Computer Vision

1
Computer Vision Image Analysis (EENG 5640)
2
Introduction to Computer Vision
Image Processing System
Image
Image
Computer Vision/ Image Analysis/ Image
Understanding System
Image/ Scene Description
Image
Pattern Classification Label
Pattern Recognition System
Pattern Vector (with Image measurements as
components in the current application)
Computer Vision generally involves pattern
recognition
3
Typical Computer Vision Applications

• Medical Imaging
• Automated Manufacturing (some experts use
  Machine vision as the term to describe Computer
  Vision for Industrial applications others use it
  as synonym for computer vision)
• Remote Sensing
• Character Recognition
• Robotics

4
Binary Image Analysis

• Grey scale to Binary transformation (Otsus
  method)
• Counting holes
• Counting objects
• Connected Component Labeling Algorithms
• Recursive Algorithm
• Two Pass Row by Row Labeling Algorithm

5
Two-Pass Algorithm Illustrative Example

1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1
1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1
1 1 1 1
1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1


1 1 1 1 1 2 2 2 2 2
1 1 1 1 1 2 2 2 2 2
1 1 2 2
1 1 1 1 1 1 2 2 2 2 2 2
1 1 2 2
3 3 3 1 1 1 4 4 4 2 2 2
3 3 4 4
3 3 4 4
3 3 3 3 3 3 3 3 3 3 3
3 3 3 3 3 3 3 3 3 3 3

1
2
3
4
Label
1 2 3 4 --
0 1 1 2
Parent
6
Binary Image analysis (Contd.)

• Morphological Processing
• Dilation, Erosion, Opening and Closing Operations
• Example to Illustrate the effects of the
  operations
• Region Properties
• Area
• Perimeter
• Circularity

7
Medical Application of Morphology
8
Industrial Application of Morphology
9
Grey Level Image Processing

• Image Enhancement Methods
• Histogram Equalization and Contrast Stretching
• Mitigation of Noise Effects
• Image Smoothing
• Median Filtering
• Frequency Domain Operations (Low Pass Filtering)
• Image Sharpening and Edge Detection
• High Pass Filtering
• Differencing Masks (Prewitt, Sobel, Roberts,
  Marr-Hildreth operators)
• Canny Edge Detection and Linking

10
Histogram Equalization- Original Image
11
Histogram Equalization- Equalized Image
12
Low Contrast Image
13
Contrast Stretching (Linear Interpolation between
79-136)
14
Histogram Equalization
15
Color Fundamentals
0, 1, 1 Cyan
0, 1, 0 Green
1, 1, 0 yellow
0, 0, 1 Blue
1, 0, 1 Magenta
1, 1, 1 White
1, 1, 1 White
1, 0, 0 Red
0, 1, 1 Cyan
0, 0, 0 Black
0, 1, 0 Green
1, 0, 0 Red
1, 0, 1 Magenta
0, 0, 1 Blue
1, 1, 0 yellow
16
RGB and HSI (HSV) Systems
17
RGB-HSI Convesion
18
RGB to HSI Conversion- Final Formulae
19
HIS-RGB Conversion
Method is given. You need to reason out why? Or
explore web for answer.
20
YIQ and YUV Systems for TVs, etc.
YIQ system is used in TV Signals. Its components
are Luminance Y 0.30R 0.59G
0.11B R-Cyan I 0.60R -
0.28G - 0.32B Magenta-Green Q 0.21R -
0.52G 0.31B In some digital products and
JPEG/MPEG Compression algorithms, YUV System as
follows is used Y
0.30R 0.59G 0.11B
U 0.493 (B Y)
V 0.877(R Y) Advantage Luminance
and Chromaticity components can be coded with
different number of bits.
21
Optical Illusion - I
22
Optical Illusion - II
23
Optical Illusion - III
24
Texture

• Pattern caused by a regular spatial arrangement
  of pixel colors or intensities.
• Two approaches
• Structural or Syntactic (usually used in case of
  synthetic images by defining a grammar on
  texels).
• Statistical or quantitative (more useful in
  natural texture analysis can be used to identify
  texture primitives (texels) in the image.

