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Chapter 5: Neighborhood ProcessingPowerPoint Presentation

Chapter 5: Neighborhood Processing

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Chapter 5: Neighborhood Processing

- Point processing: applies a function to each
- pixel
- Neighborhood processing: applies a function
- to a neighborhood of each pixel

○ Neighborhood (mask)

- -- can have different shapes and sizes

2D

◎ Linear filter: linear combination of the gray

values in the mask

。Example

○ Processing near image boundaries

- Ignore the boundary
- Pad with zeros
- (c) Copy boundary

○ Values outside the range 0-255

- Clip values
- Scale values

◎ Convolution

5-9

◎ Correlation

Gaussian filters

- n × n filter:
- 2 (n × 1)filters:

Frequency: a measure by which gray

values change with distance

High pass filter

High frequency components, e.g., edges, noises

Low frequency components, e.g., regions

Frequency domain

Spatialdomain

Fouriertransform

Low pass filter

High pass

Low pass

◎ Edge Sharpening or Enhancement

- ○ Unsharp masking

。 Perform using a filter

。 Alternatives

(a)

(b) The averaging filter can be replaced

with any low pass filters

。 Example:

(a) Original (b) Unsharp Masking

- ○ High-boost filter
- high boost = A(original) – (low pass)
- = A(original) – ((original) - (high pass)
- = (A-1)(original) + (high pass)

。 Alternatives:

(a) (A/(A-1))(original) + (1/(A-1))((low pass)

(b) (A/(2A-1))(original) +

((1-A)/(2A-1))((low pass)

。 Example:

(a) (A/(A-1))(original) + (1/(A-1))((low pass)

(b) (A/(2A-1))(original) +

((1-A)/(2A-1))((low pass)

- 。 Median filter
- 。 K-nearest neighbors (K-NN)
- 。 Geometric mean filter
- 。 Alpha-trimmed mean filter
- i) Order elements
- ii) Trim off m end elements
- iii) Take mean

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