1. Overview of Image Semantic Segmentation Technology
- Author
-
Hu Liangyuan, Chen Xingxing, Li Wang, and Hui Li
- Subjects
Structure (mathematical logic) ,Computer science ,business.industry ,Deep learning ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,0211 other engineering and technologies ,02 engineering and technology ,Image segmentation ,Machine learning ,computer.software_genre ,Semantics ,Image (mathematics) ,0202 electrical engineering, electronic engineering, information engineering ,Unsupervised learning ,020201 artificial intelligence & image processing ,Segmentation ,Artificial intelligence ,business ,Focus (optics) ,computer ,021101 geological & geomatics engineering - Abstract
With the continuous development of artificial intelligence technology, image semantic segmentation has increasingly become the focus of computer vision and artificial intelligence algorithms. Image semantic segmentation technology has been applied in remote sensing satellite images, agricultural products production, medical diagnosis and treatment and other fields, and these fields need very accurate and effective semantic segmentation technology. This paper summarizes the common semantic segmentation techniques, introduces the main algorithms of image semantic segmentation techniques, mainly explains the image semantic segmentation techniques based on traditional machine learning and image semantic segmentation techniques based on deep learning, expounds the structure of each method, summarizes the performance and effect of each method, and finally expounds our views on the future development direction and challenges of semantic segmentation.
- Published
- 2020