25
Quantitative Texture Measures

• Edge related
• Edginess (proportion of strong edges in a small
  window around pixels.
• Edge direction histograms (the pattern vector
  constituted by the proportion of the edgels in
  the horizontal, vertical, and other quantized
  directions among the total pixels in a chosen
  window around a pixel (I,j) under consideration
• Co-occurrence matrix based

26
Co-occurrence matrix based Measures

• Construction of Co-occurrence matrix Cd i,j
  where d is the displacement of j from i (e.g.
  (0,1), (1, 1), etc.
• Normalized and symmetric co-occurrence matrices
  Nd i,j and Sd i,j.
• Zucker and Terzopouloss Chi-square metric to
  choose the best d (i.e. d with most structure).
• Numeric measures from Nd i,j

27
Choice of the Best Co-occurrence Matrix and
Computation of Features
28
Laws Texture Energy Measures

• Simple because masks are used
• 2-D masks are created using 1-D masks
• L5 (Level) 1 4 6 4 1
• E5 (Edge) -1 -2 0 2 1
• S5 (Spot) -1 0 2 0 -1
• W5 (Wave) -1 2 0 -2 1 (not in the
  text!)
• R5 (Ripple) 1 4 6 -4 1
• (e.g. 5x5 matrix of L5E5 mask is obtained by
  multiplying transpose of L5 by E5).

29
Laws Algorithm for Texture Energy Pattern
Vector Construction
30
Laws Texture Segmentation Results- I
31
Laws Texture Segmentation Results- II
32
Laws Texture Segmentation Results- III
33
Gabor Filter Based Texture Analysis
Gabor Filter is mathematically represented by
(refer Wikipedia)
Where
and
?
? Orientation of the normal to parallel stripes
?
? Wavelength or inverse of the frequency of the
cosine function
g
s
? Spatial aspect ratio ? Sigma of the
Gaussian function
?
? Phase offset of the cosine function
34
Image Segmentation
Image Segmentation
Contour-Based Methods (e.g. Canny Edge Detection
and Linking)
Region-Based Methods
Region Growing (e.g. Haralick and Shapiro Method)
Partitioning/Clustering (e.g. K-Means Clustering,
Isodata clustering, Ohlander et al.s recursive
histogram-based technique)
35
Clustering Algorithms

• Clustering (partitioning) of pixels in the
  pattern space
• Each pixel is represented by a pattern vector
  of properties. For example, in case of a colored
  image, we could have
  
• could be of any dimensionality (even 1,
  i.e. could be a scalar as in case of a grey
  level image).
• Depending upon the problem, may include
  other measurements on texture, shading, etc. that
  constitute additional dimensional components of
  the pattern vector.
• Note i and j denote pixel row columns.

36
K-Means Clustering algorithm
37
Isodata Clustering Algorithm
T
38
ISODATA Clustering Problem
To which cluster does X belong to?
If split threshold TS 3.0 and Merge Threshold
TM 1.0, what will be the new cluster
configuration? Get new cluster means in case of
a Split.
39
Image Databases- Content-Based Image Retrieval
Any Problem with Traditional Text (in Caption)
Based Retrieval? Typical SQL (Structured Query
Language) Query SELECT FROM IMAGEDB WHERE
CATEGORY GEMS AND SOURCE SMITHSONIAN
AND (KEYWORD AMETHYST OR KEYWORD
CRYSTAL OR KEYWORD PURPLE) This will
retrieve the gem collection of the Smithsonian
Institute from its IMAGEDB database restricting
its search based on the logical combination of
the keyword specified.
Looks like no problem here!
40
Limitations of the Key Word Based Retrieval

• Human coding of key words is expensive but still
  some keywords by which one likes to retrieve the
  image cannot be visualized and hence may be left
  out. Key words may sometimes retrieve unexpected
  images as well!
• What kind of images do you expect with the key
  word pigs?

41
Unexpected Retrieval- An Example
42
Content-Based Image Retrieval

• Uses Query-By-Example (QBE) Concept
• IBMs QBIC (Query By Image Content) is the first
  system
• In QBE systems, you specify an example plus some
  constraints
• Typical example images for specification-
• A digital Photograph
• User painted drawing
• A line-drawing sketch

43
Matching- Image Distance (Similarity Measures)

• 4 Major classes
• Color Similarity
• Texture Similarity
• Shape Similarity
• Object and relationship similarity

44
Color Similarity Measures

• QBIC lets the user choose up to 5 colors from the
  color table and specify their percentages
• Color histograms (K-bin) can be used
• Here h(I) and h(Q) are K-bin histograms of
  images I and Q, and A is (K x K) similarity
  matrix.

