Invited Speakers
Prof.
Wei Fang
Nanjing University of Information Science & Technology,
China
Wei Fang is currently a Professor of Computer Science at the
NanJing University of Information Science &Technology in
China. He was a Visiting scholar of University of Florida,
USA. Dr. Fang is a Senior Member of China Computer Society,
a member of IEEE, a member of ACM, a member of Jiangsu
Artificial Intelligence Society, and is also a Science and
Technology Consulting Expert of Jiangsu Province, a member
of Artificial Intelligence Professional Committee of
Shanghai Meteorological Society. He is the founding Director
of Institute of Artificial Intelligence and Meteorological
Big Data. He organized several conferences. He is a guest
editor and editorial board member for several SCI journals.
Speech Title: Research on the Application of Artificial
Intelligence in Meteorology
Abstract: Artificial intelligence (AI) has become widely
used in a variety of fields, including disease prediction,
environmental monitoring, and pollutant prediction. In
recent years, there has also been an increase in the volume
of research into the application of AI to Meteorology .This
speech aims to explore the latest trends in the application
of AI in Meteorology.
The Artificial Intelligence (AI) is playing a more and more
essential role in the industrial revolution and we are
seeking a lot of evolution in various machine learning
methodologies. Forecast of meteorological disasters is an
important and challenging worldwide problem. Various
techniques have been used to solve it, but the accuracy of
them is not high due to the highly nonlinear, random, and
complex nature of precipitation. In recent years, with the
rapid development of artificial intelligence technology, it
has gradually penetrated into all aspects of people's lives,
and the meteorological field are no exception. This speech
will share our experience and research results in all
aspects of the application of meteorology based on
artificial intelligence.
Prof. Zhenbo Li
China Agricultural University, China
Zhenbo Li, professor, doctoral supervisor, vice dean of
College of Information and Electrical Engineering, China
Agricultural University. His main research direction is
computer vision and smart agriculture. He has presided over
8 national research projects such as National Natural
Science Foundation of China and National Key Research and
Development Program, 8 provincial and ministerial level
research projects. He has published more than 90 high-level
papers in academic journals and top academic
conferences(T-IP, ICCV, ECCV etc.) , obtained more than 40
national invention patents, more than 60 software
copyrights, and won 4 provincial and ministerial science and
technology awards.
Speech Title: Data-driven Image Restoration and
Underwater Image Enhancement
Abstract: The data-driven artificial intelligence model
has become a new research paradigm. This presentation
presents a summary of the latest research on the image
restoration (IR) and underwater image enhancement (UIE)
tasks. I will introduce some of our related research. Most
existing image-restoration models employed static CNN-based
models, where the fixed learned filters cannot fit the
diverse degradation well. To address this, we propose a
novel dynamic image restoration contrastive network
(DRCNet). Image restoration aims to recover images from
spatially-varying degradation. In contrast, UIE aims to
reconstruct clear images from the diverse degradation images
caused by light scattering and attenuation in underwater
scenarios. However, existing UIE methods always face the
dilemma of the trade-off between performance and efficiency.
Therefore, we design a novel invertible UIE network, InvUIE,
that can optimally balance these competing goals. Moreover,
due to the degraded underwater images have hampered the
existing computer vision applications. We designed a novel
tied bilateral learning network is proposed for UIE, dubbed
TBAIE, which improves the degraded underwater images to meet
the requirements of various computer vision applications,
such as aquaculture etc..
Prof. Xin Xu
Wuhan University of Science and Technology, China
Xin Xu received the B.S. and Ph.D. degrees in computer
science and engineering from Shanghai Jiao Tong University,
Shanghai, China, in 2004 and 2012 respectively. He is
currently a Full Professor with the School of Computer
Science and Technology, Wuhan University of Science and
Technology, Wuhan, China. His research deals with image
processing, computer vision, and deep learning. More
specifically, his research areas focus on building a
hierarchical person re-identification architecture including
detection and recognition for nighttime surveillance
scenarios. His publication was selected as the cover paper
of the journal International Journal of Intelligent Systems
in 2022. He was shortlist in the Best Paper Finalist of the
IEEE International Conference on Multimedia and Expo (ICME)
2021.
