Disparate Scene Registration – VISION AND IMAGE PROCESSING (VIP) RESEARCH GROUP

Linlin Xu

Linlin Xu — Wed, 29 Mar 2023 19:13:55 +0000

I am a Research Assistant Professor in the Department of Systems Design Engineering, working on AI, machine learning, Earth Observation and Environmental Remote Sensing projects.

In my key research areas, I have demonstrated excellence, leadership and recognition. I, as principal investigator, have successfully secured many competitive grants, including NSERC Discovery Grant. I have published one book chapter, 66 journal papers on high-rank remote sensing journals and 41 conference articles on high-impact conferences.

I have been involved in many invited talks and presentations to different audiences, and I frequently receive reviewer invitations from high-impact remote sensing journals and conferences. I serve as the Associate Editor of Frontiers in Environmental Science (Section: Big Data, AI and the Environment).

LinkedIn: linkedin.com/linlinxu.

Google Scholar: https://scholar.google.com/linlinxu

Email: l44xu@uwaterloo.ca

Research interests

With the explosion of Earth observation (EO) data, fast EO market growth is bottlenecked by the lack of intelligent analytics that can automatically transfer large-volume, ever-increasing, noisy, heterogeneous raw EO data into compact, real-time, value-added information products. I tailor-design cutting-edge AI solutions to enable automatic generation of scalable EO information products in a high-precision, fast and cost-effective manner to better support various key applications in environmental monitoring, resource exploration and climate change studies. I have strong expertise on AI and machine learning (ML), geospatial data science, different remote sensing techniques (i.e., Hyperspectral, Lidar and SAR remote sensing), and their environmental monitoring applications, e.g., land cover/use classification, hyperspectral mapping of biophysical, biochemical, and geochemical parameters, oil spill detection, and high-resolution Pan-Arctic sea/lake ice mapping.

Students

Supervision – Current

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Ph.D

M.A.Sc

Supervision – Completed

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Ph.D

M.A.Sc

Research topics

Computer Vision
Image Segmentation/Classification
Remote Sensing
Stochastic Models

Research demos

Compressed Sensing
Disparate Scene Registration
Grid Seams: A fast superpixel algorithm for real-time applications
MAGIC System
SAR Sea Ice Image Synthesis
Satellite SAR Sea Ice Classification
Texture Classification

Publications

Journal Articles / Conference Papers

Journal Articles

Conference Papers

Top

Disparate Scene Registration

vipadmin — Mon, 20 Mar 2023 20:37:29 +0000

A problem of great interest in the field of visual pattern recognition is image registration, where images of the same scene captured under different conditions are aligned with each other. Image registration is important to a wide range of applications, such as environment change analysis, superresolution, biomedical image analysis, and computer-assisted surgery. Much of recent research in image registration has focused on multimodal image registration, where images captured using different imaging modalities are registered together.

Multimodal image registration is a very challenging problem for many reasons. Images captured using different imaging modalities are represented using different intensity mappings, making it difficult to compare images in a direct manner based on intensity values. This situation is further complicated by the presence of signal non-homogeneities, noise, and geometric distortions. Furthermore, there are many cases where it is necessary to register multiple images from different imaging modalities to obtain a more complete picture of the scene (e.g., T1/T2/CT registration, LANDSAT 4/5/7 inter-band registration). Hence, it is important to maintain internal consistency between all the images being registered, which is a difficult task to accomplish in the case of multimodal registration given the different information being captured. Therefore, an automatic method that can address all of the above issues is highly desired.

In the VIP lab, we investigate simultaneous, invariant strategies for tackling the problem of registering multiple disparate images acquired using different imaging modalities. In particular, we explore the concept of joint complex phase order distributions, which are well-constrained and can be efficiently extensible to high-dimensional problems. An illustration of this approach is shown below (Fig 1.). It can be observed that as the images come into alignment, the samples in the joint complex phase order distribution becomes tightly packed around a n-dimensional hyperline.

Figure 1

Examples of registration results using this approach are shown below (Fig 2.) for both medical and remote sensing imagery.

