Hands-On Image and Video Analysis using Deep Learning Methods

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26 July. 2019

Workshop on Hands-On Image and Video Analysis using Deep Learning Methods

Prof. Marios S. Pattichis
Department of Electrical and Computer Engineering
The University of New Mexico
Albuquerque, NM, USA

23 - 24 July, 2019

Organised by: RISE & IEEE Signal Processing Society and Engineering in Medicine and Biology Cyprus Chapters

Abstract:

Several image and video analysis problems have been greatly impacted by the development of recent Deep Learning methods. Applications range from image classification, object detection, image segmentation, to video activity classification.

The primary goal of the workshop was to cover the essential elements that are needed to understand the essential characteristics of the deep learning architectures used in different applications in image and video analysis.The 2-day workshop covered the basic algorithms with example code on the first day, followed by hands-on implementations on the second day.  The workshop described practical methods in Python for implementing, training, and testing custom architectures for particular applications. Example topics included 2D and 3D Convolutional neural nets (CNN), Residual Networks, Autoencoders, Random Forests, and Transfer learning.

Biography:

Marios Pattichis received the B.Sc.(High Hons. and Special Hons.) in Computer Sciences and the B.A. (High Hons.) degree in Mathematics, both in 1991, the M.S. degree in Electrical Engineering in 1993, and the Ph.D. degree in computer engineering in 1998, all from the University of Texas, Austin. He is currently a Professor and Associate Chair with the Department of Electrical and Computer Engineering, University of New Mexico (UNM), Albuquerque. His current research interests include digital image, video processing, communications, dynamically reconfigurable computer architectures, and biomedical and space image-processing applications.
Dr. Pattichis is currently a Senior Associate Editor for the IEEE Transactions on Image Processing. He has previously served a Senior Associate Editor for the IEEE Signal Processing Letters, Associate Editor for the IEEE Transactions on Image Processing, IEEE Transactions on Industrial Informatics, and as a Guest Associate Editor for the IEEE Transactions on Information Technology in Biomedicine. At UNM, he is currently the director of the image and video Processing and Communications Lab (ivPCL). His research on large-scale video analysis methods is currently funded by the National Science Foundation. His efforts to teach Python and Raspberry Pis to teach the basics of programming to under-represented middle school students are also funded by the National Science Foundation.

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