Speech Title: Non-Convex Machine Iearning based
Large-Scale Interactive Image Annotation
Abstract: The rapid development of artificial
intelligence in recent years can be seen as a triumph of
data intelligence, where machine learning plays a critical
role. Most of current machine learning techniques are
typically based on solving convex optimization problems.
Recently, many real-world applications that require solving
non-convex optimization problems are continuously emerging,
which can be much more challenging. Typical applications
include interactive data annotation, deep network
compression, and etc. Traditionally, researchers have
attempted to approximate non-convex optimization problems as
convex problems in order to apply standard optimization
techniques. However, these approximations can be quite crude
and may not capture the full complexity of the original
problem. To effectively promote the potential of machine
learning to solve practical application, it is urgent to
directly handle non-convex problems from the source.
Prof.
Zhenlong Du
Nanjing Tech University,
China
Zhenlong Du is currently a Professor of Computer Science at
NanJing Tech University in China. He was a Visiting scholar
of SUNNY at Buffalo, USA. Dr. Du is a Senior Member of China
Computer Society, a member of IEEE, a member of ACM, and a
member of Jiangsu Computer Society. Dr. Du is honored the
3rd prize of Jiangsu Science and Technology Award in 2020,
he is honored the Jiangsu Six Talent Peaks. Recently, Dr. Du
has hosted National Natural Science Foundation of China,
Higher Education Doctoral Program of the Ministry of
Education, etc. Meanwhile, Dr. Du is hosting several
industrial research projects.
Speech Title: Fusion of Edge and Cloud in Manufacture
Updating and Digitalized Promotion
Abstract: The report presents the fusion practice
between AI model and analytic modeling in manufacture
updating and digitalized promotion. It analyzes the
coordination of edge along with cloud based on their
respective merits. The presentation gives the combination
method of edge computing and cloud storage, which could
quickly deploy AI models in industrial scenarios, acquire
the industrial process data via edge sensors, handle the
complicated industrial issues with AI model and analytic
modeling. The presentation gives the model transformation,
pipeline organization, edge invocation, and demonstrates the
application by UAC, fabricated decoration, and multi-channel
HCI.
Assoc.
Prof. Guangyu Gao
Beijing Institute of Technology, China
Guangyu Gao is an Associate Professor at the School of
Computer Science and Technology, and served as the Deputy
Director of the Institute of Data Science and Knowledge
Engineering, at the Beijing Institute of Technology. He is
also the secretary of the Secretariat of the China
Engineering Education Accreditation Association (CEEAA), and
the former Assistant Director of the Deputy Department of
the Economic and Information Commission of Shunyi District,
Beijing. He received his Ph.D. from the Beijing University
of Posts and Telecommunications (BUPT) in 2013, and spent
about one year at the National University of Singapore as a
Joint-Ph.D. student from 2012 to 2013. He was awarded the
IBM Visiting Scholarship in 2016, and the IBM Faculty Awards
in 2016, 2017, and 2019.
His current research interests include Computer Vision, Deep
Learning, and Multimedia Analysis. In recent years, he
published more than 30 high-level academic papers (such as
TPAMI, TCSVT, TMM, NeurIPS, CVPR, ACMMM, etc.) as well as a
series of national invention patents. As the Principal
Investigator, he is also undertaking 3 National Natural
Science Funds, 4 University-Industry Collaborative Education
Programs, and a number of School-Enterprise cooperation
projects (with IBM, Taobao, etc.). He has successively won
the Honorable Mention Award (second place) of the VisDrone
Competition in IEEE ICCV-2019 (CCF-A), and the Nominate
Video Award of the IEEE UIC-2015 Video Competition.
