Flashcards in CSCE4240 - Exam 2 Deck (53):

1

##
histogram algorithm

step 1.

### create a one-dimensional array h of size L with initial value of zero

2

##
histogram algorithm

step 1. create one-dimensional array h of size L with initial value of zero

step 2.

### loop through all pixels in the image A

3

##
histogram algorithm

step 1. create one-dimensional array h of size L with initial value of zero

step 2. loop through all pixels in the image

step 3.

### for a pixel value v, increase the value at h(v) by one

4

##
histogram algorithm

step 1. create one-dimensional array h of size L with initial value of zero

step 2. loop through all pixels in the image

step 3. for a pixel value v, increase the value of h(v) by one

step 4.

### continue until all pixels in the image A are visited

5

## what is an image histogram

### acts as a graphical representation of the tonal distribution in a digital image

6

## what is histogram stretching

### maps the value of all pixels to a new value that spans the full gray scale range

7

## what is histogram equalization

### maximize the usage of the full brightness range. maximum contrast is achieved when image histogram is uniform distribution

8

## median filter algorithm

### sort all the pixels in an increasing order and take the middle value

9

## what are sampling rates

### rate at which amplitude values are digitized from the original waveform

10

## what is sinusoidal basis

### use smoothly-varying sinusoidal patters at different frequencies, angles for basis of images - hadamard basis doesn't capture real image gradients

11

## what is magnitude

### how much of a certain frequency component is in an image

12

## what is phase

### where that certain frequency lies

13

## butterworts lowpass filter

### introduces unwanted artifacts into the result. uses smooth transition

14

##
JPEG encoding

step 1.

### transform RGB to YUV and subsample color

15

##
JPEG encoding

step 1. transform RGB to YUV and subsample color

step 2.

### perform discrete cosine transform on 8x8 image blocks

16

##
JPEG encoding

step 1. transform RGB to YUV and subsample color

step 2. perform discrete cosine transform on 8x8 image blocks

step 3.

### perform quantization

17

##
JPEG encoding

step 1. transform RGB to YUV and subsample color

step 2. perform discrete cosine transform on 8x8 image blocks

step 3. perform quantization

step 4.

### zig-zag ordering and run-length encoding

18

##
JPEG encoding

step 1. transform RGB to YUV and subsample color

step 2. perform discrete cosine transform on 8x8 image blocks

step 3. perform quantization

step 4. zig-zag ordering and run-length encoding

step 5.

### entropy encoding

19

## what is quantization

### aims at reducing the total number of bits by dividing each entry in the frequency space block by an integer then round

20

##
wavelet-based image fusion

step 1.

### decompose images using wavelet transform

21

##
wavelet-based image fusion

step 1. decompose images using wavelet transform

step 2.

###
combine coefficients

1. combine approximation subbing and the average

2. select the maximum among detail subtends and put in the composite

22

##
wavelet-based image fusion

step 1. decompose images using wavelet transform

step 2. combine coefficients

step 3.

### perform inverse wavelet transform on the composite wavelet matrix

23

##
noise-aware image fusion algorithm

step 1.

### decompose images with wavelet transform

24

##
noise-aware image fusion algorithm

step 1. decompose images with wavelet transform

step 2.

### compute the subbing noise variance On

25

##
noise-aware image fusion algorithm

step 1. decompose images with wavelet transform

step 2. compute the subbing noise variance

step 3.

### estimate the threshold (lamda) that separate SIC and NIC

26

##
noise-aware image fusion algorithm

step 1. decompose images with wavelet transform

step 2. compute the subbing noise variance

step 3. estimate the threshold (lamda) that separate SIC and NIC

step 4/5.

###
4. combine SIC

5. combine NIC

27

##
noise-aware image fusion algorithm

step 1. decompose images with wavelet transform

step 2. compute the subbing noise variance

step 3. estimate the threshold (lamda) that separate SIC and NIC

step 4/5. combine SIC and combine NIC

step 6.

### synthesize a fused image from the subbing composite

28

##
JPEG2000 algorithm

1.

### image tilting

29

##
JPEG2000 algorithm

1. image tilting

2.

### DC-level shifting

30

##
JPEG2000 algorithm

1. image tilting

2. DC-level shifting

3.

### components tranformation

31

##
JPEG2000 algorithm

1. image tilting

2. DC-level shifting

3. components tranformation

4.

### wavelet transform

32

##
JPEG2000 algorithm

1. image tilting

2. DC-level shifting

3. components tranformation

4. wavelet transform

5.

### quantization

33

##
JPEG2000 algorithm

1. image tilting

2. DC-level shifting

3. components tranformation

4. wavelet transform

5. quantization

6.

### coefficient coding

34

##
Image morphology edge detection

1.

### dilate the original image

35

##
Image morphology edge detection

1. dilate the original image

2.

### subtract the original image from the dilated one

36

## looks for particular pattern within the image

### hit-and-miss transform

37

## delete any such point that has more than one foreground neighbor, as long as doing so does not locally disconnect the region

### thinning

38

## suppresses the bright details that are smaller than the specified SE

### opening

39

## suppresses the dark details

### closing

40

##
Chain Code

1.

### find the top-left pixel on the boundary; call P0

41

##
Chain Code

1. find the top-left pixel on the boundary; call P0

2.

### traverse the four neighborhood of the current pixel in the counter-clockwise order

42

##
Chain Code

1. find the top-left pixel on the boundary; call P0

2.traverse the four neighborhood of the current pixel in the counter-clockwise order

3

### stop when current boundary pixel Pk equals to P1 and Pk-1 equals to P0

43

##
mean shift

1.

### start from an arbitrary point in the distribution

44

##
mean shift

1. start from an arbitrary point in the distribution

2.

### region of interest is a circle centered at this point

45

##
mean shift

1. start from an arbitrary point in the distribution

2. region of interest is a circle centered at this point

3.

### on each iteration, find the center of mass for the ROI

46

##
mean shift

1. start from an arbitrary point in the distribution

2. region of interest is a circle centered at this point

3. on each iteration, find the center of mass for the ROI

4.

### move the circle to this center

47

##
mean shift

1. start from an arbitrary point in the distribution

2. region of interest is a circle centered at this point

3. on each iteration, find the center of mass for the ROI

4. move the circle to this center

5.

### continue the iterations until it convergences

48

##
hough transform

1.

### discretize parameter space into bins

49

##
hough transform

1. discretize parameter space into bins

### for each feature point in the image, put a vote in every bin in the parameter space that could have generated this point

50

##
hough transform

1. discretize parameter space into bins

2. for each feature point in the image, put a vote in every bin in the parameter space that could have generated this point

3

### find bins that have the most votes

51

## selecting seed without a-priori knowledge

### compute the histogram and choose the grey values with the highest peak

52

## otsu thresholding method idea

### find the threshold that minimizes the weighted within-class variance and maximizes the between class variance

53