intelligent vision processor l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Intelligent Vision Processor PowerPoint Presentation
Download Presentation
Intelligent Vision Processor

Loading in 2 Seconds...

play fullscreen
1 / 58

Intelligent Vision Processor - PowerPoint PPT Presentation


  • 136 Views
  • Uploaded on

Intelligent Vision Processor. John Morris Computer Science/ Electrical & Computer Engineering, The University of Auckland. “Iolanthe II” rounds Channel Island - Auckland-Tauranga Race, 2007. Intelligent Vision Processor. Applications  Robot Navigation

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Intelligent Vision Processor' - issac


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
intelligent vision processor

Intelligent Vision Processor

John Morris

Computer Science/Electrical & Computer Engineering,The University of Auckland

“Iolanthe II” rounds Channel Island -Auckland-Tauranga Race, 2007

intelligent vision processor2

Intelligent Vision Processor

Applications

 Robot Navigation

Collision avoidance – autonomous vehicles

Manoeuvring in dynamic environments

Biometrics

  • Face recognition

Tracking individuals

Films

Markerless motion tracking

Security

Intelligent threat detection

Civil Engineering

Materials Science

Archaeology

intelligent vision
Intelligent Vision
  • Our vision system is extraordinary
    • Capabilities currently exceed those of any single processor
  • Our brains
    • Operates on a very slow ‘clock’:
      • kHz region
    • Massively parallel
      • >1010 neurons can compute in parallel
    • Vision system (eyes) can exploit this parallelism
      • ~3 x 106 sensor elements (rods and cones) in human retina
intelligent vision5
Intelligent Vision
  • Matching and recognition
    • Artificial intelligence systems are currently not in the race!

For example

    • Face recognition
      • We can recognize faces
        • From varying angles
        • Under extreme lighting conditions
        • With or without glasses, beards, bandages, makeup, etc
        • With skin tone changes, eg sunburn
    • Games
      • We can strike balls travelling at > 100km/h
      • and
      • Direct that ball with high precision
human vision
Human vision
  • Uses a relatively slow, but massively parallel processor (our brains)
  • Able to perform tasks
    • At speeds
    • and
    • With accuracy

beyond capabilities of state-of-the-art artificial systems

intelligent artificial vision
Intelligent Artificial Vision
  • High performance processor
    • Too slow for high resolution (Mpixel+) imagein real time (~30 frames per second)
  • Useful vision systems
    • Must be able to
      • Produce 3D scene models
      • Update scene models quickly
        • Immediate goal: 20-30Hz to mimic human capabilities
        • Long term goal: >30 Hz to provide enhanced capabilities
      • Produce accurate scene models
intelligent artificial vision8
Intelligent Artificial Vision

Use human brain as the fundamental model

We know it works better than a conventional processor!

We need

human vision systems

Lens

Eyeball

Retina

Human Vision Systems
  • Higher order animals all use binocular vision systems
    • Permits estimation of distance to an object
    • Vital for many survival tasks
      • Hunting
      • Avoiding danger
      • Fighting predators
  • Distance (or depth) computed by triangulation

P

P’’

P’

P’-P’’ is the disparity

It increases as P comes

closer

human vision systems10
Human Vision Systems
  • Higher order animals all use binocular vision systems
    • Permits estimation of distance to an object
    • Vital for many survival tasks
      • Hunting
      • Avoiding danger
      • Fighting predators
  • Distance (or depth) computed by triangulation

P

P’’

P’

P’-P’’ is the disparity

Increases as P comes

closer

artificial vision

P

Opticalaxis

P’’

P’

Fixationpoint

P

F

P’’

P’

Artificial Vision
  • Evolution took millions of years to optimize vision
    • Don’t ignore those lessons!
    • Binocular vision works
  • Verging optics
    • Human eyes are known to swivel to ‘fixate’ on an object of interest
real vs ideal systems
Real vs Ideal Systems
  • Real lenses distort images
  • Distortion must be removed for high precision work!
  • Easy
  • but
  • Conventional technique uses iterative solution
    • Slow!
  • Faster approach needed for real time work

