed independently. The algorithm represents a computational approach for image processing that automatically detects and connects edges to generate more complete image structures. Its core principle operates on the concept of active growth - starting from seed points, the algorithm progressively expands and links edges until meeting predefined termination conditions. Key implementation aspects include: - Seed point initialization and neighborhood exploration mechanisms - Gradient-based edge detection and continuity evaluation functions - Growth direction control using priority queues and cost functions - Termination conditions based on edge strength thresholds and connection completeness The algorithm's primary advantage lies in its autonomous edge processing capability, minimizing manual intervention requirements while maintaining adaptability across diverse image processing tasks. My development process involved thorough research of relevant literature and algorithmic principles, followed by complete implementation. Through this hands-on development, I gained deep understanding of the internal mechanics, enabling future modifications and enhancements. The algorithm demonstrates significant application potential across multiple domains including medical image analysis, computer vision systems, and image recognition frameworks. Its modular design allows for integration with existing image processing pipelines while maintaining computational efficiency.