Yu-qian Zhao is a professor and Ph.D supervisor in School of Information Science and Engineering of Central South University.
He received the B.S. degree in applied geophysics, M.S. degree in control theory and engineering, and Ph.D. degree in computer applications from Central South University, China, in 1997, 2002 and 2006, respectively. After that, he engaged in postdoctoral research at Xiangya School of Medicine, Changsha, China, from July 2007 to July 2009, at New Jersey Institute of Technology, Newark, USA, from July 2009 to August 2010, and at National Engineering Research Center of Advanced Energy Storage Materials, Changsha, China, from July 2012 to September 2015. And he visited Ecole Centrale de Lyon, France, as a senior visiting scholar from January to February 2015. He became an associate professor of Central South University in September 2006, and was promoted to a professor in September 2012. From June 2002 to July 2006, he was appointed as the associated director of Institute of Biomedical Engineering, and from December 2005 to October 2010, the associated director of College of Information Physics Engineering in Central South University. In 2013, he was awarded New Century Excellent Talents in University of Education Ministry of China.
Prof. Zhao is now the associate director of Hunan Machine Vision and Intelligent Medical Research Center, and the member of Hunan Association for Artificial Intelligence (HAAI), Computer Vision Committee of China Computer Federation (CCF-CV), Visual Big Data Committee of China Society of Image and Graphics (CSIG-VBD), Biomedical Information and Control Committee of Chinese Society of Biomedical Engineering (CSBME-BIC), Mixed Intelligence Committee of Chinese Association of Automation (CAA-MI), and Smart Medical Care Committee of Chinese Association of Artificial Intelligence (CAAI-SMC).
His major teaching courses include Image processing, Methods and applications of object detection and recognition, Pattern recognition, Automatic control theory, Signals and systems, etc. He has instructed national and provincial innovative experimental projects several times and has trained many outstanding students, including more than 50 postgraduates, and 8 ph.Ds. With an excellent power for teaching, he has won “Excellent teaching quality award”, “Experimental teaching achievement award”, “Educational teaching achievement award”, and “Teacher excellence award of Xinheng educational foundation” many times, and won “Faculty Advisor Award for excellent master’s dissertation in Hunan Province” two times.
His major research interests include intellisense, autopilot, computer vision, image processing, machine learning, smart medical care, artificial intelligence, surface inspection, etc. During the past several years, he has published over 100 peer-reviewed papers in international journals, including over 50 papers covered by Science Citation Index (SCI), and has been authorized with more than 40 invent patents, 3 utility model patents, and 3 software copyrights. Besides, he has presided over 4 projects of the National Natural Science Foundation of China and 15 provincial and ministerial scientific research projects. The research production of project “Research of Digital Geological Data Tampering Detection Method and Application” was identified as the advanced domestic level by Hunan Provincial Department of Land and Resources. The project “Image Analysis for Multi-object Detection and Identification” won the second class prize of Hunan Provincial Natural Science Award in 2017.

一、

Course Name: Digital Image Processing

Objectives and requirements: To enable students to understand the concept and types of digital images, and master the following principles and methods of digital image processing: image transform, image enhancement, image coding, image restoration and reconstruction.

Recommended materials: Digital Image Processing (Chinese version Third Edition) [ Gonzalez] Electronic Industry Press 2011

Reference materials: Liu Hui et al. Medical Image and Medical Image Processing, Electronic Industry Press 2013;

二、

Course Name: Automatic Control Theory

Objectives and requirements:To enable students to master the basic theory of automatic control, to conduct qualitative analysis and quantitative estimation of simple continuous system and preliminary design, and to prepare for learning courses and participating in Control Engineering Practice. Students will master the basic methods of automatic control system analysis and design, such as time domain analysis of control system, root locus analysis, frequency domain analysis, state space analysis, sampling control system analysis, etc. 