Speech Title: Addressing Few-shot Semantic Segmentation
with Consideration of Feature Diversity
Abstract: Fully-supervised semantic segmentation
requires pixel-level annotations, which are costly and
time-consuming, and also cannot well generalize to unknown
categories. Thus, the Few-Shot Semantic Segmentation has
been proposed to simulate human learning by inferring the
semantic segmentation of a query image from a few annotated
sample images of known classes. However, the most advanced
few-shot semantic segmentation methods still have limited
performance on public datasets due to typical problems such
as ignoring the diversity of category features and instance
features. To overcome these problems, we focus on the
perspectives of category feature diversity and instance
feature diversity, and propose the Hierarchical
Context-agnostic Network and a Double Recalibration Network
for few-shot semantic segmentation. On the one hand, the
hierarchical context-agnostic network generates multi-scale
feature representations for objects of different granularity
in the annotated support images to provide diverse category
guidance features. The background exclusion support module
utilizes the context information in the background pixels to
improve the model's discrimination ability for target
objects and background regions. On the other hand, the
double recalibration network considers the diversity of
instance features by proposing a self-adaptive
self-correction module and a cross-recalibration module for
efficient fusion of prior information in the annotated
support images to enrich the semantic features of query
images. The two modules complement each other to form the
double recalibration network. Finally, the proposed methods
were evaluated on two public datasets, Pascal-5i and
COCO-20i, and the results show significant performance
improvements in few-shot semantic segmentation under diverse
category and instance features, outperforming existing
state-of-the-art methods.
Assoc. Prof. Dong Liang
Nanjing University of Aeronautics and Astronautics, China
Prof. Dong Liang received the B.S. degree in
Telecommunication Engineering and the M.S. degree in
Circuits and Systems from Lanzhou University, China, in 2008
and 2011, respectively. In 2015, he received Ph.D. at the
research direction of System Information Science, the
Graduate School of Information Science and Technology ,
Hokkaido University, Japan. He is currently an associate
professor with the College of Computer Science and
Technology, Nanjing University of Aeronautics and
Astronautics. His research interests include model
communication in pattern recognition and intelligent
systems, optomechatronic and imaging systems. He was awarded
the Best Student Paper Award from International Symposium on
Optomechatronic Technology (ISOT) in 2013. He was awarded
the Excellence Research Award from Hokkaido University in
2013. He has published several research papers including in
Pattern Recognition, IEEE TIP/TNNLS/TGRS/TCSVT, and AAAI. He
is an Organizing Committee member of the 2021 China
Symposium on Machine Learning and Applications (MLA’21). He
is a Program Committee member of the China Conference of
Biometrics 2015-2022 (CCBR’15-22). He serves as a guest
editor of MDPI Sensors. He has served as a reviewer for
numerous international academic journals and conferences
such as IEEE TIP/TNNLS/TGRS/TCSVT/TITS/JSTARS, and
AAAI/IJCAI/ICML/ICPR/ICIP/ICASSP.
Speech Title: ALL-E: Aesthetics-guided Low-light Image
Enhancement
Abstract: Evaluating the performance of low-light image
enhancement (LLE) is highly subjective, thus making
integrating human preferences into image enhancement a
necessity. Existing methods fail to consider this and
present a series of potentially valid heuristic criteria for
training enhancement models. In this talk, we introduce a
new paradigm,i.e., aesthetics-guided low-light image
enhancement (ALL-E), which introduces aesthetic preferences
to LLE and motivates training in a reinforcement learning
framework with an aesthetic reward. Each pixel, functioning
as an agent, refines itself by recursive actions, i.e., its
corresponding adjustment curve is estimated sequentially.
Extensive experiments show that integrating aesthetic
assessment improves both subjective experience and objective
evaluation. Our results on various benchmarks demonstrate
the superiority of ALL-E over state-of-the-art methods.
Assoc.
Prof. Bo Li
Nanchang Hangkong University, China
Bo Li received the Ph.D. degree in 2008 in computational
mathematics, Dalian University of Technology (DUT), Dalian,
China. Now he is the professor in the School of Mathematics
and Information Science of Nanchang Hangkong University. His
current research interests include the areas of image
processing and computer graphics. He has published more than
40 research papers in international journals (including IEEE
TIP, IEEE TVCG, IEEE TMM) and conferences.
Speech Title: BBDM: Image-to-Image Translation with
Brownian Bridge Diffusion Models
Abstract: Image-to-image translation is an important and
challenging problem in computer vision and image processing.
