OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...
Image segmentation ajal
1. SEGMENTATION OF
FOREGROUND – BACKGROUND
FROM NATURAL IMAGES
B Y
AJAL.A.J
ASSISTANT PROFESSOR
UNIVERSAL ENGINEERING COLLEGE
2. OUTLINE
Introduction
Types of segmentation algorithms
Evaluations of RGB Color space
SEGMENTATION
EXPERIMENTAL RESULTS
Summary
Appendix
3. ABSTRACT
This paper presents a part of a more challenging research
project aimed at developing a computer vision system for a
robot capable of identifying all objects from known natural
backgrounds such as forest, sky, ocean, under-water scenes
and etc.
Segmentation is an import issue in the field of machine vision
for detection and recognition of objects.
The success of segmentation is solely depends on the
separation of foreground objects from background objects.
We present a simple framework to extract the foreground
objects from the known natural backgrounds in still and moving
images using pixel based color segmentation in RGB space.
4. What is an Image?
2D array of pixels
Binary image (bitmap)
Pixels are bits
Grayscale image
Pixels are scalars
Typically 8 bits (0..255)
Color images
Pixels are vectors
Order can vary: RGB,
BGR
Sometimes includes
Alpha
5. What is an Image?
2D array of pixels
Binary image (bitmap)
Pixels are bits
Grayscale image
Pixels are scalars
Typically 8 bits (0..255)
Color images
Pixels are vectors
Order can vary: RGB,
BGR
Sometimes includes
Alpha
6. What is an Image?
2D array of pixels
Binary image (bitmap)
Pixels are bits
Grayscale image
Pixels are scalars
Typically 8 bits (0..255)
Color images
Pixels are vectors
Order can vary: RGB,
BGR
Sometimes includes
Alpha
7. What is an Image?
2D array of pixels
Binary image (bitmap)
Pixels are bits
Grayscale image
Pixels are scalars
Typically 8 bits (0..255)
Color images
Pixels are vectors
Order can vary: RGB,
BGR
Sometimes includes
Alpha
8. What is an Image?
2D array of pixels
Binary image (bitmap)
Pixels are bits
Grayscale image
Pixels are scalars
Typically 8 bits (0..255)
Color images
Pixels are vectors
Order can vary: RGB,
BGR
Sometimes includes
Alpha
9. HSV VS RGB.
In day to day practice, we'll most likely use
two models:
HSV and RGB.
HSV stands for
Hue,
Saturation, and
Value,
and it uses these three concepts to describe a color.
RGB the three colors that make up an image on a monitor.
11. Color segmentation
In the problem of segmentation, the goal is to separate spatial regions of
an image on the basis of similarity within each region and distinction
between different regions.
Approaches to color-based segmentation range from empirical evaluation
of various color spaces, to clustering in feature space , to physics-based
modeling
The essential difference between color segmentation and color recognition
is that the former uses color to separate objects without a priori
knowledge about specific surfaces; the latter attempts to recognize colors
of known color characteristics
17. SEGMENTATION
Partitioning images into meaningful
pieces, e.g. delineating regions of
anatomical interest.
Edge based – find boundaries between
regions
Pixel Classification – metrics classify regions
Region based – similarity of pixels within a
segment
18. minimum cut
“allegiance” = cost of assigning two nodes to different
layers (foreground versus background)
foreground
node
background
node
pixel nodes
allegiance to
foreground
allegiance to
background
pixel-to-pixel
allegiance
19. minimum cut
“allegiance” = cost of assigning two nodes to different
layers (foreground versus background)
foreground
node
background
node
pixel nodes
allegiance to
foreground
allegiance to
background
pixel-to-pixel
allegiance
20. Normalized Cuts
• Graph partitioning technique
• Bi-partitions an edge-weighted graph in an optimal sense
• Normalized cut (Ncut) is the optimizing criterion
i j
wij
Edge weight => Similarity between i and j
A B
Minimize Ncut(A,B)
Nodes
• Image segmentation
• Each pixel is a node
• Edge weight is similarity between pixels
• Similarity based on color, texture and contour cues
21. 21
Unknown clusters and centers
Maximization step:
Find the center (mean)
of each class
Start with random
model parameters
Expectation step:
Classify each vector
to the closest center
23. Segmentation fault
A segmentation fault (often shortened to
segfault) or access violation is a particular
error condition that can occur during the
operation of computer software.