Image of a rectangular gridwith a real lens

why stereo
Why Stereo?
  • Range finders give depth information directly
    • SONAR
      • Simple
      • Not very accurate (long l)
      • Beam spread  Low spatial resolution
    • Lasers
      • Precise
      • Low divergence  High spatial resolution
      • Requires fairly sophisticated electronics
        • Nothing too challenging in 2008

Why use an indirect measurement when direct ones are available?

why stereo14
Why Stereo?
  • Passive
    • Suitable for dense environments
    • Sensors do not interfere with each other
    • Wide area coverage
      • Multiple overlapping views obtainable without interference
  • Wide area 3D data can be acquired at high rates
  • 3D data aids unambiguous recognition
    • 3rd dimension provides additional discrimination
  • Textureless regions cause problems

but

    • Active illumination can resolve these
    • Active patterns can use IR (invisible, eye-safe) light
artificial vision challenges16
Artificial Vision - Challenges
  • High processor power
    • Match parallel capabilities of human brain
  • Distortion removal
    • Real lenses always show some distortion
  • Depth accuracy
    • Evolution learnt about verging optics millions of years ago!
  • Efficient matching
    • Good corresondence algorithms
artificial vision17
Artificial Vision
  • Simple stereo systems are being produced
    • Point Grey, etc
    • All use canonical configuration
      • Parallel axes, coplanar image planes
    • Computationally simpler
    • High performance processor doesn’t have time to deal with the extra computational complexity of verging optics

Point Grey Research

Trinocular vision system

artificial system requirements
Artificial System Requirements
  • Highly Parallel Computation
    • Calculations are not complex

but

    • There are a lot of them in megapixel+ ( >106 ) images!
  • High Resolution Images
    • Depth is calculated from the disparity
      • If it’s only a few pixels, then depth accuracy is low
      • Basic equation (canonical configuration only!)

Baseline

Focal Length

Depth, z = b f

d p

Pixel size

Disparity

artificial system requirements19
Artificial System Requirements
  • Depth resolution is critical!
    • A cricket* player can catch a 100mm ball travelling at 100km/h
  • High Resolution Images Needed
    • Disparities are large numbers of pixels
    • Small depth variations can be measured

but

    • High resolution images increase the demand for processing power!

*Strange game played in former British coloniesin which a batsmen defends 3 small sticksin the centre of a large field against a bowler whotries to knock them down!

artificial system requirements20
Artificial System Requirements
  • Conventional processors do not have sufficient processing power
  • but Moore’s Law says
    • Wait 18 months and the power will have doubled

but

    • The changes that give you twice the poweralso give your twice as many pixels in a rowand four times as many in an image!

Specialized highly parallel hardwareis the only solution!

fpga hardware
FPGA Hardware
  • FPGA = Field Programmable Gate Array
    • ‘Soft’ hardware
    • Connections and logic functions are ‘programmed’ in much the same way as a conventional von Neuman processor
    • Creating a new circuit is about as difficult as writing a programme!
    • High order parallelism is easy
      • Replicate the circuit n times
        • As easy as writing a for loop!
fpga hardware23
FPGA Hardware
  • FPGA = Field Programmable Gate Array
    • ‘Circuit’ is stored in static RAM cells
    • Changed as easily as reloading a new program
fpga hardware24
FPGA Hardware
  • Why is programmability important?
  • or
  • Why not design a custom ASIC?
    • Optical systems don’t have the flexibility of a human eye
      • Lenses fabricated from rigid materials
    • Not possible to make a ‘one system fits all’ system
    • Optical configurations must be designed for each application
      • Field of view
      • Resolution required
      • Physical constraints
    • Processing hardware has to be adapted to the optical configuration
  • If we design an ASIC, it will only work for one application!!
stereo correspondence
Stereo Correspondence

Can you find all the matching points in these two images?

“Of course! It’s easy!”

The best computer matching algorithms get 5% or more of the points completely wrong!

