# Invariants to translation and scaling Normalized central moments

- Slides: 43

Invariants to translation and scaling Normalized central moments

Invariants to rotation M. K. Hu, 1962 - 7 invariants of 3 rd order

Hard to find, easy to prove:

Drawbacks of the Hu’s invariants Dependence Incompleteness Insufficient number low discriminability

Consequence of the incompleteness of the Hu’s set The images not distinguishable by the Hu’s set

Normalized position to rotation

Normalized position to rotation

Invariants to rotation M. K. Hu, 1962

General construction of rotation invariants Complex moment in polar coordinates

Basic relations between moments

Rotation property of complex moments The magnitude is preserved, the phase is shifted by (p-q)α. Invariants are constructed by phase cancellation

Rotation invariants from complex moments Examples: How to select a complete and independent subset (basis) of the rotation invariants?

Construction of the basis This is the basis of invariants up to the order r

Inverse problem Is it possible to resolve this system ?

Inverse problem - solution

The basis of the 3 rd order This is basis B 3 (contains six real elements)

Comparing B 3 to the Hu’s set

Drawbacks of the Hu’s invariants Dependence Incompleteness

Comparing B 3 to the Hu’s set - Experiment The images distinguishable by B 3 but not by Hu’s set

Difficulties with symmetric objects Many moments and many invariants are zero

Examples of N-fold RS N=1 N=2 N=3 N=4 N=∞

Difficulties with symmetric objects Many moments and many invariants are zero

Difficulties with symmetric objects The greater N, the less nontrivial invariants Particularly

Difficulties with symmetric objects It is very important to use only non-trivial invariants The choice of appropriate invariants (basis of invariants) depends on N

The basis for N-fold symmetric objects Generalization of the previous theorem

Recognition of symmetric objects – Experiment 1 5 objects with N = 3

Recognition of symmetric objects – Experiment 1 Bad choice: p 0 = 2, q 0 = 1

Recognition of symmetric objects – Experiment 1 Optimal choice: p 0 = 3, q 0 = 0

Recognition of symmetric objects – Experiment 2 2 objects with N = 1 2 objects with N = 2 2 objects with N = 3 1 object with N = 4 2 objects with N = ∞

Recognition of symmetric objects – Experiment 2 Bad choice: p 0 = 2, q 0 = 1

Recognition of symmetric objects – Experiment 2 Better choice: p 0 = 4, q 0 = 0

Recognition of symmetric objects – Experiment 2 Theoretically optimal choice: p 0 = 12, q 0 = 0 Logarithmic scale

Recognition of symmetric objects – Experiment 2 The best choice: mixed orders

Recognition of circular landmarks Measurement of scoliosis progress during pregnancy

The goal: to detect the landmark centers The method: template matching by invariants

Normalized position to rotation

Rotation invariants via normalization

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- Class invariants
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- Normalized cut loss for weakly-supervised cnn segmentation
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- Second normal form
- Unnormalized data example
- Emr normalized instance hours
- Apple's plant layout procedure
- Normalized device coordinate
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- Normalized device coordinate
- Normalized rotation
- Derivation of time independent schrodinger equation
- Normalized data vs denormalized data
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- Orthogonal view
- Normalized state vector
- Segmentation
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- Normalized degree centrality example
- Voice translation rules
- 10 noun phrases
- Comparative scaling techniques
- Measurement and scaling
- Primary scales of measurement
- Measurement and scaling
- Signals
- Interval scale example
- Scaling up and down
- Shifting and scaling functions
- Comparing and scaling unit test
- Shifting and scaling functions
- Gracey curette classification
- Bridge scaling
- Decision tree scaling
- Scaling factors in vlsi
- Facebook scaling memcache