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ECS776P IMAGE PROCESSING

创建日期：2024-08-20 16:34:47

所属分类：计算机computer science

标签： ECS776P

IMAGE PROCESSING

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ECS776P IMAGE PROCESSING

Question 1

Consider the 4x5 image with a dynamic range of [0-9] as in Figure 1:

S1	S2	S3	S4	S5
S1	S9	S8	S7	S6
0	S1	S7	S7	S6
9	0	S6	0	S5

Figure 1: An image of size 4x5

where S1-S2-S3- …S8-S9 are the 9 digits of your student number, from left to right.

For example, if your student number is 123456789, the original image you use for this question is

1	2	3	4	5
1	9	8	7	6
0	1	7	7	6
9	0	6	0	5

If your student number is 987654321, the original image you use for this question is

9	8	7	6	5
9	1	2	3	4
0	9	3	3	4
9	0	4	0	5

Note that a few pixel values are independent of your student number.

(a) Give the bit-planes of the image in Figure 1. [8 Marks]

(b) Find the transformation for histogram equalisation of the original image in Figure 1 and give the resulting image after histogram equalisation has been applied. [10 Marks]

(c) Give the formulas for power-law and apply it with power 0.4 to the original image in Figure 1 for intensity power-law transformation preserving the dynamic range from 0 to 9.

Question 2

Consider the 4x5 image with a dynamic range of [0-9] as in Figure 2:

S1	S2	S3	S4	S5
S1	S9	S8	S7	S6
0	S1	S7	S7	S6
9	0	S6	0	S5

Figure 2: An image of size 4x5

where S1-S2-S3- …S8-S9 are the 9 digits of your student number, from left to right.

For example, if your student number is 123456789, the original image you use for this question is

1	2	3	4	5
1	9	8	7	6
0	1	7	7	6
9	0	6	0	5

Note that a few pixel values are independent of your student number.

(a) Calculate the entropy of the image in Figure 2. [5 Marks]

(b) Find the Huffman code of the image in Figure 2, and give its bit rate. [14 Marks]

(c) Convert the image in Figure 2 into a binary image with a threshold of 5, and give the run-length code for the resulting binary image. [6 Marks]

Question 3

(a) In Figure 3, the first image (0) is the original image and all the other images (1)-(7) are the results of the application of some image processing operations on (0). Identify what operation has been used to produce each of the images (1)-(7) from image (0), and explain your answers.

Figure 3: Original image (0) and the processed results (1-7) [15 Marks]

(b) After edge detection operation is applied to an image of dynamic range from 0 to 255, give two methods in mathematical terms to transform. pixel values of the resulting image to better show the edges. [5 Marks]

i) What is the pixel size of the image?

ii) If the image has 3 components and the pixel values of two components are between 20 and 120, and the pixel values of the other component are between 50 and 250, how many megabytes do we need to store the whole image without compression? [5 Marks]

Question 4

Consider the 3x3 image with a dynamic range of [0-9] as in Figure 4:

S1	S6	S7
S2	S5	S8
S3	S4	S9

Figure 4: An image of size 3x3

where S1-S2-S3- …S8-S9 are the 9 digits of your student number, from left to right.

For example, if your student number is 123456789, the original image you use for this question is

1	6	7
2	5	8
3	4	9

(a) For the 3x3 image in Fig. 4, show the result of the convolution with the Laplacian mask. Indicate where the edges could be detected and explain why. Use replicate border extension for the convolution. [12 Marks]

(b) For the 3x3 image in Fig. 4, show the result of the median filtering. Use mirror border extension for the operation. [8 Marks]

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239 QF5203 INFR08010 IT114105 FIT2093 ECON3124 ECON1195 MATH 337 STAT 3006 COMP202 COMP3130 MATH3609 COMP 250 CS6140 IEEE ELE00041I FIN40090 CIV 5899 MATH3066 IGNMENT MATH 2009 STAT3405 HIST 101 MATH 3281 CEGE0042 ICT532 LAB 7 LAB 5 MCO455 CSIT214 Q4 IFB104 A1 ECO220Y5Y DA5020 CMPSC473 FIN9007 DSCI553 MSCI534 COMP3308 CSE-112 CMPT 310 EE104 F 2C P20 ALY2010 BUSM060 STA258 ECO00030M R BIOSTAT 274 COS6012-B FIT3155 CMPUT 340 EC421 INFS4205 ECOM135 NATS 1740 COMP809 MBE 6005 STA465 CSSE2310 CIVL2700 DATA4700 CMPSC 473 ISE 529 CS5044 MAST30020 COMP2207 ENGG1001 COMP0046 FIT3165 STAT GR5241 MECH0007 vATHK1001 ANALYTIC ATHK1001 MATH352001 HW3 S1 BMAN 24662 COMP 636 Stat-461 STAT 302 STAT 2132 STAR534 ENG4137 CMP3752M CS3103 BISM3222 STA 135 ME155A IIMT3601 ECON3371 ECON 3371 HW4 FINA 5840 DT211C CTEC3902 MAT005 MATH2148 S261F Econ 332 MATH3734 MIS41270 7CCMFM13 SYST 664 CIS434 CENG10014 MT4111 MT4527 ECMM424 CMT219 Math 136 MATH 3120 COMP10002 MAST20009 MATH0058 BEEM117 ISYE 6420 MGMTMFE 405 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1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A PHIL2617 PHYS3116 MIE250 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