45
Color-Layout-Based Similarity

• Distances between corresponding grid squares of
  the database and example images are found and
  summed up.
• Each grid square spans over multiple pixels. Then
  how do you compare grid squares?
• Use Mean Color
• Use Mean and Standard Deviation
• Use Multi-bin Histogram

46
Texture-Based Similarity Measure

• Pick-and-Click distance
• Grid based texture similarity can be found by the
  same process as in the gridded color case

47
Shape-Based Similarity Measures

• Histogram approach is difficult to apply
  particularly when you want scale and rotation
  invariance.
• Boundary Matching
• Granlunds Fourier Descriptors for Translation,
  Scale, starting point (for boundary tracing),
  and rotation invariant matching.

48
Boundary (Sketch) Matching

• Obtain a normalized image- reduce the original
  image to a fixed size, e.g., (64x64) median
  filter
• 2 stage edge detection-global and local
  thresholds.
• Perform linking and thinning.
• Find Correlation between line drawing (L)s grid
  square and various shifts (n) of the DB image As
  grid square sum up best correlations.

49
Line Sketch of a Horse
50
Retrieved Images of Paintings
51
Granlunds Fourier Descriptors

• Let be
  the points on the boundary of the query shape.
• The k-th discrete Fourier coefficient is given
  by
• Leaving out and , we can compute
  translation, rotation, starting-point and scale
  invariant shape descriptors ) as follows

52
Invariant Properties of Fourier Descriptors

• Translation
• Rotation
  . takes care of the problem because
• Scale By
  using for scaling, we are eliminating c.
• Starting point- Once again . operation helps!
  
  

53
Relational Similarity

• Spatial Relationship- relational graphs
  indicating inter-object relationships can be
  constructed. Once objects are identified,
  relationships can be matched, by graph matching
  techniques.
• Abstract Relationship- Happy face it involves
  separation and identification of a face region
  first, and then checking whether it is a happy or
  sad face.

54
Matching in 2D

• Transformation- Mapping from one coordinate space
  to another.
• linear or nonlinear (called warps)
• one to one correspondence between points if
  linear
• Invertible or non-invertible
• Image Registration- Process of establishing point
  by point correspondence between two images of a
  scene.

55
Affine Transformation (Mapping)

• Wikipedia Definition Affine (Latin affinis ?
  Connected with) mapping between two vector
  (affine) spaces is a linear transformation
  followed by translation.
• It preserves
• Collinearity of points
• Ratios of distances along a line

56
Image Operations Represented by Affine
Transformation

• Can we write the affine transformation
• Y A.X t (Can we absorb t into A)?
• For scaling and rotation, yes.
• For translation, does not seem to be possible.
  What is the way out?

57
Homogeneous Coordinates

• Introduced by August Ferdinand Mobius
  (1790-1868), a German mathematician and
  theoretical astronomer.
• (x, y) ? (w.x, w.y, w) (2.x, 2.y, 2) (3.x,
  3.y, 3)
• (x, y, z) ? (w.x, w.y, w.z, w) Same concept an
  be extended to any n-dimensional space.
• You can represent a point at infinity (how?)

58
Usage of Homogeneous Coordinates

• The rotation, scaling, and translation can be
  modeled as matrix multiplication operations as
  follows
• If control (easily distinguishable) points of two
  images are identified, and registered with an
  affine mapping, it is easy to identify the
  presence of the same object(s) in both-
  Recognition by Alignment.

59
Shears and Reflections



(1, 1ex)
(1, 1)
(1ey, 1)
(0, 1)
(0, 1)
(1, 1)
(ey, 1)
y?
y?
(1, ex)
(0, 0)
(1, 0)
x?
(1, 0)
(0, 0)
x?
Horizontal Shear
Vertical Shear
Reflections about x-axis (x, y) ? (x, -y)
Reflections about y-axis (x, y) ? (-x, y) You
can express these also as an affine
transformations.
60
General Affine Transformation

• You may consolidate all the previous affine
  transformations (translation, rotation, scale,
  shear, and reflection) into the general affine
  transformation as follows

61
Best 2D Transformation with Least-Squares Fitting

• Let (xj, yj), j 1, n the control points in an
  image, and (xj, yj) are the corresponding
  points in the transformed image. Then the
  least-squares fit method seeks to minimize the
  error
• Setting the 6 partial derivatives of the form
  corresponding to the 6 translation parameters to
  zero, we get 6 equations for 6 unknowns.