In this talk, I will introduce a novel image-to-image
translation method based on the Brownian Bridge Diffusion
Model~(BBDM), which models image-to-image translation as a
stochastic Brownian Bridge process, and learns the
translation between two domains directly through the
bidirectional diffusion process rather than a conditional
generation process. To the best of our knowledge, it is the
first work that proposes Brownian Bridge diffusion process
for image-to-image translation. Experimental results on
various benchmarks demonstrate that the proposed BBDM model
achieves competitive performance through both visual
inspection and measurable metrics.
Assoc. Prof. Hong Wu
University of Electronic Science and Technology of China, China
Dr. Hong Wu is an Associate Professor of Computer Science at
University of Electronic Science and Technology of China
(UESTC). He received his Ph.D. in pattern recognition and
intelligent system from the National Laboratory of Pattern
Recognition (NLPR) at the Institute of Automation (IA),
Chinese Academy of Sciences (CAS), in 2004, and got his B.S.
in Computer Science from the University of Science and
Technology of China (USTC) in 1993. In May 2004, he joined
NEC Labs China as an associate researcher undertaking
research on web mining. In May 2006, he joined the
University of Electronic Science and Technology of China
(UESTC) as an associate professor. His current research
interests are computer vision, pattern recognition and data
mining.
Speech Title: Feature Aggregation and Refinement Network
for 2D Anatomical Landmark Detection
Abstract: Localization of anatomical landmarks is
essential for clinical diagnosis, treatment planning, and
research. In this paper, we propose a novel deep network,
named feature aggregation and refinement network (FARNet),
for the automatic detection of anatomical landmarks. To
alleviate the problem of limited training data in the
medical domain, our network adopts a deep network
pre-trained on natural images as the backbone network and
several popular networks have been compared. Our FARNet also
includes a multiscale feature aggregation module for
multi-scale feature fusion and a feature refinement module
for highresolution heatmap regression. Coarse-to-fine
supervisions are applied to the two modules to facilitate
the end-to-end training. We further propose a novel loss
function named Exponential Weighted Center loss for accurate
heatmap regression, which focuses on the losses from the
pixels near landmarks and suppresses the ones from far away.
Our network has been evaluated on three publicly available
anatomical landmark detection datasets, including
cephalometric radiographs, hand radiographs, and spine
radiographs, and achieves state-of-art performances on all
three datasets.
Assoc.
Prof. Xiaoli Zhang
Jilin University, China
Xiaoli Zhang is an associate professor in the Jilin
University, China, where he is leading the research in the
areas of multimodal image processing and deep learning in
computer vision applications. He has published more than 60
research papers in international journals (including IEEE
TIP, Bioinformatics, information sciences) and conferences.
He is a senior member of CCF, member of CSIG and IEEE.
Speech Title: When Image Fusion Meets Deep Learning
Abstract: Image fusion is an essential technique in
computer vision that aims to combine information from
multiple images to generate a single enhanced image with
richer and more comprehensive details. Over the years,
traditional image fusion methods have made significant
contributions to various applications, such as medical
imaging, remote sensing, and surveillance. However, with the
advent of deep learning, the field of image fusion has
witnessed a paradigm shift. This lecture explores the
intersection of image fusion and deep learning, examining
how deep learning techniques have revolutionized the field
by enabling more accurate and robust fusion results. We
delve into the fundamental concepts and challenges of both
image fusion and deep learning, highlighting their
individual strengths and limitations. Furthermore, we
explore various deep learning architectures specifically
designed for image fusion. We discuss how these
architectures can effectively extract and fuse information
from multiple input images, resulting in superior fusion
outcomes.
Assoc. Prof. Jin Wang
Beijing University of Technology, China
Jin Wang is an Associate Professor with the Beijing Key
Laboratory of Multimedia and Intelligent Software
Technology, Faculty of Information Technology, Beijing
University of Technology. He is also a researcher from
Beijing Institute of Artificial Intelligence. He is an
IEEE/ACM/CCF Member. His research areas include digital
image processing, image/video coding, computer vision, and
artificial intelligence. He authored more than 30
publications in journals and conferences, such as IEEE TIP,
IEEE TMM, IEEE TCSVT, ACM MM, IEEE INFOCOM, IEEE DCC, and
IEEE ICME. He holds more than 6 national invention patents.
