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Dr. Xinbo Gao's Research

As the name suggests, VIPs Lab focuses on the theoretical research and system development of video and image (visual information) processing (perception) based on the machine learning and deep neural networks. In addition, we also do some research on communications, especially the networking and Transmission on the high attitude platform. The detailed research topics include machine learning, heterogeneous image reconstruction or synthesis, image quality assessment, intelligent visual perception and remote sensing image processing and analysis. Some topics are given as follows.

Machine Learning	New Methods: Deep Learning, Transfer Learning, Reinforcement Learning etc New Models: Probabilistic Graph Model, Random Forest, HMM, GMM etc New Representation: Non-negative Matrix Decomposition, Glocal (Global-local) features, etc
Heterogenerous Image Reconstruction	Image Super-resolution Reconstruction from Multi-frame Images or Video Sequence Single Image Super-resolution Reconstruction based on Dictionary Learning or GAN-like networks High-resolution Face Reconstruction from Clips of Video Surveillance
	Image Synthesis from Photo to Sketch Image Synthesis from Sketch to Photo Frontal Face Synthesis from Multi-view Faces Please access theHIT Group'sWebpage
Image Quality Assessment	Full-reference Image or Video Quality Assessment Reduced-reference Image or Video Quality Assessment No-reference Image or Video Quality Assessment Image Quality Assessment Based on fidelity, intelligibility, and aesthetics Please access theIQAWebpage
Intelligent Image Perception	Content-based Image Retrieval and Applications to Wisdom Tourism etc Scene Perception based on New Topic Models Pattern Recognition: Face Recognition, Character Recognition etc
Remote Sensing Image Processing & Analysis	Remote Sensing Imaginary (RSI) based on Satellite, Unmanned Aerial Vehicle etc 3D Reconstruction based on Unmanned Aerial Vehicle RSI Enhancement (Defogging, Dehazing) and Quality Assessment Change Detection, Scene Perception and Object Recognition Other Applications to Medical Image Analysis, Aurora Image Analysis etc
Networking and Communications on HAPS	Networking on Demand for 3D Ad hoc Cooperative Transmission: Intellectual routing, Network coding Feedback based Network Dynamic Programming

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In the past, we did some research on the computational intelligence, visual information processing, analysis and understanding, and their applications on natural images, medical images, biometrics, security and communications. Also, we have developed some application system, especially software packages, such as PACS, medical image visualization system, 3G multimedia massaging service system and food safety detection system.
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Machine Learning: Algorithms and Theoretical Frameworks

As an active field of artificial intelligence (AI), machine learning is concerned with the task to make the computer obtain the intelligence of human beings by learning or computation, for examples, dimensionality reduction, feature extraction, classification, clustering, regression or fitting. According to the degree of dependence on the labeled samples, the machine learning is divided into three categories, supervised, unsupervised and semi-supervised learning. There are many interesting topics in this direction:

	Sample Selection and Feature Selection:Bootstarp, Bagging, Boosting-like algorithms, Relevance feedback
	Dimensionality Reduction:Linear or nonlinear methods, Kernel PCA, LAD and their variations, GP-LVM, etc
	Supervised Learning:Fuzzy NN, Kernel-based algorithms, Incremental Learning, etc
	Unsupervised Learning:Cluster tendency, Fuzzy cluster analysis and Cluster validity, etc
	Semi-supervised Learning:LNP-based algorithm, Pairwise constraint, Transfer learning, etc

Note:During the past ten years, Idevoted myself to the research of cluster analysis. Cluster analysis is one of multivariate statistical analysis methods, and a branch of unsupervised pattern recognition. Its goal is to partition an unlabeled sample set into clusters such that the homogenous samples are classified into the same cluster, and the inhomogeneous samples are classified into different clusters. As an effective analysis tool, cluster analysis has been widely used in image processing, computer vision, pattern recognition, fuzzy control and data mining.

Computational Intelligence: Algorithms and Theoretical Frameworks

Computational intelligence is an offshoot of artificial intelligence. As an alternative to GOFAI (good old-fashioned artificial intelligence) it rather relies on heuristic algorithms such as in fuzzy systems, neural networks and evolutionary computation. In addition, computational intelligence also embraces techniques that use Swarm intelligence, Fractals and Chaos Theory, Artificial immune systems, Wavelets, etc. Computational intelligence combines elements of learning, adaptation, evolution and Fuzzy logic (rough sets) to create programs that are, in some sense, intelligent. Computational intelligence research does not reject statistical methods, but often gives a complementary view (as is the case with fuzzy systems). Artificial neural networks is a branch of computational intelligence that is closely related to machine learning.

