This document discusses deep convolutional neural networks and their many uses for computer vision. It provides an overview of computer vision, conventional computer vision techniques, and deep learning. It then describes the key components of convolutional neural networks, including convolutional layers, pooling layers, and fully connected layers. The document traces the evolution of convolutional neural networks from early models in 1980 to more recent state-of-the-art models from 2012 onward. It highlights several applications of convolutional neural networks like object detection, segmentation, recognition, style transfer, and image generation. In conclusion, the document demonstrates convolutional neural networks have many uses in computer vision tasks.

1. Deep Convolutional Neural
Networks and their Many Uses
for Computer Vision
Dr. Fares Al-Qunaieer
Lead Data Scientist
Saudi Information Technology Company (SITE)

2. Computer Vision
A field to develop algorithms that make machines and
computers “understand” the content of images and videos
Machine
Learning
Image
Processing
Computer
Vision

3. Conventional Computer Vision
Hand crafted operations and features

4. What is Convolution?
Multiply and sum kernel/filter across the image

5. Convolution
Sobel Operators

6. Deep Learning
Machine Learning Algorithms that use Neural Networks,
with many layers

7. Convolutional Neural Networks (ConvNets)
Image by Aphex34 - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=45679374
• Neural Networks with convolution layers (and some more)
• Learn best kernels/filters from data instead of manually selected

8. Components and Layers of ConvNets
• Convolutional layers
• Pooling layers
• Fully connected layers
• Activation functions (e.g., ReLU)

9. Convolutional Layers
Image by Aphex34 - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=45659236

10. Pooling Layers
Image by Aphex34 - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=45673581
Max Pooling

11. Fully Connected Layers

12. Activation Functions

13. Visualizing ConvNets

14. Evolution of ConvNets
(Historical Journey)

15. Neocognitron (1980)
K. Fukushima: "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position", 1980

16. LeNet-5 (1998)
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition, 1998

17. AlexNet (2012)
Krizhevsky, Alex; Sutskever, Ilya; Hinton, Geoffrey E. "ImageNet classification with deep convolutional neural networks". 2012

18. VGG Net (2014)
Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." 2014

19. GoogleNet – Inception (2014)
C. Szegedy et al., "Going deeper with convolutions," 2015

20. ResNet (2016)
K. He, X. Zhang, S. Ren and J. Sun, " Deep Residual Learning for Image Recognition," 2016

21. Applications

22. Objects Detection and Localization
Images source: https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e
YOLO (You Only Look Once)
Faster RCNN (Region Based CNN)
https://www.youtube.com/watch?v=MPU2HistivI

23. Objects Segmentation
V. Badrinarayanan, A. Kendall and R. Cipolla, "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation," 2017
Ronneberger, O., Fischer, P., & Brox, T. “U-Net: Convolutional
Networks for Biomedical Image Segmentation” 2015

24. Face Recognition (FaceNet)
F. Schroff, D. Kalenichenko and J. Philbin, "FaceNet: A unified embedding for face recognition and clustering," 2015
Image source: https://omoindrot.github.io/triplet-loss

25. Style Transfer
L. A. Gatys, A. S. Ecker and M. Bethge, "Image Style Transfer Using Convolutional Neural Networks," 2016

26. Image Generation - GAN (Generative Adversarial Network)
Image source: Goodfellow et al; Karras, Laine, Aila / Nvidia

27. Other Applications
• Scene labelling
• Action recognition
• Human Pose estimation
• Document analysis
• Medical diagnosis
• And many more …

28. DEMO

29. Thank You