Computer Vision and Image Analysis電腦視覺與影像分析

  • 開課機構: 財團法人資訊工業策進會-微軟專業學程MPP
  • 開課平台: 資策會-數位學習平台
  • 講師介紹: Andrew Byrne;Ivan Griffin, PhD;Daire McNamara

  • Andrew Byrne

    Senior Content Developer Microsoft Corporation

    Andrew is a Senior Content Developer at Microsoft. His passion for software and teaching comes from 20+ years of software development experience at Microsoft, Siemens, Ericsson and his own startup.

    Ivan Griffin, PhD

    Founder Emdalo Technologies, Ltd.

    Ivan Griffin is a director and founder of Emdalo Technologies, where he works on developing embedded machine learning solutions. Ivan has over 20 years of experience in the embedded and semiconductor industries. He has a strong technical background combined with commercial and strategic understanding, and a proven track record in a number of successful start-ups. He has co-authored one patent application in computer vision, and two European and US patents in digital broadcast radio. Ivan has a Bachelor’s (1995) and Master’s degree in Electronic/Computer Engineering (1997) and Ph.D. (2010) in Computer Science from the University of Limerick, Ireland.

    Daire McNamara

    Founder Emdalo Technologies, Ltd.

    An engineer by training, Daire co-founded Emdalo Technologies in 2013 with Dr. Ivan Griffin to realize Machine Learning at the Edge. Daire has over 20 years’ experience in the high-tech electronics industries, having held senior commercial, management and product development roles in start-up and early phase companies targeting US, Asia-Pacific and European markets.


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    About this course

    Computer Vision is the art of distilling actionable information from images.

    In this hands-on course, we’ll learn about Image Analysis techniques using OpenCV and the Microsoft Cognitive Toolkit to segment images into meaningful parts. We’ll explore the evolution of Image Analysis, from classical to Deep-Learning techniques.

    We’ll use Transfer Learning and Microsoft ResNet to train a model to perform Semantic Segmentation.


    What you'll learn
    •Apply classical Image Analysis techniques, such as Edge Detection, Watershed and Distance Transformation as well as K-means Clustering to segment a basic dataset.
    •Implement classical Image Analysis algorithms using the OpenCV library.
    •Compare classical and Deep-Learning object classification techniques.
    •Apply Microsoft ResNet, a deep Convolutional Neural Network (CNN) to object classification using the Microsoft Cognitive Toolkit.
    •Apply Transfer Learning to augment ResNet18 for a Fully Convolutional Network (FCN) for Semantic Segmentation.


    Prerequisites:•Working knowledge of Python•Skills equivalent to the following ourses ◦DAT263x: Introduction to AI◦DAT236x: Deep Learning Explained