62
2D-Object Recognition via Affine Mapping- Local
Feature Focus Method
Local-Feature-Focus-Method For each pair of
model and image features Find the
maximal subset of matching neighboring
features Find Best T If enough
features align, confirm the presence of
the model object
F3
F2
G2
G1
F1
F4
G3
G4
Model F
G8
G5
E4
G6
G7
E1
E2
E3
Image
Model E
63
2D-Object Recognition via Affine Mapping- Pose
Clustering Method
Pose-Clustering-Method (P, L) // P is the set
of image features // L is the set of stored
model features For each pair (Pi, Pj) of image
features For each pair (Lm, Ln) of model
features of the same type
Compute the affine (RST) parameters a
Each a will be a point in the parameter space
Examine the parameter space for large
cluster modes return all ak s corresponding to
dominant modes
L Junction
Y Junction
T Junction
Arrow Junction
X Junction
Some Typical Model Features
64
2D-Object Recognition via Affine Mapping-
Geometric Hashing
e10
Te01

• Useful when model database (DB) is very large
  and an object in the image is known to be an
  affine transform of one of the DB models

e01
x
Tx
e00
Te10

• If e00, e01, and e10 are any three non-collinear
  feature points from the model feature point set
  M, any point x e M can be represented as follows
  using the affine basis set (coordinate set)
  constructed from these 3 points
• x x(e10 e00) h(e01 e00) e00
• We use the property that under an affine
  transform, the same relation holds
• Tx x(Te10 Te00) h(Te01 Te00) Te00

Te00
65
Geometric Hashing Method (Contd.)
GH-Offline-Preprocessing (D, H) //D- Database
Model Set // H- Initially empty hash table
for each model M in D Extract feature
set FM For each non-collinear triplet E
of FM For each other point x of FM
Calculate (x, h) for x with
respect to E Store (M, E) in the
table H at index (x, h)
66
Geometric Hashing Method (Contd.)
GH-Online-Recognition (D, H) //- Database
Model Set H- Hash table constructed in the
offline processing for each possible (M, E)
tuple in the database set Bin-Count
(M, E) 0 Extract image feature set
FI for each non-collinear triplet E of FI and
for each other point x of IM
Calculate (x, h) for x with respect to E
Retrieve (M, E) pairs in the table H at
index (x, h) Increment the
Bin-Count of those (M, E) pairs Return
(M, E) values with highest Bin-Count values.
67
Practical Problems in Geometric Hashing

• Errors in feature point coordinates
• Missing and extra feature points
• Occlusions and multiple objects
• Unstable bases
• Weird affine transforms on subsets points and
  consequent hypotheses for presence of
  hallucinated objects (This problem is present in
  pose clustering and focus feature methods also).

(a) Image Points
(b) Hallucinated Object
68
General Framework for 2D-Object Recognition via
Relational Matching

• Consistent Labeling Problem is a 5-tuple
• (P, L, RP, RL, f)
• P- Object Parts (found in the image)
• L- Object Labels (names of stored model features)
• RP Set of Relationships between Parts
• RL Set of Constraint Relationships between
  Labels
• f is a mapping such that
• if (pi, pj) e RP, then (f(pi), f(pj)) e RL

69
Brute Force Method for Consistent Labeling -
Interpretation Tree Search
Bool Interpretation-Tree-search(P, L, RP, RL,
f) p first(P) for each I in L
f f U (p, I)//add part-label to
interpretation OK true for each
N-tuple (p1, , pN) in RP containing p
if then OK
false break if OK then
P P p if is-empty(P) then
output(f) else Interpretation-Tree-Searc
h(P, L, RP, RL, f)
C5
C6
C2
C1
H3
H4
H2
H1
C3
C4
C7
C8
(a) Labels
P1
P2
P5
P4
Nil
P3
P4
(b) Image Parts
P1 H1
P1 C1
(c) Interpretation Tree
70
Discrete Relaxation Labeling
Discrete Relaxation Labeling(P, S, R) // Pi , i
1, , D, is the set P of the detected image
features // S is the set of sets S(Pi ), i 1,
, D, of initially compatible labels for Pis
// R set of relationships over which
compatibility is determined repeat
for each (Pi, S(Pi)) for
each label Lk e S(Pi) for each relation R(Pi,
Pj) over image parts If there exists Lm e
S(Pj) with R(Lk, Lm) in model then keep Lk in
S(Pi) else delete Lk from S(Pi)
until no change in any S(Pi) return (S)
71
Continuous (Probabilistic) Relaxation

Loop on and until labels of all
parts stabilize and become unique
compatibility values
72
Extraction of 3D Information from 2D Images-
Shape from X Techniques

