Research on the Improvement of Image Segmentation Based on the Combination of Semi-Supervised Learning and Computer Vision

The paper discusses the combination of semi-supervised learning with computer vision to improve image segmentation. Image segmentation is the process of dividing an image into homogeneous regions based on distinct characteristics. Traditional image segmentation methods have limitations, and the paper highlights the advantages of using geometric active contour curves driven by an energy function composed of prior models and image data. Semi-supervised learning combines supervised and unsupervised learning, leveraging limited guidance information to enhance learning using labeled and unlabeled samples. The paper emphasizes that image segmentation algorithms are challenging to design due to the need for a balance between homogeneity, flatness, completeness, and differences between segmented regions. Researchers have proposed various theories and methods to address computer vision problems, recognizing that image segmentation is a fundamental aspect of computer vision. The paper also mentions the different types of image segmentation techniques, such as edge-based methods and region-based methods. In conclusion, the paper explores the combination of semi-supervised learning and computer vision techniques to address the challenges of image segmentation, highlighting the importance of balancing different factors in achieving accurate segmentation results.