He has hosted more than 7 scientific research projects
supported by National Natural Science Foundation and Beijing
Municipal Natural Science Foundation, etc. He serves as a
session chair of ACM MM 2021, a Technical Program Committee
(TPC) member for ACM MM 2022, and a Program Committee (PC)
member for AAAI 2022.
Speech Title: Multi-Scale Feedback Reconstruction for
Guided Depth Map Super-Resolution
Abstract: Guided depth map super-resolution (GDSR) is
one of the mainstream methods in depth map super-resolution,
as high-resolution color images can guide the reconstruction
of the depth maps and are often easy to obtain. However, how
to make full use of extracted guidance information of the
color image to improve the depth map reconstruction remains
a challenging problem. In this work, we first design a
multi-scale feedback module (MF) that extracts multi-scale
features and alleviates the information loss in network
propagation. We further propose a novel multi-scale feedback
network (MSF-Net) for guided depth map super-resolution,
which can better extract and refine the features by
sequentially joining MF blocks. Specifically, our MF block
uses parallel sampling layers and feedback links between
multiple time steps to better learn information at different
scales. Moreover, an inter-scale attention module (IA) is
proposed to adaptively select and fuse important features at
different scales. Meanwhile, depth features and
corresponding color features are interacted using
cross-domain attention conciliation module (CAC) after each
MF block. We evaluate the performance of our proposed method
on both synthetic and real captured datasets. Extensive
experimental results validate that the proposed method
achieves state-of-the-art performance in both objective and
subjective quality.
Assoc. Prof. András Horváth
Peter Pazmany Catholic Universtiy, Hungary
András Horváth is an Associate Professor at the Faculty of
Information Technology and Bionics at Peter Pazmany Catholic
University. His research is mostly focused on computer
vision and artificial intelligence, especially on the
efficient implementation of modern machine learning
algorithms with emerging devices. He took part in the
DARPA-UPSIDE (Unconventional Processing of Signals for
Intelligent Data Exploitation) project between 2012 and 2018
in a consortium with Intel, MIT, which aimed the development
of an object recognition pipeline with oscillatory based
computing, implemented on emerging devices (e.g.:
spin-torque and resonant body oscillators) and was involved
in multiple international research grants sponsored by the
European Union and ONR.
He is author or co-author of more than 50 publications which
appeared in various international journals. He is an active
Reviewer for various peer reviewed journal papers. (e.g.:
IEEE Transaction on Signal Processing , IEEE Transactions on
Circuits and System, etc.). He is a member of the IEEE
Circuits and System and the IEEE Computational Intelligence
Societies and the secretary of the Cellular Nanoscale
Networks and Array Computing Technical Committee.
Speech Title: Preventing Adversarial Attacks in Deep
Neural Networks
Abstract: With the application of deep neural networks
becoming mainstream in our everyday lives, questions about
robustness and reliability of these networks are also
becoming ever more important. Adversarial attacks reveal an
important weakness of neural networks. Small perturbations
of the input image easily cause misclassifications without
changing the perception of a human observer. Among various
attack methods, sticker-based attacks are the most
threatening because they are robust enough for use in real
world applications. These modifications cause an abrupt
change on a small area of the input image and can invoke
arbitrarily large activations in the network. In this talk I
will introduce methods to detect and prevent adversarial
attacks and yield safer applications in practice.
