1. ENT 273 Object Recognition and Feature Detection Hema C.R.
Machine Vision
ENT 273
Object Recognition
and
Feature Detection
Hema C.R.
Lecture 7

2. Road Map Feature Extraction Contour Chain codes Object Recognition
Object Representation
Feature Detection
Hough Transform
Fourier Descriptors
Hema ENT 273 Lecture 7

3. Contour Represented as ordered list of edges or a curve
Criteria for good contour
Efficiency: simple and compact representation
Accuracy: accurately fit image features
Effectiveness: suitable for operations to be performed at a later stage
Hema ENT 273 Lecture 7

4. Definitions Edge list Contour Boundary
Ordered set of edge points or fragments
Contour
Edge list or a curve that is used to represent the edge list
Boundary
Closed contour that surrounds a region
Note: The term edge generally refers to edge points
Hema ENT 273 Lecture 7

5. Object Recognition
Object recognition systems find objects in the real world from an image of the world.
Object recognition can be defined as a labeling problem based on models of known objects.
Hema ENT 273 Lecture 7

6. OBJECT RECOGNITION
The next step in Robot Vision is the Object Recognition. This problem is accomplished using the extracted feature information.
The object recognition algorithm is to be powerful and fast so that the required object is uniquely recognized. There are mainly two methods
Template matching technique
A template is provided to computer and the computer is trained to match the object with the template irrespective of object orientation.
Hema ENT 273 Lecture 7

7. Structural technique
Several structural techniques are available. These may take the features also in to account. We discuss a common method known as
Hema ENT 273 Lecture 7

8. Chain Codes Notation for recording list of edge points along contour
Chain code specifies the direction of the contour at each edge
Directions are quantized into one of eight directions
These codes are also known as freeman codes
Are used for the description of pixel border
Local information of the objects can be obtained from the chain code
E.g. where image border turns 90 degrees etc.
CHAIN CODE. Here there are two approaches
(a) 4-Directional Chain Code
(b) 8 Directional Chain code
Hema ENT 273 Lecture 7

9. Start with the start point and go along the arrows
4-directional converter
8-directional converter 1 2 3 4 5 6 7
OBJECT
Start with the start point and go along the arrows
Comparing the contour of object with respect to 4-directional chain code converter, we get
Chain code of Object:
START
Hema ENT 273 Lecture 7

10. We then get the difference;
CHAIN CODE :
We then get the difference;
Difference between 0 and 3 is 3 (ref 4 direc. converter)
Difference between 3 and 0 is 1
Difference between 0 and 3 is 3 and so on
Difference between 1 and 1 is 0
Difference between 1 and 0 is 3.
Difference Code is :
Take the minimum (decimal)value of Difference code as
This SHAPE number is for the object, uniquely recognized, independent of rotation (by 900). Normally chosen from Difference Code of smallest order
Hema ENT 273 Lecture 7

11. Another Object Verify: Chain Code: 0330011033323333221112111
Difference code:
Shape Number:
Hema ENT 273 Lecture 7

12. If the object edges are of slopes 450, , and , then we can use the 8-Directional converter. The procedure of getting the shape number is the same.
This method of object recognition is fast and can be used for different shapes of objects to be recognized if they are coming in a random sequence.
Hema ENT 273 Lecture 7

13. Chain coding example 2 3 4 1 5 8 7 6 3 5 6 7 1 2
Hema ENT 273 Lecture 7

14. Components of a object recognition system
Model database – model base
Feature detector
Hypothesizer
Hypothesis verifier
Feature
Detector
Hypothesis
Formation
verification
Modelbase
Image
Features
Candidate
objects
Object
Class
Hema ENT 273 Lecture 7

15. Components Model Database Feature Detector Hypothesizer
Contains all models known to the system for recognition –such as size, color, shape, CAD drawing etc
Feature Detector
Applies operators to images and identifies location of features that help the object hypothesis
Hypothesizer
Assigns likelihood to objects using features detected and selects object with highest likelihood
Hypothesis Verifier
Uses object models to select most likely object
Note: Depending on the complexity of the problems one or more modules becomes trivial.
Hema ENT 273 Lecture 7

