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ICS1020 CV
The document introduces Vanessa Camilleri, a lecturer in AI who is interested in computer vision, virtual reality, games, and machine learning education; it then provides an overview of computer vision, discussing how machines capture visual data through cameras, how images are digitized and represented, and the main techniques used to make sense of visual data including object detection, recognition and neural networks.
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ICS1020 CV
- 1. Dr V. Camilleri- vanessa.camilleri@um.edu.mt November 2020 Foundations of Ai Principles of Computer Vision
- 2. Hi ⊠Iâm VanessaCamilleri Iâm a lecturer at the Department of AI, Faculty of ICT I can be contacted via vanessa.camilleri@um.edu.mt My main interests are in the ïŹelds of Creative Computing & Education, which include VR, AR & MR, Games & Game AI, and ML for Education.
- 4. Quite a vasttopic! Computer Vision ⊠So to help our understanding we will be focusing on 1 scenario⊠a self-driving ambulance
- 5. âą How canwe capture the world around us? âą How can we make it easier for the machines to capture this visual data? âą How can the machines make sense out of this data? How will they learn? What is Computer Vision?
- 7. Computer Vision helps machinesmake sense out of received visual data.
- 8. âą First cameraused the camera obscura concept âą First surviving captured shot in mid 1820âs by NiĂ©pce Cameras How do machines capture visual data?
- 9. âą What isthe best camera for computer vision use? âą Depends on the use & context âą Some general characteristics may include: âą low-latency, âą adequate low-light and light transition performance, âą IO connections, and âą weatherprooïŹng Cameras How do machines capture visual data?
- 10. Activity time Work ingroups (use chat/social media/âŠ) Focus on Practical Applications of Computer Vision and discuss current developments in one of these research ïŹelds: facial recognition, self-driving cars, AR & MR, healthcare, etc.
- 11. âą Acquisition âąProcessing âą Understanding Stages of Computer Vision
- 12. âą The processof acquiring images âą 2D media âą 3D media âą An engineering discipline focusing on automating/digitising the human vision system Image Acquisition Stages of Computer Vision
- 13. âą A numericrepresentation of an image on a 2-D Grid âą Each element is referred to as a pixel and its value represents the shade or colour of that segment Image Digitisation Stages of Computer Vision
- 14. âą Colour inImages is represented by: âą Bilevel images; pixels are either 0 or 1 âą Grayscale images; pixel values range from 0 to 255 âą RGB images; 3 channels/ values per pixel representing red, green or blue Image Digitisation Stages of Computer Vision
- 15. âą Intrinsic Parameters: âą Focal length âą Principal Point âą Lens Distortion âą Extrinsic Parameters: âą Rotation âą Translation (relative to other cameras or original position) Camera Parameters Stages of Computer Vision
- 16. âą How canthe machine âunderstandâ the 2D space of the image? âą How can the machine be endowed with a 3D understanding of the complexity of the environment? 2D images to 3D Scenes Stages of Computer Vision
- 17. 2D images to3D Scenes Stages of Computer Vision
- 18. Making sense Stages ofComputer Vision âą 6 main techniques âą Object ClassiïŹcation & Detection âą Object Recognition âą Object Tracking âą Object Labelling âą Text Recognition
- 19. Making sense Stages ofComputer Vision âą Approaches: âą ArtiïŹcial Neural Networks âą Deep Learning âą Supervised Learning âą Convolutional Neural Networks âą Recurrent Neural Networks âą Generative Query Networks (Google DeepMind)
- 20. Making sense Stages ofComputer Vision âą ArtiïŹcial Neural Network âą Deep Neural Network (not to be confused with Deep Learning) âą Deep Learning
- 21. Making sense Stages ofComputer Vision âą Convolutional Neural Network
- 22. Object ClassiïŹcation &Detection Stages of Computer Vision âą ClassiïŹcation: Assigns a label to the whole image âą Usually denoted by a bounding box âą Detection: Applies classiïŹcation and localisation to many objects instead of just a single dominant object
- 23.
- 24. Itâs all aboutmaking sense⊠Challenges of Computer Vision But what about a caption forâŠ
- 25. What about this⊠Challengesof Computer Vision
- 26. Activity time Work ingroups (use chat/social media/âŠ) Go back to the self-driving ambulance scenario. Think about Computer Vision. 1. How can diïŹerent applications of CV be used during an accident? 2. What services can CV oïŹer for the ambulance?