Assoc. Prof. Ruiheng Zhang
Beijing Institute of Technology, China
Ruiheng Zhang received the B.E. degree in 2014 from Beijing
Institute of Technology, China, and the Dual-Ph.D. degree
from University of Technology Sydney, Australia and Beijing
Institute of Technology, China. He is currently an Associate
Professor in Beijing Institute of Technology. He is the
author of more than 30 research papers and one book,
including Remote Sensing of Environment, IEEE TMM, ISPRS,
Pattern Recognition, ICLR, IJCAI and so on. He is involved
as a member of the Editorial Board of Frontiers in Robotics
and AI, Artificial Intelligence and Applications. He has
served as Co-Chair of IMASBD 2022, TPC of ICDIP 2022, VSIP
2022 and ICIVIS 2022. He is also a Reviewer for the ICLR,
ACM MM, IJCAI, DICTA, IEEE TMM, IEEE TGRS, IEEE JSTARS,
Pattern Recongition, Neurocomputing, Remote Sensing, Applied
Science, Sensors, Electronics. His current research
interests include deep learning, object understanding, and
multi-modal remote sensing.
Speech Title: Infrared Target Detection with Limited Data
Abstract: Target detection technology in infrared images
under complex background has been widely used in many
military and civil fields. In recent years, with its unique
advantages in improving the efficiency of feature extraction
and mining the hidden representation of data, deep learning
has surpassed the traditional pattern recognition methods,
providing a reliable way to achieve automatic target
detection. However, the data requirement of deep learning
restricts its performance on infrared target detection,
where collecting a large number of infrared image datasets
is time-cosuming and cost. How do we solve the contradiction
between the demand of deep learning for large amounts of
data and the lack of infrared image data? In addition,
infrared images have the characteristics of low resolution,
low contrast, low signal-to-noise ratio, fuzzy visual effect
and lack of rich color information, which bring great
challenges to the target detection task.
Assoc. Prof. Jingjing Zhang
China University of Geosciences (Wuhan), China
Jingjing Zhang received his B.S. degree in 2007 from China
University of Geosciences, Wuhan, China. He obtained his
Ph.D. degree in 2014 from the University of Chinese Academy
of Sciences, China. From 2014 to now, he worked in the
School of Automation, China University of Geosciences,
Wuhan, China. He worked as a postdoctoral in the School of
Energy and Power Engineering, Huazhong University of Science
and Technology, from 2015 to 2017. He also worked as a
visiting scholar at KU Leuven, Belgium, from 2017 to 2018.
His research interests include augmented reality,
computational photography, and infrared small target
tracking.
Speech Title: Adaptive ROI Selection for Imaging
Photoplethysmography
Abstract: Region of interest (ROI) selection is a key
process for remote heart rate measurement approach. Previous
studies usually use a time window to make a goodness metric
for ROI selection, which is computationally intensive and
hard to apply for head rotation conditions. We proposed
adaptive ROI selection methods to solve the problems.
Results show that our proposed methods outperforms other
methods.
Assoc. Prof. Yifan Zuo
Jiangxi University of Finance and Economics, China
Yifan Zuo, received the Ph.D. degree from the University of
Technology Sydney, Ultimo, NSW, Australia, in 2018. He is
currently an Associate Professor with the School of
Information Management, Jiangxi University of Finance and
Economics. His research interests include Image/Point Cloud
Processing. The corresponding papers have been published in
major international journals such as IEEE Transactions on
Image Processing, IEEE Transactions on Circuits and Systems
for Video Technology, IEEE Transactions on Multimedia.
Speech Title: Point Cloud Processing with Multimodality
Guidance
Abstract: Recently, there has been growing interest in
point cloud processing with multimodality guidance. In this
report, the speaker will introduce the principles and the
advantages of multimodality guidance in point cloud
processing and present the speaker's latest research
achievements: (1) a multi-modal feature fusion method that
enhances the distinctiveness of features for point cloud
registration by leveraging texture information from images
as guidance; (2) a method that combines pre-trained CLIP
models with downstream tasks in point clouds. It effectively
correlates features of point clouds and images in
high-dimensional space, and improves the performance for
point cloud downstream tasks.
Assoc. Prof. Ying Li
Nanjing Normal University, China
Ying Li is currently an Associate Professor in the School of
Computer and Electronic Information / School of Artificial
Intelligence, Nanjing Normal University (NNU). She received
her Ph.D. degree from Dalian University of Technology (DUT)
in 2019, advised by Prof. Xiangwei Kong. She was a visiting
scholar at University of Texas at San Antonio, supervised by
Prof. Qi Tian from Dec 2015 to Dec 2017. She worked as an
intern in the Computer Vision group at the Noah’s Ark Lab of
Huawei from Aug 2018 to May 2019. Her research interests are
computer vision and pattern recognition. She is an Associate
Editor (AE) of Pattern Recognition.
