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Interaction through video

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VIPeR Toolkit. VIPeR Toolkit: Callbacks. Main() Camera. Video File. Callback1() Callback2 ... And on movies. Morphological Operations ... – PowerPoint PPT presentation

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Title: Interaction through video


1
Interaction through video
  • Gabriel J. Brostow

2
Video the BEST modality
  • As passive or active as needed
  • Simple directional localization
  • Line-of-site supports see/be seen paradigm
    (within visible spectrum)

According to a vision person.
3
Light
4
Photoreceptors
5
Light ? Color
6
Color ? Data
Link More CCD/CMOS info
7
Pixels are NOT squares
  • Pixel Aspect Ratio
  • Ratio of
  • Vert. samples per mm
  • to
  • Hor. samples per mm
  • Examples
  • 1
  • 0.91
  • Image Size
  • Samples in x
  • x
  • Samples in y
  • Examples
  • 35mm (36x24)
  • VGA (640 x 480)

8
Pixel Representation
  • Stored as buffer of values representing
    intensity
  • binary, gray, color
  • 0/1, 0-255, floating pt (0.01.0), log-scale

9
Wrapper vs. CODEC
  • Wrappers
  • tif, mov, qt, avi
  • CODECS
  • Sorenson, DV, Cinepak, MPEG II
  • CAUTION Lossy vs. Lossless

10
DV
  • 720 x 480
  • 24-bit
  • 29.97 fps
  • .9 pixel aspect ratio (not square!)
  • 44.1kHz stereo audio
  • 411 YUV

11
Code Development for Processing Video Streams
  • NOT reinventing the wheel

12
SDKs Galore!
IPL
VIPeR Toolkit
OpenCV
VisSDK
13
VIPeR Toolkit Callbacks
Main()
Camera
Callback1()
Video File
Callback2()
14
Image Analysis
  • Thresholds
  • Statistics
  • Pyramids
  • Morphology
  • Distance transform
  • Flood fill
  • Feature detection
  • Contours retrieving

15
Image Thresholding
  • Fixed threshold
  • Adaptive threshold

16
Image Thresholding Examples
Source picture
Fixed threshold
Adaptive threshold
17
Image Pyramids
  • Gaussian and Laplacian pyramids
  • Image segmentation by pyramids

18
Image Pyramids
  • Gaussian and Laplacian

19
Pyramid-based color segmentation
On still pictures
And on movies
20
Morphological Operations
  • Two basic morphology operations using structuring
    element
  • erosion
  • dilation
  • More complex morphology operations
  • opening
  • closing
  • morphological gradient
  • top hat
  • black hat

21
Morphological Operations Examples
  • Morphology - applying Min-Max. Filters and its
    combinations

Dilatation I?B
Opening IoB (I?B)?B
Erosion I?B
Image I
Closing IB (I?B)?B
TopHat(I) I - (I?B)
BlackHat(I) (I?B) - I
Grad(I) (I?B)-(I?B)
22
Distance Transform
  • Calculate the distance for all non-feature points
    to the closest feature point
  • Two-pass algorithm, 3x3 and 5x5 masks, various
    metrics predefined

23
Feature Detection
  • Fixed filters (Sobel operator, Laplacian)
  • Optimal filter kernels with floating point
    coefficients (first, second derivatives,
    Laplacian)
  • Special feature detection (corners)
  • Canny operator
  • Hough transform (find lines and line segments)
  • Gradient runs

24
Canny Edge Detector
25
Contour Retrieving
  • The contour representation
  • Chain code (Freeman code)
  • Polygonal representation

Initial Point Chain code for the curve
34445670007654443
Contour representation
26
Hierarchical representation of contours
Image Boundary
(W1)
(W2)
(W3)
(B2)
(B3)
(B4)
(W5)
(W6)
27
Contours Examples
Source Picture (300x600 180000 pts total)
Retrieved Contours (lt1800 pts total)
After Approximation (lt180 pts total)
And it is rather fast 70 FPS for 640x480 on
complex scenes
28
Optical Flow
  • Block matching technique
  • Horn Schunck technique
  • Lucas Kanade technique
  • Pyramidal LK algorithm
  • 6DOF (6 degree of freedom) algorithm

Optical flow equations
29
Pyramidal Implementation of the optical flow
algorithm
Image Pyramid Representation
Iterative Lucas Kanade Scheme
J image
I image
Location of point u on image uLu/2L Spatial
gradient matrix Standard Lucas Kanade scheme
for optical flow computation at level L dL Guess
for next pyramid level L 1 Finally,
Generic Image
(L-1)-th Level
Image pyramid building
L-th Level
Optical flow computation
30
Camera Calibration
  • Define intrinsic and extrinsic camera parameters.
  • Define Distortion parameters

31
Camera Calibration
Now, camera calibration can be done by holding
checkerboard in front of the camera for a few
seconds.
And after that youll get
3D view of etalon
Un-distorted image
32
Further Tracking Recognition
  • Kalman Filtering
  • Condensation (Factored Sampling)
  • Hidden Markov Models

33
Video has the answers
  • Person Identification
  • Who?
  • Faces
  • Gait / limb lengths
  • How are you?
  • Activity Recognition
  • Need help?
  • Cheating at Blackjack?
  • Asleep at the wheel?
  • Long-term Inference
  • Depressed?
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