Adaptive Threshold Segmentation and Edge Detection Algorithm Integrating Canny Operator

The Canny operator represents a classical edge detection algorithm that employs multi-stage processing to identify edges within digital images. Meanwhile, adaptive threshold segmentation dynamically adjusts threshold values based on local image characteristics, resulting in more accurate and robust edge extraction. The integration of these two methodologies significantly enhances edge detection performance, particularly in challenging scenarios involving non-uniform illumination or low-contrast images. When implementing this algorithm in MATLAB, the initial step involves image preprocessing through Gaussian filtering to mitigate noise interference. Subsequently, gradient operators such as Sobel are employed to compute gradient magnitude and orientation maps. The core innovation of the Canny algorithm lies in its edge refinement through non-maximum suppression, followed by edge connectivity using dual thresholds (high and low thresholds). The adaptive threshold mechanism operates by dynamically setting threshold values according to local statistical characteristics (e.g., mean or median values) rather than relying on global fixed thresholds. This approach demonstrates superior adaptability to local image variations, effectively reducing noise interference while improving edge detection precision. The final integration with Canny's edge-linking strategy ensures the extraction of continuous edge structures. This combined methodology exhibits distinct advantages in handling edge detection challenges across complex scenarios, including medical imaging applications, industrial inspection systems, and object recognition in natural environments. Leveraging MATLAB's matrix computation capabilities and Image Processing Toolbox functions, practitioners can efficiently implement this algorithm while further optimizing parameters to meet specific application requirements. Key implementation steps include utilizing imgaussfilt() for Gaussian smoothing, imgradient() for gradient calculation, and custom functions for adaptive threshold determination based on local neighborhood statistics.