A segmentation fault occurs when a program attempts to access
a memory location that it is not allowed to access, or attempts to
access a memory location in a way that is not allowed (for
example, attempting to write to a read-only location, or to
overwrite part of the operating system).
25. Thresholding
Suppose that an image, f(x,y), is composed of
light objects on a dark background, and the
following figure is the histogram of the image.
Then, the objects can be extracted by
comparing pixel values with a threshold T.
25
26. Region Growing
1. Define seed point
2. Add n-neighbors to list L
3. Get and remove top of L
4. Test n-neighbors p
if p not treated
if P(p,R)=True then p→L
and add p to region
else p marked boundary
5. Go to 2 until L is empty
Two Regions R and ¬ R
SeedpointsSeedpoints ElementinElementinL
BorderelementBorderelementRegionelementRegionelement
27. Our approach: The Algorithm
The left and right images areThe left and right images are
segmented and each areasegmented and each area
identifies a node of a graphidentifies a node of a graph
A bipartite graph matchingA bipartite graph matching
between the two graphs isbetween the two graphs is
computed in order to match eachcomputed in order to match each
area of the left image with onlyarea of the left image with only
one area of the right imageone area of the right image
This process yields a list ofThis process yields a list of
reliably matched areas and a listreliably matched areas and a list
of so-called don’t care areas.of so-called don’t care areas.
The Outputs of the algorithmThe Outputs of the algorithm
are the disparity map and theare the disparity map and the
performance mapperformance map
28. GPCA
Generalized Principal Component Analysis (GPCA)
method for.
modeling and segmenting mixed data using a
collection of subspaces
done by introducing certain algebraic models into
data clustering.
Unique property (applied to images) is that it
decomposes images into regions with
fundamentally different characteristics and
derives an optimal PCA-based transformation for
each region.
29. Computing a principal component
analysis
To compute a principal
component analysis in SPSS,
select the Data Reduction |
Factor… command from the
Analyze menu.
31. Intelligent Scissors
Fully automatic segmentation is an unsolved
problem due to wide variety of images.
Intelligent Scissors is a semi-automatic
general purpose segmentation tool.
The efficient and accurate boundary
extraction, which requires minimal user input
with a mouse, is obtained.
The underlying mechanism for the Intelligent
Scissors is the “live-wire” path selection tool.
35. Floor plan of the
prototype chip
Layout of the
encoder module
36. Pros & Cons
Very useful for rapid prototyping
Strongly growing community and code base
Problems:
Very complex
Overhead -> higher run-times
Still under development
37. Summary / Closing
Thoughts
Segmentation is the essential but critical problem in
the field of machine vision. At a stretch, robotics can
not be done with a complete knowledge about
foreground and background objects.
We have proposed pixel based color segmentation
approach to segment the known backgrounds such
as forest, sky, ocean, underwater scenes and etc.
which will be of unique color generally and the results
obtained were satisfactory.
This color segmentation process will overcome the
main problems with change of pose and occlusion
and overcomes the limitation occurs in the motion
analysis and background subtraction methods.
38. Conclusions
Translation (visual to semantic) model for object recognition
Identify and evaluate low-level vision processes for recognition
Feature evaluation
Color and texture are the most important in that order
Shape needs better segmentation methods
Segmentation evaluation
Performance depends on # regions for annotation
Mean Shift and modified NCuts do better than original NCuts for # regions < 6
Color constancy evaluation
Training with illumination helps
Color constancy processing helps (scale-by-max better than gray-world)
39. Reference Reading
Digital Image Processing
Gonzalez & Woods,
Addison-Wesley 2002
Computer Vision
Shapiro & Stockman,
Prentice-Hall 2001
Computer Vision: A Modern Approach
Forsyth & Ponce,
Prentice-Hall 2002
Introductory Techniques for 3D Computer Vision
Trucco & Verri,
Prentice-Hall 1998
40. REFERENCES :
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