Wrap Up Lecture Instructor - Simon Lucey 16-423 - Designing Computer Vision Apps
Today • Review - Project Presentations • Emerging Trends in Mobile Vision • Facial Feature Detection in iOS. • Project Discussions
Project Presentations • Each team will have 2.5 minutes per member to present (e.g. 2 member team will have 5 minutes allotted). • We will be strict with this, and will start class at exactly 12pm, please be early!!! • Each team will upload their YouTube clips to the link.
Project Presentations. • 16423 staff will select the “best” presentation plus an honorable mention. • The result will be announced on Piazza after class, the winning team can come by my office to pick up their prize. • All details on the Final Project submission can be found at:- http://16423.courses.cs.cmu.edu/slides/Final_Project.pdf . • Final Write Up is due on midnight Friday 11th of December. Reminder: Final Project makes up 50% of your grade!!!
Today • Review - Project Presentations • Emerging Trends in Mobile Vision • Facial Feature Detection in iOS. • Project Discussions
Why is Mobile CV Different? 6
Why is Mobile CV Different? 6
Why is Mobile CV Different? 7
Why is Mobile CV Different? 7
Why is Mobile CV Different? 7
Balancing Power versus Perception 8
Emerging Trends - Low Power 9 Taken from: http://lpirc.net/
Emerging Trends - High Speed Camera iPhone 6 Samsung Galaxy S5 10
Emerging Trends - High Speed Camera iPhone 6 Samsung Galaxy S5 10
Emerging Trends - Depth Cameras 11
Emerging Trends - Augmented Reality 12
Emerging Trends - Mobile SLAM 13 P. Tanskanen, K. Kolev, L. Meier, F. Camposeco, O. Saurer, M. Pollefeys : Live metric 3d reconstruction on mobile phones. (ICCV 2013)
Emerging Trends - Mobile SLAM 13 P. Tanskanen, K. Kolev, L. Meier, F. Camposeco, O. Saurer, M. Pollefeys : Live metric 3d reconstruction on mobile phones. (ICCV 2013)
Emerging Trends - Mobile SLAM 13 P. Tanskanen, K. Kolev, L. Meier, F. Camposeco, O. Saurer, M. Pollefeys : Live metric 3d reconstruction on mobile phones. (ICCV 2013)
New Work…. 14
New Work…. 14
Emerging Trends - Deep Vision 15
Emerging Trends - Deep Learning BC AD (before ConvNets) (after deep learning) 6.8% ImageNet Challenge Year
Emerging Trends - Deep Learning Training • Data and Compute Speed Matter • Energy and Space do NOT!!!
Emerging Trends - Deep Learning Testing • Data and Compute Speed Matter • Energy and Space now Matter!!!!
Emerging Trends - Deep Learning Testing • Data and Compute Speed Matter • Energy and Space now Matter!!!!
Reminder: ASICs for Low Energy • Application Specific Integrated Circuits (ASIC) • ASICs are perfect for targeting a specific application domain. • Inherently low-power as they are “frozen in silicon” for a specific application domain (e.g. graphics cards, ethernet cards, DSPs, etc.). • Drawbacks, • incredibly expensive to develop. • time consuming and resource-intensive to develop. • Positives, • Extremely energy efficient. 19
Emerging Trends - “Frozen” DeepNets 20 (Taken from recent talk by Yann Lecunn at Hot Chips conference in May 2015).
Emerging Trends - Deep Learning 21 APIs in the current versions of OpenGL ES do not have the “scatter”
Emerging Trends - Deep Learning Deep Learning Check out this article. 21 APIs in the current versions of OpenGL ES do not have the “scatter”
Emerging Trends - Deep Learning 22 (Taken from recent talk by Yann Lecunn at Hot Chips conference in May 2015).
Today • Review - Project Presentations • Emerging Trends in Mobile Vision • Facial Feature Detection in iOS. • Project Discussions
Face Detection
Face Detection in iOS • In iOS face detection comes built in, and can be performed much more efficiently than standard OpenCV. • Utilizes the QuartzCore and CoreImage frameworks within the project.
Face Detection in iOS • In iOS face detection comes built in, and can be performed much more efficiently than standard OpenCV. • Utilizes the QuartzCore and CoreImage frameworks within the project.
Facial Feature Detection
CIFaceFeature
CIFaceFeature
CIFaceFeature Example • We are now going to demonstrate a simple example of face detection in iOS. • On your browser please go to the address, https://github.com/slucey-cs-cmu-edu/CIFaceFeature_Lena • Or better yet, if you have git installed you can type from the command line. $ git clone https://github.com/slucey-cs-cmu-edu/CIFaceFeature_Lena.git
CIFaceFeature Example
CIFaceFeature Example
CIFaceFeature Example
CIFaceFeature Example
CIFaceFeature Example
CIFaceFeature Example
Smerk and GPUImage • Recently, an extension to GPUImage was proposed to allow for the utilization of iOS face detection within iOS. • Called “Smerk” - GitHub project page can be found at:- https://github.com/MattFoley/Smerk
Smerk and GPUImage • Recently, an extension to GPUImage was proposed to allow for the utilization of iOS face detection within iOS. • Called “Smerk” - GitHub project page can be found at:- https://github.com/MattFoley/Smerk
Smerk Example • We are now going to demonstrate how we can perform real- time face tracking through GPUImage. • On your browser please go to the address, https://github.com/slucey-cs-cmu-edu/Smerk_Example • Or better yet, if you have git installed you can type from the command line. $ git clone https://github.com/slucey-cs-cmu-edu/Smerk_Example.git
Smerk Example
Smerk Example
Smerk Example
Smerk Example
Smerk Example
Smerk Example
Smerk Example
Smerk Example
Smerk Example
Smerk Example
Today • Review - Project Presentations • Emerging Trends in Mobile Vision • Facial Feature Detection in iOS. • Project Discussions
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