…and take a long time to do it!They’re not candidates for real time systems!!

stereo correspondence27
Stereo Correspondence
  • High performance matching algorithms are global in nature
    • Optimize over large image regions using energy minimization schemes
    • Global algorithms are inherently slow
      • Iterate many times over small regions to find optimal solutions
correspondence algorithms
Correspondence Algorithms
  • Good matching performance, global, low speed
    • Graph-cut, belief-propagation, …
  • High speed, simple, local, high parallelism, lowest performance
    • Correlation
  • High speed, moderate complexity, parallel, medium performance

Dynamic programming algorithms

stereo configuration

Points along these lineshave the same disparity

Stereo Configuration
  • Canonical configuration – Two cameras with parallel optical axes
  • Rays are drawn through each pixel in the image
  • Ray intersections represent points imaged onto the centre of each pixel

Depthresolution

  • but
  • To obtain depth information, a point must be seen by both cameras, ie it must be in the Common Field of View
stereo camera configuration
Stereo Camera Configuration
  • Now, consider an object of extent, a
  • To be completely measured, it must lie in the Common Field of View
  • but
  • place it as close to the camera as you can so that you can obtain the best accuracy, say at D
  • Now increase b to increase the accuracy at D
  • But you must increase D so that the object stays within the CFoV!
  • Detailed analysis leads to an optimum value ofb  a

a

D

b

a

increasing the baseline
Increasing the baseline

Increasing the baseline

decreases performance!!

% good matches

Images: ‘corridor’ set (ray-traced)

Matching algorithms: P2P, SAD

Baseline, b

increasing the baseline33
Increasing the baseline

Examine the distribution of errors

Increasing the baseline

decreases performance!!

Standard Deviation

Images: ‘corridor’ set (ray-traced)

Matching algorithms: P2P, SAD

Baseline, b

increased baseline decreased performance
Increased Baseline  Decreased Performance
  • Statistical
    • Higher disparity range
      • increased probability of matching incorrectly - you’ve simply got more choices!
  • Perspective
    • Scene objects are not fronto-planar
    • Angled to camera axes
      • subtend different numbers of pixels in L and R images
  • Scattering
    • Perfect scattering (Lambertian) surface assumption
    • OK at small angular differences
      • increasing failure at higher angles
  • Occlusions
    • Number of hidden regions increases as angular difference increases
      • increasing number of ‘monocular’ points for which there is no 3D information!
evolution
Evolution
  • Human eyes ‘verge’ on an object to estimate its distance, ie the eyes fix on the object in the field of view

Configuration commonly

used in stereo systems

Configuration discovered

by evolution millions of years ago

Note immediately that the CFoV is much larger!

look at the optical configuration
Look at the optical configuration!
  • If we increase f, then Dmin returns to the critical value!

Original f

Increase f

depth accuracy verging axes increased f
Depth Accuracy - Verging axes, increased f

Now the depth accuracy

has increased dramatically!

Note that at large f,

the CFoV does not extend

very far!

summary real time stereo
Summary: Real time stereo
  • General data acquisition is:
    • Non contact
      • Adaptable to many environments
    • Passive
      • Not susceptible to interference from other sensors
    • Rapid
      • Acquires complete scenes in each shot
    • Imaging technology is well established
      • Cost effective, robust, reliable
  • 3D data enhances recognition
    • Full capabilities of 2D imaging system
    • Depth data
  • With hardware acceleration
    • 3D scene views available for
      • ControlMonitoring
    • in real time
    • Rapid response  rapid throughput

Host computer is free to process complex control algorithms

Intelligent Vision Processing

Systems which can mimic human vision system capabilities!

system architecture
System Architecture

FPGA

L Camera

SerialInterface

Firewire/GigE/CameraLink

Line BuffersDistortion Removal

Image Alignment

RCamera

PC

Host

Higher orderInterpretation

Control

Signals

CorrectedImages

Stereo

Matching

Disparity

Depth

DepthMap

distortion removal
Distortion removal
  • Image of a rectangular grid from camera with simple zoom lens
  • Lines should be straight!
  • Store displacements of actual image from ideal points in LUT
  • Removal algorithm
    • For each ideal pixel position
      • Get displacement to real image
      • Calculate intensity of ideal pixel (bilinear interpolation)
distortion removal43
Distortion Removal
  • Fundamental Idea
    • Calculation of undistorted pixel position
      • Simple but slow
      • Not suitable for real time

but

      • It’s the same for every image!
      • So, calculate once!
    • Create a look up table containing ideal  actual displacements for each pixel

