Ci Liang

Title: Innovations of Ci Liang in Semantic Segmentation

Introduction

Ci Liang is a notable inventor based in Gothenburg, Sweden. He has made significant contributions to the field of semantic segmentation through his innovative methods. His work focuses on enhancing the accuracy and reliability of image data classification.

Latest Patents

Ci Liang holds a patent for a "Semantic segmentation network model uncertainty quantification method based on evidence inference." This method involves constructing a Fully Convolutional Network (FCN) model and training it with a dataset to achieve a trained model for semantic segmentation. The process includes applying Dempster-Shafer theory to the trained model, allowing for uncertainty quantification in the classification results of image data.

Career Highlights

Ci Liang is affiliated with Beijing Jiaotong University, where he contributes to research and development in the field of image processing and machine learning. His expertise in semantic segmentation has positioned him as a valuable asset in academic and research circles.

Collaborations

Ci Liang has collaborated with notable colleagues such as Rui Wang and Wei Zheng. Their combined efforts have further advanced the research in semantic segmentation and related technologies.

Conclusion

Ci Liang's innovative work in semantic segmentation and his contributions to the field through his patent demonstrate his commitment to advancing technology. His research continues to influence the landscape of image data classification.