Figure 2

Related people

Directors

Students

Alumni

Related research areas

Computer Vision

Image Segmentation/Classification

Alexander Wong

Alexander Wong — Thu, 23 Feb 2023 21:38:59 +0000

Alexander Wong is currently the Canada Research Chair in Medical Imaging Systems and an assistant professor in the Department of Systems Design Engineering at the University of Waterloo. He had previously received his B.A.Sc. degree in Computer Engineering from the University of Waterloo, Waterloo, ON, Canada in 2005, his M.A.Sc. degree in Electrical and Computer Engineering from the University of Waterloo, Waterloo, ON, Canada in 2007, and his Ph.D. degree Systems Design Engineering from the University of Waterloo, ON, Canada in 2010. He was also a NSERC postdoctoral research fellow at Sunnybrook Health Sciences Centre. He has published over 450 refereed journal and conference papers, as well as patents, in various fields such as computational imaging, artificial intelligence, computer vision, and multimedia systems. He has received numerous awards including three Outstanding Performance Awards, a Distinguished Performance Award, an Engineering Research Excellence Award, a Sandford Fleming Teaching Excellence Award, an Early Researcher Award from the Ministry of Economic Development and Innovation, a Best Paper Award at the NIPS Workshop on NIPS Workshop on Transparent and Interpretable Machine Learning (2017), a Best Paper Award at the NIPS Workshop on Efficient Methods for Deep Neural Networks (2016), two Best Paper Awards by the Canadian Image Processing and Pattern Recognition Society (CIPPRS) (2009 and 2014), a Distinguished Paper Award by the Society of Information Display (2015), two Best Paper Awards for the Conference of Computer Vision and Imaging Systems (CVIS) (2015,2017), Synaptive Best Medical Imaging Paper Award (2016), two Magna Cum Laude Awards and one Cum Laude Award from the Annual Meeting of the Imaging Network of Ontario, CIX TOP 20 (2017), AquaHacking Challenge First Prize (2017), Best Student Paper at Ottawa Hockey Analytics Conference (2017), and the Alumni Gold Medal.

Students

Supervision – Current

PDF

ph.D.

M.A.Sc.

Supervision – Completed

PDF

ph.D.

M.A.Sc.

Research topics

Biomedical Imaging
Computer Vision
Discovery Radiomics
Evolutionary Deep Intelligence
Image Segmentation/Classification
Multiresolution Techniques
Remote Sensing
Scientific Imaging
Stochastic Models
Video Analysis

Research demos

Action Recognition in Video
Bias Field Correction in Endorectal Diffusion Imaging
Coded Hemodynamic Imaging
Correlated Diffusion Imaging
Disparate Scene Registration
Enhanced Decoupled Active Contour Using Structural and Textural Variation Energy Functionals
Enhanced Low-dose Computed Tomography
Grid Seams: A fast superpixel algorithm for real-time applications
Hybrid Structural and Texture Distinctiveness Vector Field Convolution for Region Segmentation
Image Denoising
Multiplexed Optical High-coherence Interferometry
SAR Sea Ice Image Synthesis
Skin Cancer Detection
Statistical Textural Distinctiveness for Salient Region Detection in Natural Images
Stereo Vision for Dimension Estimation

Publications

Journal Articles / Conference Papers

Journal Articles

Conference Papers

Top

David Clausi

David A Clausi — Mon, 13 Feb 2023 20:18:05 +0000

I completed my Ph.D. in Systems Design Engineering in 1996 and immediately afterwards, I worked in software medical imaging field at Agfa (nee Mitra) in Waterloo, Ontario. I started my academic career in 1997 as an Assistant Professor in Geomatics Engineering at the University of Calgary, Canada. In 1999, I returned to my alma mater and I am now a Professor specializing in the fields of Intelligent and Environmental Systems. In addition, I was the Associate Chair – Graduate Studies (2009-2012). I was the Co-chair of IAPR Technical Committee 7 – Remote Sensing during 2004-2006. I have numerous recognitions, including received five Outstanding Performance Awards, three Distinguished Performance Awards, the Sanford Fleming Teaching Excellence Award, and a University of Calgary Teaching Excellence Award. In 2010, I received the award for “Research Excellence and Service to the Research Community” by the Canadian Image Processing and Pattern Recognition Society (CIPPRS). In 2012, I received the Engineering Research Excellence Award.

Currently, I am the Associate Dean – Research & External Partnerships within the Faculty of Engineering, responsible for all research related activities in the largest Engineering school in Canada.

Research interests

My research interests are in the fields of computer vision, image processing, and pattern recognition with an emphasis on the automated interpretation of SAR (synthetic aperture radar) and sports analytics (ice hockey). I am an active interdisciplinary and multidisciplinary researcher, publishing refereed journal and conference papers in the diverse fields of remote sensing, sports analytics, computer vision, algorithm design, and biomechanics. My research efforts have led to successful commercial implementations including creating and selling a startup company (CREZ).