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Computer Vision Lab
- 1. computer vision lab by Karenne mata
- 2. hello! I am Karenne Mata You can find the material for this lab in github https://github.com/karenne/ComputerVisionLab 2
- 3. “Computer vision and machine learning have really started to take off, but for most people, the whole idea of what is a computer seeing when it's looking at an image is relatively obscure. Mike Krieger 3
- 4. What is computer vision? "Computer vision is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do.” - Wikipedia 4 ?
- 5. Still a little obscure, isn’t it? 5 I am the Machine looking at this image A Black Box, actually my brain I am probably seeing a 5! But I need some training before getting so good at seeing numbers
- 6. HISTORY OF COMPUTER VISION or how have we built the black box Optical Character Recognision 1870 -1974: Development of the technology that allows the translation from image to text 1974 – 2000: Massive commercialization 2000 – Now: Open software realeased (Adobe, GoogleDrive, WebOCR) The fathers of computer vision Larry Roberts (1963); “Machine Perception of three dimensional solids”; MIT PhD. Thesis. David Marr (1982); “Vision. A Computational Investigation into the Human Representation and Processing of Visual Information”; MIT Press Convolutional Nets 1980 Fukushima. The Neocognitron 1998 Yann LeCun et al. LeNet5 2012 AlexNet 2015 ResNet-152 6
- 7. Some necessary maths…. What is a neural network 7 a11 a1n… am1 amn… … y1 ym … x1x1 xn… x2 xn X = zh = wTX + b ah = g(zh) zo = wT ah + b ao = g(zo)
- 8. convolutional neural networks (CNN). The foundations of image classification 8 Input Output 0 0 0 0 0 1 0 0 0 0 ✘ Convolutional Layer: ✘ Kernel ✘ Padding ✘ Stride ✘ Activation Function ✘ Pooling Layer p11 p1n… pm1 pmn… p11 p1n… pm1 pmn… p11 p1n… pm1 pmn… * More detail on: https://www.analyticsvidhya.com/blog/2020/02/mathematics- behind-convolutional-neural-network/
- 9. Opening convolutional neural networks 9 * Figure 4 from paper Zeiler et al. (2013) Visualizing and Understanding Convolutional Networks.
- 10. Business stories 1. Autonomous vehicles Self driving cars are constantly streaming the environment to react according the signals and other cars or people. 2. Google Translate app Google allows the translation of the text embedded in images thus the user doesn’t need to write the words down. 3. Facial recognition Security systems can use advanced algorithms of computer vision to recognize a person only showing the face. 4. Healthcare Diagnostic of X-rays and all the image based sources in medicine. 5. Real-time sports tracking See in real time the performance of a player and the deployment of a strategy. 6. Manufacturing Packaging and labeling quality assessments. 10 * Bernard Marr; 7 Amazing Examples Of Computer And Machine Vision In Practice (8 Apr 2019) Forbes
- 11. Another business story: Emotion AI 1. Medical diagnosis. Diagnosis of some mental health diseases such as depression or anxiety 2. Truth detector Emotion AI could be used as a truth detection device that can be used e.g. in fraud detection 3. Marketing Knowing how the customer feels regarding a product or the mood of the person can improve the quality of the campaigns 11 * 13 Surprising Uses For Emotion AI Technology, Smarter with Garner (Susan Moore. September 11, 2018) "Emotion AI is a subset of artificial intelligence (the broad term for machines replicating the way humans think) that measures, understands, simulates, and reacts to human emotions” Meredith Somers, Emotion AI, explained (March 8, 2019)
- 12. 12 An example Check this github repository to use an API for face expression recognision: https://github.com/justadudewhohacks/face-api.js This is fun!
- 13. thanks! Any questions? You can find me at https://www.linkedin.com/in/karenne-mata-4b81a0116/ 13
Editor's Notes
- Larry Roberts – How to represent images from 2D arrays to 3D using topology and algebra. Representation of the image rather than trying to convert to text. David Marr creates the bottom up approach, low level image processing meaning detecting corners, edges and motion Fukushima: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position” Yann LeCun “Gradient-Based Learning Applied to Document Recognition” 1989 LeCun CNN with backpropagation
- Stride is the number of pixels shifts over the input matrix. Pad the picture with zeros (zero-padding) so that it fits ReLU stands for Rectified Linear Unit for a non-linear operation. The output is ƒ(x) = max(0,x). Pooling layers section would reduce the number of parameters when the images are too large. Spatial pooling also called subsampling or downsampling which reduces the dimensionality of each map but retains important information. Spatial pooling can be of different types: Max Pooling Average Pooling Sum Pooling
- Stride is the number of pixels shifts over the input matrix. Pad the picture with zeros (zero-padding) so that it fits ReLU stands for Rectified Linear Unit for a non-linear operation. The output is ƒ(x) = max(0,x). Pooling layers section would reduce the number of parameters when the images are too large. Spatial pooling also called subsampling or downsampling which reduces the dimensionality of each map but retains important information. Spatial pooling can be of different types: Max Pooling Average Pooling Sum Pooling