• Direct 3D perception- Range Imaging (Costly)
• How do human perceive depth/3D shape?
• Stereo
• Shading
• Monocular cues, e.g., Relative depth information
  from occlusions of the background objects by
  foreground objects, perspective view with
  farther objects appearing smaller with distance
• Shape from X (X binocular stereo/ photometric
  stereo/shading/texture/ boundary/motion

73
Binocular Stereo
P
x'
d
x
x/f OP/(f z) x/f (OP d) / (f
z) (x-x)/f d/(f z) z f . d /(x x)
- f
f
z
Depth can be inferred from the disparity
(x-x) Only problem remains to be solved is the
correspondence problem.
74
Surface Orientation from Reflectance Models
i angle of incidence e angle of emittance g
phase angle n, ng, and ns are unit vectors along
the surface normal, view direction, and source
direction, respectively.
n
ng
z
ns
y
i
g
e
x
For specular (smooth mirror-like) surfaces,
maximum amount of light is reflected in the
direction of what is called specular angle, and
it reduces in the directions away from this one.
An estimate of the cosine of the difference
between the specular and viewing angles is given
by C 2cos(i) cos(e) cos (g). For dusty/matte
(Lambertian) surfaces, reflectivity in any
direction is proportional to angle of incidence.
A general formula that includes both effects is
L(i, e, g) s Cn (1 s) cos (i) 0 lt s lt
1. Larger the n, sharper the peaking in the
specular direction.
75
Reflectance Map for Lambertian Surfaces
q
R r0 n.ns (1) The
spatially varying reflectance factor r0 is called
albedo. For a surface z f(x, y), let p
?z/?x and q ?z/?y
0.9
0.8
p
?z (?z/?x). ? x ? ? p. ? x
y
z
n
(?x, 0, p.?x )T l to (1, 0, p)T rx, say
0.7
(0, ?y, p.?y)T l to (0, 1, q)T ry, say
n ? rx x ry (-p, -q, 1)T ns ? (-ps, -qs, 1)
r Putting in (1), we get R(p, q)
?y
?x
x
76
Photometric Stereo
With 3 sources of light Rk(x, y) Ik(x, y)
ro(nk.n) k 1, , 3 I r0 N. n where I
I1(x, y), I2(x, y), I3(x, y) T and
q
R2(p, q) 0.75
R1(p, q) 0.9
ro N-1 I n 1/ r0 . N-1 I
Solution point
p
R3(p, q) 0.5
77
Shape from Shading
We know that (-p, -q, 1) is the vector in the
direction of surface normal and (-ps, -qs, 1) is
the corresponding vector for source direction,
the reflectance (i.e. image intensity) for a
Lambertian surface is given by
At each pixel site (k, l) we need to find the
best (pkl, qkl) pair that gives an Rkl matching
with the image intensity Ekl. In other words, we
minimize
This may not have unique solution. Hence, we used
in photometric stereo 3 images to resolve the
ambiguity. Here we use surface continuity for the
same
Overall we minimize
We get the final solution by setting
and
78
Shape from Shading- Ikeuchis Relaxation Approach
Continuing from the previous slide by setting
and , we get
and
Now, the equations for obtaining p and q
iteratively by Ikeuchis relaxation approach are

Mask to compute average p and q
Advantage Relaxation method can enforce the
boundary conditions and get good solutions.
Limitation In the current formulation,
occluding boundary with p and q at ? causes a
problem. How to solve this problem?
79
Photometric Stereo by Relaxation Approach
Same relaxations equations can be extended to the
photometric stereo problem involving n ( 2 or
more) images
Here and represent the intensity at
the pixel site in the image captured
with the th light source, and the
corresponding reflectance map, respectively.
Advantage Relaxation method can enforce the
boundary conditions and get good solutions.
Limitation In the current formulation,
occluding boundary with p and q at ? causes a
problem. How to solve this problem? See the
next slide!
80
Solution to the Problem of Infinite Gradients at
Occluding Boundary
Ikeuchi suggested to formulate the problem in
polar coordinates and solve! If P is a point on a
surface patch, and OP is a unit normal, the x, y,
and z components of this vector are given
by (sin ?. cos ?, sin ?. sin ?, cos ?) (1) We
can represent similarly the x, y, and z
components of the unit vector in the direction
the light source with polar coordinates (?s, ?s)
as (sin ?s. cos ?s, sin ?s. sin ?s, cos
?s) Since, for Lambertian surfaces, R ro. cos i
where i is the angle between the two directions,
we can rewrite R as the following function of ?
and ? R (?, ? ) ro. (sin ? sin ?s cos ? cos ?s
sin ? sin ?s sin ? sin ?s cos ? cos ?s )
ro. ( sin ? sin ?s cos(? - ?s ) cos ? cos ?s ).
At the occluding boundary ? ?/2 and ?
tan-1?y/?x. The same equations as before hold
with ? and ? replacing p and q. From ? and ? to p
and q may be done in the end using (1).
z
P
?
O
y
?
x
81
Needle Diagram (Display of Unit Normal Vectors in
the Image Space)
(a) Image of a resin Droplet on a flower of a
plant
(b) Needle diagram for the image in (a)
82
Depth Construction from the (p, q) tuples (Needle
Diagram)
Since p ?z/?x and q ?z/?y,
Because of imperfect (p, q) values, this integral
may not yield correct values. The values may be
sensitive to the path chosen for integration.
Integral around a close loop of pixels may not
vanish. Hence, better approach is to choose p and
q that minimizes

