The document discusses computer vision and related technologies presented by Componica, LLC. It covers topics like facial detection, facial recognition, image registration, augmented reality, QR codes, and optical character recognition. Examples of applications are identifying diseases with smartphones, emotion detection, and automated manufacturing inspection. The conclusion emphasizes opportunities for computer visioning technology in society.

1. Computer Vision:
Extracting Data from the
Visual World
A Brief Example...
!
Steven Mitchell, Ph.D.
Componica, LLC

2. About us.
Componica, LLC (http://www.componica.com/)
Strong Background in Computer Vision
Copyright 2011 - Componica, LLC (http://www.componica.com/)

3. About us.
Componica seamlessly combines the worlds of
machine learning, computer visioning & mobile
development & applying the latest in visionary
technology to the world of mobile media.
Words for Spanish / Russian / French
Copyright 2011 - Componica, LLC (http://www.componica.com/)

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Why is computer vision relevant?
How do these things work?
Should I be concernd?

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In this slideshow:
Facial Detection - Find me a face.
Facial Recognition - Who’s face is it?
Image Registration - Aligning pictures together.
...which leads to augmented reality.
QR Codes - They’re everywhere.
Optical Character Recognition - Reading Stuff.

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Face Detection
This is NOT facial recognition.
Developed by Viola / Jones in 2000. Major break-thru in
image recognition...this was not possible prior.
How much does a cow weigh?
An army of simple face detectors.
"Robust Real-time Object Detection"!
Paul Viola and Michael Jones

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BTW, It’s how the Kinect sees people.

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BTW, It’s how the Kinect sees people.
"Real-Time Human Pose Recognition in Parts from Single Depth Images"!
Shotton, Fitzgibbon, Cook, Sharp, Finocchio, Moore, Kipman, Blake!
Microsoft Research Cambridge & Xbox Incubation

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Facial Recognition
Remove effects caused by lighting and
perspective.
After you find a face, reduce it to numbers.
"Statistical Models of Appearance for Computer Vision"!
T.F. Cootes and C.J.Taylor

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Facial Recognition
Let’s mix some paint...
Comparing numbers in hyperspace
k-Nearest Neighbor, Wikipedia

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The most common way to register images. Find the most
interesting points on the two images.
Compare all the interesting points from one image to the other
forming matching pairs of points between images.
Image Registration - Interesting Points

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Augmented Reality
FAST interest point detection 0.55ms
Building query bit masks 0.12ms
Matching into database 0.35ms
Robust pose estimation 0.1ms
Total frame time 1.12ms
Table 1. Timings for the stages of our approach on a dataset with
images taken from within the range of trained viewpoints.
Figure 5. Increasing the range of viewpoint bins in the training set
allows more viewpoint invariance to be added in a straightforward
manner.
gests that the bit count dissimilarity score provides a reason-
able way of scoring matches. To confirm this we computed
the average number of inlier and outlier matches over all of
the frames in the two sequences, and plotted these against
the dissimilarity score obtained for the match in Figure 4.
For the sequence on the left where the viewpoints are in-
cluded in the training set many good matches are found in
Once you have correspondence, you
can compute 3D geometry.
http://mi.eng.cam.ac.uk/~er258/work/fast.html
http://nghiaho.com

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QR Codes
http://en.wikipedia.org/wiki/QR_Code
!
"Quick Response code" invented
by Toyota subsidiary Denso
Wave in 1994.
Open License
Up to 2.5K of data
Error Correction
Easy to read and generate:
ZXing library

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Optical Character Recognition
iPhone 4th Gen
iPod Touch 4th Gen

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Optical Character Recognition

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Commentary
Ubiquitous Surveillance...extreme dislike.
Birthday Paradox...The probability that, in a set of
n randomly chosen people, some pair of them will
have the same birthday.

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Commentary
Video Cameras may fit the criteria of legally blind.

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Computer visioning technology and society:
opportunities, possibilities:
Smartphones that ID diseases, plants, insects.
Robotic lawnmowers that don’t run over the
neighbor’s cat.
Computers that judge emotions by reading your
face.
Keyless entry based on face, iris.
Automated inspection of manufactured parts.
Conclusion