CSCE4240 - Exam 2 Flashcards Preview

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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

otsu thresholding method assumption

the histogram is a binomial distribution and the objects colors are mostly homogeneous