	Fuzzy System:Fuzzy Logic, Rough Sets, Vague Sets, Type-II Fuzzy, Granular computing, etc
	Neural Networks:BP-like, RBF, LVQ, ART, SVM, Ying-Yang machine, etc
	Natural-Inspired Algorithms:Genetic algorithm, Evolutionary programming, Artificial immune iystem, Clonal selection algorithm
	Swarm Intelligence:Particle swarm optimization, Ant colony Algorithm, and Particle filtering
	Sparsity Representation and Compressed Sensing: Multiscale geometric analysis, Dictionary learning,etc

Visual Information Processing, Analysis and Understanding

With the rapidly development of multimedia technology and internet, image and video becomes a popular and common media to transfer information. Hereby, image and video encoding (for storage or transport), processing (enhancement, restoration, and segmentation) and analysis (feature extraction based on color, region, edge, and texture) attract more and more attention of researches. Content-based information retrieval, digital watermarking, and image and video quality assessment become the most important research issues. Our research interests include:

	Encoding:JPEG2000 for image, H.264, MPEG-4, MPEG-7 for video, H.323/SIP video conference system
	Video Enhancement:Super-resolution reconstruction, De-interlacing, Frame rate up-conversion, Deblocking, In-painting, etc
	Digital Watermarking:Robust and invertible watermarking, Image and video fingerprint, steganography, etc
	Quality assessment for visual information:Perception-based assessment methodology, Metrics and Databases
	Segmentation:Spatial segmentation, Temporal segmentation for video, Spatio-temporal segmentation, /Level Sets
	Content-based Information Retrieval:Search engine, Content analysis, Automatic annotation, re-ranking, etc
	Situation Analysis:Semantic analysis for News or Soccer program, Aurora classification, Situation association
	Video surveillance:Face detection, modeling (AAM), tracking, face sketch-based retrieval and recognition
	Heterogeneous Image Synthesis and Recognition:photo-sketch-cartoon, Near infrared-visible light-thermal infrared
	Online Detection System:X-Ray detection for food security, Roller detection for paper-making, illegal information detection for MMS

Medical Image Processing and Analysis System

As an important application field of image engineering, medical image processing attracts much more of our research interest in recent years. With the theory and practice of image processing, we hope to speed up the progress of digital hospital and computer-aided diagnosis. This research direction mainly focuses on the development of medical image processing system (Software).

	Protocol Parsing:DICOM3.0, HL-7 and related protocol
	Scientific Visualization:3D Reconstruction (surface and volume rendering), display, measurement and analysis
	PACS:Picture archive and communication systems, Viewer, Interactive analysis
	CAD:Computer-aided detection and diagnosis (Mammography, Lung)
	IGS:Image-based guidance for surgery, Multi-modality fusion

Near Earth Space Information Grid

Near earth space information grid is a 3D Ad Hoc networks with multiple layers, which has the following features: (i) it can work for a relatively long time with prompt response; (ii) it is able to perform the surveillance and detection continuously; (iii) it can generate and change the network topology on demand. Hence, it would be widely applied to the circumstances like military detection, communication relay, anti-terrorism and disaster rescue. Based on the above characteristics, this project examines the 3D Ad hoc network design and cooperative transmissions. Through network optimization, and topology adjustment, we achieve the optimal network under service demand to transmit the information efficiently with high reliability at the expense of minimum cost. Specifically, we investigate the following three key issues:

	3D Ad Hoc Network Design on Demand:Network modeling, Multi-objective optimization for networks
	Cooperative Transmission:Intellectual routing, Network coding, Physical layer coding and cooperation
	Feedback based Network Dynamic Programming:Performance feedback, Topological adjustment

We explore the fundamental scientific issues from the practical applications, which are related to the network information theory. This study becomes challenging and insightful, when we consider it from a new perspective, new applications, and new circumstances. This project will produce the new theory, novel techniques, which lay out the foundations for the near earth space information grid networks.

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Last Modified: 2023-08-20