Live-Stream


Mathematical Morphology in Geoscience, Remote Sensing and Geospatial Data Science: An Overview

02 March  10:00 am EST

Abstract: Data available at multiple spatial/spectral/temporal scales pose numerous challenges to the data scientists. Of late researchers paid wide attention to handling such data acquired through various sensing mechanisms to address intertwined topics—like pattern retrieval, pattern analysis, quantitative reasoning, and simulation and modeling—for better understanding spatiotemporal behaviors of several terrestrial phenomena and processes [1]. Georges Matheron and Jean Serra of the Centre of Mathematical Morphology, Fontainebleau founded Mathematical Morphology (MM) [2]-[5]. Since the birth of MM in the mid-1960s, its applications in a wide-ranging disciplines have illustrated that intuitive researchers can find varied application-domains to extend the applications of MM. Mathematical Morphology is one of the better choices to deal with the aforementioned intertwined topics. Various original algorithms and techniques that are mainly based on mathematical morphology have been developed and demonstrated. This lecture that presents an overview of mathematical morphology and its applications in geosciences, remotely sensed satellite data and Digital Elevation Model (DEM) processing and analysis, as well as geospatial data sciences, would be useful for those with research interests in image processing and analysis, remote sensing and geosciences, geographical information sciences, spatial statistics, and mathematical morphology, mapping of earth-like planetary surfaces, etc. The content of this broad overview of the lecture will be offered in two parts. In the first part, basic morphological transformations would be covered. An overview of the applications of those transformations, covered in the first part, to understand the granulometries, morphological filtering, morphological interpolations and extrapolations would be given with several case studies during the second part.

Bibliography
1. B. S. Daya Sagar and Jean Serra, 2010, Preface: Spatial Information Retrieval, Analysis, Reasoning and Modelling, International Journal of Remote Sensing, v. 31, no. 22, p. 5747-5750.
2. Georges Matheron, 1975, Random Sets and Integral Geometry (New York: John Wiley & Sons).
3. Jean Serra, 1982, Image Analysis and Mathematical Morphology, Academic Press: London, p. 610.
4. Pierre Soille, 2010, Morphological Image Analysis: Principles and Applications, Springer, p. 408.
5. B. S. Daya Sagar, 2013, Mathematical Morphology in Geomorphology and GISci, CRC Press: Boca Raton, p. 546.

Speaker
Prof. Dr. B. S. Daya Sagar
IEEE GRSS Distinguished Lecturer (DL)
Systems Science and Informatics Unit, Indian Statistical Institute-Bangalore Centre, India

B. S. Daya Sagar is a Full Professor of the Systems Science and Informatics Unit (SSIU) at the Indian Statistical Institute. Sagar received his MSc and Ph.D. degrees in Geoengineering and Remote Sensing from the Faculty of Engineering, Andhra University, Visakhapatnam, India, in 1991 and 1994 respectively. He is also the first Head of the SSIU. Sagar has made significant contributions to the field of geosciences, with special emphasis on the development of spatial algorithms meant for geo-pattern retrieval, analysis, reasoning, modeling, and visualization by using concepts of mathematical morphology and fractal geometry. He has published over 85 papers in journals and has authored and/or guest-edited 11 books and/or special theme issues for journals.  Learn more about Prof. Dr. B. S. Daya Sagar.

Abstract:

GPU/accelerator architectures have greatly improved the training and inferencing speed for neural-network-based machine learning models. As major industry players race to develop ambitious applications such as self-driving vehicles, unstructured data analytics, human-level interactive systems, and human intelligence augmentation, major challenges remain in computational methods as well as hardware/software infrastructures required for these applications to be effective, robust, responsive, accountable and cost-effective. These applications impose much higher levels of data storage capacity, access latency, energy efficiency, and throughput. In this talk, I will present a vision for building a new generation of computing components and systems for these applications.

Speaker’s Bio:

Wen-mei W. Hwu is a Professor and holds the Sanders-AMD Endowed Chair in the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign. He is the director of the IMPACT research group (www.crhc.uiuc.edu/Impact). He co-directs the IBM-Illinois Center for Cognitive Computing Systems Research (C3SR) and serves as one of the principal investigators of the NSF Blue Waters Petascale supercomputer. For his contributions, he received the ACM SigArch Maurice Wilkes Award, the ACM Grace Murray Hopper Award, the IEEE Computer Society Charles Babbage Award, the ISCA Influential Paper Award, the IEEE Computer Society B. R. Rau Award and the Distinguished Alumni Award in Computer Science of the University of California, Berkeley. He is a fellow of IEEE and ACM. Dr. Hwu received his Ph.D. degree in Computer Science from the University of California, Berkeley.

Description

The Low Power Image Recognition Challenge (LPIRC) 2019 is a one-day workshop that will extend the successes of LPIRC from the past four years, identifying the best computer vision solutions that can simultaneously achieve high accuracy and energy efficiency. Since the first competition, held in 2015, the winners’ solutions have improved 24x in the ratio of accuracy divided by energy.

The live-stream of LPIRC will feature presentations from researchers and last year's winner (all times PDT):

09:30-09:40 - Welcome by Organizers and Summary of Online Challenge

09:40-10:30 - 2018 competition winners will give a talk on their winning solutions — Amazon's Tao Sheng, and Expasoft's Alexander Goncharenko and Sergey Alyamkin

10:30-11:10 - Invited Talk: Rethinking the Computations in Computer Vision (and the Hardware that Computes Them) - UC Berkeley's Kurt Keutzer

11:10-13:40 - Live-stream will shut down temporarily

13:40-14:00 - Invited Talk: Visual Wake Words Challenge - Google's Aakanksha Chowdhery and Pete Warden

Additional Supporting Details

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    Title, Company

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  • Additional Speaker

    Title, Company

    Brownie sesame snaps candy canes. Wafer muffin powder chocolate bear claw bonbon pastry. Topping caramels carrot cake marshmallow soufflé icing.

  • Additional Speaker

    Title, Company

    Brownie sesame snaps candy canes. Wafer muffin powder chocolate bear claw bonbon pastry. Topping caramels carrot cake marshmallow soufflé icing.

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Muffin croissant fruitcake candy cake.

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  • Brownie sesame snaps candy canes. Wafer muffin powder chocolate bear claw bonbon pastry. Topping caramels carrot cake marshmallow soufflé icing.

    > Read More

  • Brownie sesame snaps candy canes. Wafer muffin powder chocolate bear claw bonbon pastry. Topping caramels carrot cake marshmallow soufflé icing.

    > Read More

  • Brownie sesame snaps candy canes. Wafer muffin powder chocolate bear claw bonbon pastry. Topping caramels carrot cake marshmallow soufflé icing.

    > Read More

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