Edge Detection Algorithm Optimization and Simulation Based on Machine Learning Method and Image Depth Information

Machine learning algorithms have become a hot topic in current research due to their unique learning performance, and have achieved fruitful research and application results in various fields. In this paper, the idea of machine learning classification algorithm is applied to depth image edge detection, AdaBoost algorithm and decision tree are used for image edge detection. The algorithm is created from training set creation, depth image feature extraction and combination of AdaBoost and image depth information, creating image training sample sets, selecting image features, training algorithm classifiers, and simulating medical ultrasound image classifiers. Finally, the machine learning algorithm was simulated and tested. The experimental results show that the edge detection effect is good, the algorithm adaptability is strong, and no adjustment parameters are needed.