Texture Classification – VISION AND IMAGE PROCESSING (VIP) RESEARCH GROUP

Fernando J. Pena Cantu

Fernando Jose Pena Cantu — Mon, 01 May 2023 21:49:32 +0000

Hi! My name is Fernando J. Pena Cantu and I am a MASc student with the VIP Lab’s Remote Sensing Group. My current research focuses on investigating the relationship between textural features and the incidence angle in synthetic aperture radar (SAR) imagery for sea ice classification. This topic has important implications for understanding the effects of SAR imaging parameters on sea ice detection and classification.

I am also involved in research on deep learning for sea ice segmentation and small object detection for whale surveying.

My research interests span a variety of topics in remote sensing, including synthetic aperture radar, textural features, deep learning, sea ice segmentation, domain adaptation, feature selection, and diffusion models. I am excited to continue exploring these areas and contributing to the advancement of remote sensing technologies.

Email: fjpenaca@uwaterloo.ca

Supervisors

David Clausi, Andrea Scott

Research interests

My current research interest are: Remote Sensing , Synthetic Aperture Radar, Textural Features, Deep Learning, Sea Ice Segmentation, Domain Adaptation, Feature Selection, Diffusion Models

Research topics

Computer Vision
Image Segmentation/Classification
Remote Sensing

Research demos

Satellite SAR Sea Ice Classification
Texture Classification

Linlin Xu

Linlin Xu — Wed, 26 Apr 2023 14:17:28 +0000

I am a post-doc in Vision and Image Processing Lab, working with professors David Clausi, Alexander Wong, Fan Li, and Steven Leigh, on machine learning, intelligent remote sensing analytics and environmental monitoring.

I recieved my PhD degree in Geography in 2014 from Department of Geography and Environment Management, University of Waterloo. I achieved my Master in 2010 and Bachelor degrees in 2007, both in geomatics engineering, from China University of Geosciences (Beijing).

Research interests

My research interests lie broadly in the following aspects: (1) SAR, PolSAR data processing and the associated applications, particularly in ocean environment monitoring; (2) Hyperspectral and multispectral unmixing, classification, segmentation, feature extraction, and the associated applications; (3) Quantitative and computational methods in remote sensing and other geospatial application, spatial-multivariate data modeling and mining, quantitative remote sensing, image processing & computer vision, GIS, WebGIS; (4) GPS and InSAR data processing with the application in subside monitoring and DEM production; (5) Airborne, mobile, and terrestrial laser scanning mapping for land observation and 3D object modelling.

Research topics

Research demos

3D Reconstruction of Underwater Scenes
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

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

PDF

Ph.D

M.A.Sc

Supervision – Completed

PDF

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

Rishi Jobanputra

vipadmin — Thu, 23 Mar 2023 20:35:08 +0000

Email: rjobanpu@yahoo.com

Supervisors

David Clausi

Research interests

My research interests include preserving texture boundaries for SAR sea ice segmentation.

Research topics

Computer Vision
Image Segmentation/Classification
Remote Sensing

Research demos

Texture Classification

Publications

Journal Articles / Conference Papers

Journal Articles

Conference Papers

Top

Li Liu

vipadmin — Thu, 23 Mar 2023 17:49:20 +0000

Li Liu received her B.S. degree in Communication Engineering in 2003 and her M.S. degree in Remote Sensing and Geographic Information Systems in 2005 from the National University of Defense Technology, Changsha, China, where she is currently pursuing a Ph.D. During her PhD study she spent two years as a Visiting Student at the University of Waterloo, Canada, and she has held a visiting appointment at the Peking University in China.

Email: dreamliu2010@gmail.com

Supervisors

David Clausi, Paul Fieguth

Research interests

Her current research interests include computer vision, texture analysis, pattern recognition and image processing.

Research topics

Image Segmentation/Classification

Research demos

Texture Classification

Publications

Journal Articles / Conference Papers

Journal Articles

Conference Papers

Top

Texture Classification

vipadmin — Tue, 21 Mar 2023 17:21:03 +0000

Texture Classification is the problem of distringuishing between textures, a classic problem in pattern recognition. Since many very sophisticated classifiers exist, the key challenge here is the development of effective features to extract from a given textured image.

Many approaches have been defined to extract features, such as Gabor filters or wavelets, however a great deal of recent work has focused on patch-based methods, whereby a texture is classified strictly based on a set of small patches of pixels extracted from a given textured image.

The difficulty is that too small a patch fails to be sensitive to non-local features of the texture, whereas a very large patch leads to enormous feature vectors and computational problems associated with very high-dimensional pattern recognition.

Recent work by Liu proposed using random linear functions of patches. The number of such random features needed turns out to be relatively modest, therefore it is suddenly feasible to do texture classification using large image patches, then with some number of random features. Astonishingly, such an approach outperforms the texture classification of finely-designed, state-of-the-art texture filters.

Related people

Directors

Students

Alumni

Related publications

Journal Articles

Conference Papers

Clausi, D. A., “Towards a Novel Approach for Texture Segmentation of SAR Sea Ice Imagery”, 26th International Symposium on Remote Sensing of Environment and 18th Annual Symposium of the Canadian Remote Sensing Society, Vancouver, BC, Canada, pp. 257-261, 1996. Get it here.

Theses

Jobanputra, R., “Preserving Texture Boundaries for SAR Sea Ice Segmentation”, Department of Systems Design Engineering, Waterloo, Ontario, Canada, University of Waterloo, 2004. Get it here.

Clausi, D. A., “Texture Segmentation of SAR Sea Ice Imagery”, Department of Systems Design Engineering, Waterloo, Ontario, Canada, University of Waterloo, pp. 176, 1996. Get it here.

Paul Fieguth

Paul Fieguth — Thu, 23 Feb 2023 20:12:37 +0000

Paul Fieguth received his B.A.Sc. degree from the University of Waterloo, Ontario, Canada, in 1991 and his Ph.D. degree from the Massachusetts Institute of Technology, Cambridge, in 1995, both degrees in electrical engineering. He joined the faculty at the University of Waterloo in 1996, where he is currently Professor and, since 2010, Department Chair in Systems Design Engineering. He has held visiting appointments at the University of Heidelberg in Germany, at INRIA/Sophia in France, at the Cambridge Research Laboratory in Boston, at Oxford University and the Rutherford Appleton Laboratory in England, and with postdoctoral positions in Computer Science at the University of Toronto and in Information and Decision Systems at MIT.

Students

Supervision – Current

PDF

ph.D.

M.A.Sc.

Supervision – Completed

PDF

ph.D.

M.A.Sc.

Research topics

Biomedical Imaging
BIOSCAN Insect Biodiversity Assessment
Computer Vision
Image Segmentation/Classification
Multiresolution Techniques
Remote Sensing
Scientific Imaging
Stochastic Models
Video Analysis

Research demos

3D Reconstruction of Underwater Scenes
Decoupled Active Contours
Image Denoising
Porous Media
SAR Sea Ice Image Synthesis
Skin Cancer Detection
Texture Classification

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).