Speech Title: Challenges and New Trends for Fine-grained
Image Retrieval
Abstract: Fine-grained image retrieval is the task of
retrieving images that belong to a specific subcategory
within a larger category. This is a challenging task, as it
requires the retrieval system to distinguish between subtle
differences in visual features. This talk will discuss the
challenges and new trends for fine-grained image retrieval,
and introduce our recent works in related fields, including
semantic retrieval, large-scale retrieval, multi-label
pattern retrieval, multi-modal image-text retrieval,
cross-domain person ReID and fine-grained action
recognition.
Assoc. Prof. Dakshina Ranjan Kisku
National Institute of Technology Durgapur, India
Dakshina Ranjan Kisku is currently an Associate Professor in
the Department of Computer Science and Engineering at
National Institute of Technology Durgapur, India. He
received his BCSE, MCSE and Ph.D. (Engineering) degrees in
Computer Science and Engineering from Jadavpur University,
India. Dr. Kisku is an active researcher in the area of
biometrics, image processing, computer vision and machine
learning. He was a Researcher in the Computer Vision Lab at
the University of Sassari, Italy from March 2006 to March
2007. Prior to that, he was a Project Associate in
Biometrics and Computer Vision Lab at Indian Institute of
Technology Kanpur, India. He was a Postdoctoral Scientist in
the Signal Processing Laboratory at Bahcesehir University,
Turkey in 2012. Prior to joining NIT Durgapur, he was
working as a faculty member for several years at Asansol
Engineering College, India and Dr. B. C. Roy Engineering
College, India. Dr. Kisku has more than 120 scientific
publications to his credit published in refereed
conferences, journals and edited books. He has co-authored
and edited three books on biometrics and homeland security,
and one book on medical biometrics. He has filed two patents
in the area of Healthcare Informatics for publication. He is
a recipient of IEI Young Engineers Award, IEI Excellence
Award, Outstanding Scientist Award, Outstanding Reviewers
Awards, IEEE Travel Award, IAPR Endorsement Scholarship,
MIUR Research Fellowship, TUBITAK PDF Fellowship,
Visvesvaraya Fellowship and many other accolades. Dr. Kisku
is a Fellow of RSPH, UK and Senior Member of IEEE (USA). He
regularly serves as a member of technical committee for many
conferences and also reviewer for many refereed journals,
conferences and books.
Assoc. Prof. Chunzhi Li
Huzhou University, China
Chunzhi Li received the M.Sc. and Ph.D. degrees respectively
in the Department of Computer Science from Wuyi University,
Jiangmen, China and in the Department of Computer Science
and Technology, East China Normal University, Shanghai,
China, in 2007 and 2014. She is currently an associate
professor with the Department of School of Information
Engineering, Huzhou University, Huzhou City, Zhejiang
Province, China, also a visiting scholar with the College of
computer science and technology, Zhejiang University,
Zhejiang Province, China. She has been a visiting associate
professor in 2019-2020 with the Department of Electrical and
Electronic Engineering, the University of Hong Kong, Hong
Kong. Her main research area is Remote Sensing Image
Processing and has published more than 20 papers on “remote
sensing” in the excellent journals, such as IEEE
TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING.
Speech Title: Exploring Oblique Rotation Factor to
Restructure Deep Hyperspectral Image Classification
Abstract: Factor analysis (FA) is commonly used in
fields such as economics and now being introduced as a new
tool on dimensionality reduction (DR) for hyperspectral
image classification (HSIC), but FA usually employed
orthogonal rotation to directly maximize the separation
among factors, which would oversimplify the relationships
between variables and factors, worse still, the orthogonal
rotation often distorts the true relationships between
underlying traits in real life and can not always accurately
represent these relationships. To this end, this letter
proposes a DR algorithm about FA based on oblique rotation
Oblimax to improve HSIC. Firstly, the common factors are
extracted from the hyperspectral data to form a factor
loading matrix which will be obliquely rotated, then its
factor score is estimated to obtain the eigen dimensions for
the hyperspectral data, thus realizing DR. On the basis of
the successful DR, a deep classifier is constructed,
specially, a double-branch structure about 3
dimensional-convolutional neural networks (3D-CNN) with
different sizes is restructured to extract multi-scale
spatialspectral features, and early fusion is performed on
the features, then 2 dimensional-convolutional neural
networks (2D-CNN) is restructured to reduce the
computational complexity and learns more spatial features.
