Foundations & Core in Computer Vision: A System Perspective

yakzephyrAI and Robotics

Nov 24, 2013 (3 years and 6 months ago)

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Foundations & Core in Computer Vision:
A System Perspective

Ce Liu


Microsoft Research New England

Vision vs. Learning


Computer vision: visual application of machine learning?


Data


features


algorithms


data


ML
: design algorithms given input and output data


CV
:
find the best input and output data given available
algorithms



Theoretical vs. Experimental


Theoretical analysis of a visual system


Best & worst cases


Average performance


Theoretical
analysis is challenging
as many visual
distributions are hard to model (signal processing: 2
nd

order processes, machine learning: exponential families)


Experimental approach: full spectrum of system
performance as a function of the amount of data,
annotation, number of categories, noise, and other
conditions


Quality vs. Speed


HD videos, billions of images to index


Real time & 90% vs. one hour per frame & 95%?


Mechanism to balance quality and speed in modeling


Automatic vs. semi
-
automatic


Common review feedback:
parameters are hand
-
tuned;
not clear how to set the parameters



Vision system user feedback:
I don’t know how to tweak
parameters!


Computer
-
oriented vs. human
-
oriented representations


Human
-
in
-
the
-
loop (collaborative) vision


How to optimally use humans (what, which and how
accurate) beyond traditional active learning


Model design by crowd
-
sourcing


Learning by subtraction

Algorithms vs. Sensors


Two approaches to solving a vision problem


Look at images, design algorithms, experiment, improve…


Look at cameras, design new/better sensors, …


Cameras for full
-
spectrum, high res, low noise, depth,
motion, occluding boundary, object, …


What’s the optimal sensor/device for solving a vision
problem?


What’s the limit of sensors?



Thank you!

Ce Liu


Microsoft
Research New
England