ud = uud(1+k2+k4+..)r2

r2 = (uud+vud)2

distortion removal44
Distortion Removal
  • Creating the LUT
    • One entry (dx,dy) per pixel
    • For a 1 Mpixel image needs 8 Mpixels!
      • Each entry is a float – (dx,dy) requires 8 bytes
    • However, distortion is a smooth curve
    • Store one entry per n pixels
      • Trials show that n=64 is OK for severely distorted image
      • LUT row contains 210 / 2 6 = 24 = 16 entries
      • Total LUT is 256 entries
    • Displacement for pixel j,k
      • dujk = (j mod 64) * duj/64,k/64
      • duj/64,k/64 is stored in LUT
    • Simple, fast circuit

Since the algorithm runs along scan lines,this multiplication is done by repeated addition

alignment correction
Alignment correction
  • In general, cameras will not be perfectly aligned in canonical configuration
  • Also, may be using verging axes to improve depth resolution
  • Calculate locations of epipolar lines once!
  • Add displacements to LUT for distortion!
real time 3d data acquisition
Real time 3D data acquisition
  • Real time stereo vision
    • Implemented Gimel’farb’s Symmetric Dynamic Programming Stereo in FPGA hardware
    • Real time precise stereo vision
    • Faster, smaller hardware circuit
    • Real time 3D maps
      • 1% depth accuracy with 2 scan line latency at 25 frames/se

System block diagram: lens distortion removal,misalignment correction and depth calculator

Output is stream of depth values: a 3D movie!

real time 3d data acquisition47
Real time 3D data acquisition
  • Possible Applications
    • Collision avoidance for robots
    • Recognition via 3D models
      • Fast model acquisition
        • Imaging technologynot scanning!
      • Recognition of humans without markers
      • Tracking objects
        • Recognizing orientation, alignment
    • Process monitoring

eg Resin flow in flexible (‘bag’) moulds

    • Motion capture – robot training

System block diagram: lens distortion removal,misalignment correction and depth calculator

Output is stream of depth values: a 3D movie!

fpga stereo system
FPGA Stereo System

Parallel Host Interface

FirewireCables

FPGA

AlteraStratix

FirewirePhysical

Layer ASIC

FirewireLink

Layer ASIC

FPGA

Prog

Cable

summary50
Summary
  • Challenges of Artificial Vision Systems
    • Real-time Image processing requires compute power!
    • Correspondence (Matching)
    • Depth accuracy
  • Evolution Lessons
    • Emulate parallel processing capability of humanbrain
    • Use verging optics
summary51
Summary
  • Our system
    • FPGA ‘front end’ processor
      • Remove distortion
      • Correct camera misalignment
      • Stereo matching
        • Using dynamic programming
    • Latency
      • Several scan lines (1 millisecond)
        • Depends on lens distortion and camera alignment
      • Host does not have to wait for a whole image!
    • Depth (distance) maps in real-time
      • 3D vision!
    • Frees host processor for image interpretation
      • Use both technologies (FPGA, conventional CPU) where they perform best!
ongoing projects
Ongoing Projects
  • Face Recognition
    • Development of Face Models
    • Animation
  • Automated Driving
    • With Daimler-Benz
  • Stereo Algorithms
    • Improved correspondence algorithms
  • High Quality Rendering
    • Movie special effects – eg “The Lord of the Rings”
    • Using reconfigurable hardware (FPGA)
stereo matching
Stereo matching
  • Automated stereo systems find matching regions in the two images
    • The separation of the matching regions is the disparity from which depth is calculated
  • Matching algorithms generally search over a range of possible disparities
    • Looking for the best ‘match’ in the two images

Stereo Correspondence is a classical challenge for AI systems

Our brains match regions in images without effort ..

but computers struggle to match as well!

stereo photogrammetry

Epipolar constraint

Align images so that matches must appear in the same scan line in L & R images

Key task – Correspondence

Locate matching regions in both images

Stereo Photogrammetry

Pairs of images giving different views of the scene

can be used to compute a depth (disparity) map

detail system architecture

Pixel Buffers

DetailSystem Architecture

Pixel AddressGeneratorRemoves distortionand misalignment

Predecessormatrix

(dynamic programming)

n DisparityCalculatorsOne for each possibledisparity value

Stream of disparity values