Recommended materials:The Principle of Automatic Control [Hu Shousong] Science Press 2007

Reference materials:The Principle of Automatic Control Simple Tutorial [Hu Shousong] Science Press 2008

三、

Course Name:Image Detection and Object Tracking

Objectives an requirements: To enable students to master the latest technologies of image detection, image acquisition and image processing, including image enhancement, filtering, compression, correction, and edge detection etc.

Recommended materials:Image Detection and Target Tracking Technology [Li Jing] Beijing Institute of Technology press 2014

Professor Zhao's main research area is image processing, focusing on medical image segmentation, pattern recognition, industrial image detection and other branches of research. Medical image segmentation is mainly in the field of liver segmentation, abdominal multi-organ segmentation, liver vessel segmentation, retinal vessel segmentation, cell division, etc. Pattern recognition has made some achievements in the field of natural scene text detection, image tamper detection and so on. Industrial image detecting focuses on defect detection of metallic foams.

Medical image segmentation is the key technology of medical image processing and analysis. From medical research and clinical application perspective, image segmentation is the basis of medical image processing,medical diagnosis and target identification. It concerns a lot of fields such as object extraction, quantitative analysis, 3D reconstruction and so on. The purpose of medical image segmentation is to divide the original 2D or 3D image into multiple regions with different properties (such as gray scale, texture, etc.), and thus the regions of interest can be extracted, providing reliable basis for clinical diagnosis and pathological study.

Image segmentation is a process of dividing the images into several regions according to the similarity between one region and another region. However, the medical image background is pretty complex, with severe noise and blurring boundaries between organs, which obstructs the development of medical image segmentation. The laboratory, working for these difficulties, had made some achievements, and some of the related papers are as follows:

[1] Automatic segmentation for cell images based on bottleneck detection and ellipse fitting

[2] A New Approach to Segment Both Main and Peripheral Retinal Vessels Based on Gray-Voting and Gaussian Mixture Model

[3] Retinal vessels segmentation based on level set and region growing

[4] Retinal vessel enhancement based on multi-scale top-hat transformation and histogram fitting stretching

[5] Fuzzy C-means Clustering-based Multilayer Perceptron Neural Network for Liver CT Images Automatic Segmentation

Some of medical image segmentation results:

(1) Liver segmentation results  (2)3D liver vessels segmentation results  (3)Retinal vessel segmentation results  (4)Cell segmentation results    

Pattern recognition refers to processing and analyzing numerical, textual and logical relationships that characterize various forms of phenomena to describe, identify, classify and interpret targets, which is important for information science and artificial intelligence. In general, pattern recognition is to classify and identify target objects with multiple techniques, including machine learning, artificial neural networks, deep learning, etc. The laboratory has focused on the natural scene text recognition and image tampering detection, and has achieved certain results. Some of the related papers are as follows:

[6] Scene text detection using adaptive color reduction, adjacent character model and hybrid verification strategy

[7] An automatic video text detection method based on BP-adaboost

[8] Tampered region detection of inpainting JPEG images

[9] Detection of tampered region for JPEG images by using mode-based first digit features

[10] Passive detection of copy-paste forgery between JPEG images

[11] Passive Detection of Paint-Doctored JPEG Images

[12] Passive Detection of Copy-paste Tampering for Digital Image Forensics

Some of pattern recognition results:

                (1)Natural scene text detection results                                              (2)Forged images                                  (3)Tampered area      

Industrial image detection, using machine vision instead of the human eye for the detection of industrial products, can effectively improve the quality of products and reduce product cost. In this part, the laboratory has mainly focused on the defects of metal foam images, and achieved certain results. Some of the related patents are as follows:

[1] A method for on-line detecting skip plating defect of continuous strip foam metal material

[2] A method for on-line automatic detection of cavity defect of porous metal material

[3] A method for position skip plating area of continuous strip of porous metal materials

[4] Method of determining a continuous strip of porous metal material skip plating defects

[5] A method for detecting and position skip plating area of continuous strip of porous metal materials

Foam metal image defect partial detection results show:

      (1)Hole detection results                        (2)Crack detection results