. From the calculus of variations, an
integral of the form
can be minimized by solving the Euler
equation .
For our problem, the Euler equation would yield
83
Z-map Construction Algorithm

• Convolve the p and q maps with horizontal and
  vertical Prewitt/Sobel masks to get px and qy
  maps and add them up to get s pxqy map.
• Start with a random configuration of z-values on
  the image grid. Or, preferably obtain a crude
  z-map by starting with z (0, 0) 0 and applying
  the integral for computing z from p and q values
  by visiting the cell sites in raster scan
  fashion.
• Convolve the z-image with horizontal
  Prewitt/Sobel mask to get ?z/?x -image. Convolve
  again the resultant image with same mask to get
  ?2z/?x2. Convolve again the z-image with vertical
  Prewitt/Sobel mask to get ?z/?y -image. Convolve
  again the resultant image with same mask to get
  ?2z/?y2. Add the two resultant images to get
  ?2z-image.
• For all (i, j), update zij by zij e.(?2zIj
  sij) where e is a small constant.
• If none of the updated zij s change
  significantly, stop.
• Otherwise, go to step 3.

84
Moving Objects- Optical Flow
Constraint line IxuIyvIt 0
Whether the observer or the scene objects are
moving, the relative motion gives a lot of
information about depth because the points closer
to the observer seem to be moving faster. Motion
stereo is the name of phenomenon (or body of
techniques) for depth perception based on motion
information. Brightness patterns in the image
move as the objects that give rise to them move.
Optical flow is the apparent motion of the
brightness pattern. Basic optical flow equation
Expanding the left hand side by Taylor series
and equating terms, we get Velocity in the
direction of brightness gradient is given by
We cant determine flow in the
iso- brightness (right angles) direction. This
is called aperture problem.
v
(Ix,Iy)
u
Ix ?I/?x Iy ?I/?y It ?I/?t
-
85
Optical Flow Estimation- Horn and Sjobergs
Relaxation Method
As before, we need to minimize conjointly two
constraints
It Computation
Frame t
Frame t
Frame t
Frame t1
Frame t1
Frame t1
Ix Computation
Iy Computation
86
Shape/Structure from Motion



Now, equating each component of , we get
If are the image point
corresponding to the 3-D object point P, we have
from perspective projection and
(assuming f 1, and Z gtgt f).
v (vx, wy, wz)T
z
P (X, Y, Z)T
Now, the optical flow components and
are
and Now, using (1),
we can get expressions for and in terms of
Z and 6 motion parameters.
O
y
x
87
Shape from Motion- Pure Translation Case
From the imaging geometry, the (x, y) coordinates
of the image points corresponding to the
3D-Object point (X, Y, Z) are given by
Now, if we ignore terms related to rotation from
the equations of u and v on the previous slide,
we get
In order to match the estimated (u, v) values
with computed values, we need to minimize the
function
88
Shape/Object Representation and Recognition
Objects/Shapes
2-D (Planar)
3-D
Representation of closed boundaries (Recognition
Sensitive to Noise and Occlusions) e.g., Fourier
Descriptors
Surface based representations(e.g. Coons surface
patches represented by 2-D polynomials,
generalized cylinders )
Volumetric/binary voxel representation (e.g. for
3-D medical image constructed from 2D-slices)
Noise and Occlusion-tolerant Representation of
parts and their interrelationships (e.g.,
Connectivity, adjacency). Parts could be regular
shaped objects such as circles, squares,
triangles recognizable by Hough transform, or
curve segments represented using polynomial forms
(B-Splines)
89
Reconstruction Imaging- Computer Aided Tomography
(CAT) Scans
g(xq, yq)
X-ray source