16. Object Representation
Observer-Centered Representation
Applied to objects relatively in stable positions w.r. to camera
Global features of a scene are recognized
Features are selected based on experience of designer or analyzing features to form object groups
Object-Centered Representation
Uses description of objects based on usually 3D
Independent of camera parameters
Used in constructive solid geometry e.g. CAD / CAM
Hema ENT 273 Lecture 7

17. Recognition Strategies
Object recognition is a sequence of steps that is performed after appropriate features have been detected.
Not all object recognition techniques require strong hypothesis formation and verification steps
Hypothesizer
Classifier
Features
Object
Verifier
Sequential
Matching
Features
Object
Hypothesizer
Verifier
Features
Object
Hema ENT 273 Lecture 7

18. Strategies Classification Matching Nearest neighbor
Similar features in a region are clustered, based on a centroid and distance
Bayesian Classifier
Used when distribution of objects is not straightforward
When there is an overlap of features of different objects.
Probabilistic knowledge about features and frequency of objects is used
Neural Nets
Implement a classification approach
Use nonlinear boundary partition of features
Boundaries are used by training a net
Off-line computations
Computations are done before recognition
Recognition process can be converted to a look-up table
Matching
Feature Matching
Known features of the object are matched with unknown objects feature to find matches
Symbolic Matching
Relation among features are matched
Graph matching
Hema ENT 273 Lecture 7

19. Feature Detection Global Features Local Features Relational Features
Characteristic of a region
Area
Perimeter
Fourier Descriptors
Moments
Local Features
Features on the boundary of an object or a small region
Curvature
Boundary segment
Corners
Relational Features
Based on relative positions of different entities like regions, closed contours etc.
Distance between features
Used in defining composite objects
Hema ENT 273 Lecture 7

20. FEATURE EXTRACTION:
In vision, it is often necessary to distinguish one object from another. This is accomplished by mean of features that uniquely characterize the object.
Some features of objects that can be used in Vision are:
(a) Area (b) Minimum Enclosing rectangle
(c) Diameter (d) center of gravity
Perimeter (f) eccentricity (g) Aspect Ratio
(h) Number of holes (i) Moments
Hema ENT 273 Lecture 7

21. EXAMPLE: Let an original image of an object undergone several image processing techniques and finally available to us as a pixel pattern shown below:
Hema ENT 273 Lecture 7

22. Some of features can be computed as:
Moment ( M00 ) = = 24
Eccentricity = (Max x-length) / (Max y-length) = 9/4
Perimeter = 22
Area = 24
Diameter = 9
Thinness = {Diameter / area }
= ( 9 / 24 ) = 0.375
(g) compactness =
{ (perimeter)2 / area } = ( 22 2 /24 )
No: of holes = 0
Objects having these features belong to one category
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23. Example of Histogram
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27. Center of Gravity (COG)
Image Area
Center of Gravity (COG)
Or centroid for xc and yc. The area and COG is used to identify the position of the object
Hema ENT 273 Lecture 7

28. Moments- A sequence of numbers characterzing the shape of an object
The sum of power (j+k) is the order of themoment
Hema ENT 273 Lecture 7

29. If the COG is known we can determine the central of moment
Because object is balanced at COG, the first order moment is zero
Hema ENT 273 Lecture 7

30. The second order moment give the moment inertia of the image
Hema ENT 273 Lecture 7

31. Orientation-the angle of inclination
Hema ENT 273 Lecture 7

32. Eccentricity- maximum chord length is along the principal axis or major axis of object and minimum chord length is perpendicular to major axis
Hema ENT 273 Lecture 7

33. Aspect Ratio=Length of Rectangle enclosing object
Roundness,
Aspect Ratio=Length of Rectangle enclosing object
Width of rectangle enclosing Object
Hema ENT 273 Lecture 7

34. Example of Object Recognition
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37. Machine Vision Object Recognition and Feature Detection Hema C.R.
End of Lecture 7