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::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 |
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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 the HIT Group's
Webpage |
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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 the IQA
Webpage |
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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 |
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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 |
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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.
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:
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Sample Selection and Feature Selection: Bootstarp,
Bagging, Boosting-like algorithms, Relevance feedback
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Dimensionality Reduction: Linear or nonlinear
methods, Kernel PCA, LAD and their variations, GP-LVM, etc
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Supervised Learning: Fuzzy NN, Kernel-based
algorithms, Incremental Learning, etc
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Unsupervised Learning: Cluster tendency, Fuzzy cluster
analysis and Cluster validity, etc
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Semi-supervised Learning: LNP-based algorithm,
Pairwise constraint, Transfer learning, etc
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Note: During the past ten years, I devoted
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.
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Fuzzy
System: Fuzzy Logic, Rough Sets, Vague Sets,
Type-II Fuzzy, Granular computing, etc
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Neural
Networks: BP-like, RBF, LVQ, ART, SVM, Ying-Yang
machine, etc
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Natural-Inspired Algorithms: Genetic algorithm,
Evolutionary programming, Artificial immune iystem, Clonal
selection algorithm |
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Swarm Intelligence: Particle swarm optimization,
Ant colony Algorithm, and Particle filtering
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Sparsity Representation and Compressed Sensing:
Multiscale geometric analysis, Dictionary learning, etc
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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:
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Encoding: JPEG2000 for image, H.264, MPEG-4, MPEG-7
for video, H.323/SIP video conference system
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Video Enhancement: Super-resolution reconstruction,
De-interlacing, Frame rate up-conversion, Deblocking,
In-painting, etc |
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Digital Watermarking: Robust and invertible
watermarking, Image and video fingerprint, steganography,
etc
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Quality assessment for visual information:
Perception-based assessment methodology, Metrics and
Databases |
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Segmentation: Spatial segmentation, Temporal segmentation for video, Spatio-temporal segmentation,
/Level Sets
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Content-based Information Retrieval: Search engine,
Content analysis, Automatic annotation, re-ranking, etc
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Situation Analysis: Semantic analysis for News or
Soccer program, Aurora classification, Situation
association |
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Video surveillance: Face detection, modeling (AAM), tracking,
face sketch-based retrieval and recognition
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Heterogeneous Image Synthesis and Recognition:
photo-sketch-cartoon, Near infrared-visible
light-thermal infrared |
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Online
Detection System: X-Ray detection for food security,
Roller detection for paper-making, illegal information
detection for MMS
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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).
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Protocol Parsing: DICOM3.0, HL-7 and related
protocol
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Scientific Visualization: 3D
Reconstruction (surface and volume rendering), display,
measurement and analysis
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PACS: Picture archive and communication systems,
Viewer, Interactive analysis
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CAD:
Computer-aided detection and diagnosis (Mammography, Lung)
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IGS: Image-based guidance for surgery,
Multi-modality fusion
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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:
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3D Ad Hoc Network Design on Demand: Network
modeling, Multi-objective optimization for networks
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Cooperative Transmission: Intellectual
routing, Network coding, Physical layer coding and
cooperation |
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Feedback based Network Dynamic Programming:
Performance feedback, Topological adjustment
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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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