Finally, the accuracy of the proposed algorithm on the
datasets Indian Pines, Kennedy Space Center and Muufl
Gulfport, respectively achieves 99.78%, 99.95% and 95.57%.
It shows that the proposed algorithm in this letter has
advantages in improving the classification accuracy and
reducing the complexity of computation.
Assoc. Prof. Yukinobu Miyamoto
Kobe Gakuin University, Japan
MIYAMOTO Yukinobu received the B.E., M.E. and Ph.D. degrees
in computer science from Kobe University. He had studied
artificial intelligence, especially machine learning,
pattern recognition and evolutionary computation. He is
currently an Associate Professor at Faculty of Business
Administration, Kobe Gakuin University. He is concurrently a
Professor at Division of Information Systems, Graduate
School of Information Technology, Kobe Institute of
Computing. He is also a Professional Engineer in Information
Engineering (P.E.), and a Certified Information Technology
Professional (CITP).
Speech Title: An Approach for Rotation and Scale Robust
Texture Recognition Using Wavelet Transform and Moment
Features
Abstract: Texture is one of the key characteristics used
to identify objects or regions of interest in an image. One
of the difficulties in texture analysis has been the lack of
appropriate tools for characterization. Previous texture
analysis methods have been based on modified wavelet
transforms, called tree-structured wavelet transforms, and
what are classified as neural networks. However, it is
difficult to classify textures of different scales and
rotations using only these methods. We propose texture
recognition based on moment features. We compute the
gray-level density function of each image and compare its
histogram, which represents its shading. Histogram
comparison is a commonly used method for indexing images and
expressing the histogram in terms of its moments reduces the
complexity of the method. For the computation of moment
features, the wavelet transform corresponding to each node
is used. For classification, each moment feature is fed to a
neural network. This method allows the recognition of
textures of different scales and rotations and improves the
recognition rate.
Assoc.
Prof. Indrarini Dyah Irawati
Telkom University, Indonesia
Indrarini Dyah Irawati, obtained B.E and M.E degree in
Electrical Engineering at Telkom University, Bandung,
Indonesia and doctoral degree in the School of Electrical
and Information Engineering, Institute of Technology
Bandung. She joined School of Applied Science, Telkom
University as an Instructor (2007-2013), Assistant Professor
(2014-2018), Associate Professor (2019-present).
Her main research interests are in the areas of compressive
sensing, watermarking, signal processing, computer network,
and IoT.
She is currently a member of the Association for Computing
Machinery (ACM), the International Association of Engineers
(IAENG), and IEEE. She has published more than 25 research
papers-mostly the top ranked journal Scopus Indexed by IEEE,
Springer, Elsevier, and 20 Conference papers-mostly the top
ranked international conference, 3 books, 1 book chapter, 15
Intellectual property rights, and 1 patent.
Speech Title: Review on Enhanced Diagonal Minimization
(EDMIN) for Sparse Two-Dimensional Data
Abstract: Traffic matrix (TM) is important information
that is used by researchers to develop research in the
context of network monitoring, network capacity planning,
and network maintenance. TM is obtained from measurements
directly on the router in the network. Compressive Sensing
(CS) provides an alternative solution to overcome problems
on the internet network as stated earlier, reducing the
number of traffic measurement samples on the network by
compression. A novel Enhanced Diagonal Minimization (EDMIN)
algorithm used for reconstructing two-dimensional data based
on compressive sensing (CS). EDMIN finds solutions with a
global approach to obtain SV diagonal matrix so that it is
superior to other conventional algorithms.