Abstract
In the digital era, image edge detection plays an important role in various image processing applications such as medical analysis and object recognition. This research develops a hybrid model that combines the high accuracy of deep learning with the efficiency and robustness to noise of morphological processing to improve edge detection performance. The method used involves deep learning models for image feature extraction and morphological processing techniques to improve detection results. The research results show that this hybrid model is able to overcome the weaknesses of traditional methods and pure deep learning by producing edge detection that is smoother and more continuous, and more efficient in processing time. Tests on the BSDS500 dataset and medical images show that this model provides consistent performance in a variety of image conditions, including images with high noise and low contrast. This hybrid approach offers a practical and effective solution for a variety of image processing applications.
Published Version
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