File size: 7,415 Bytes
17e2002 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 | 1
00:00:01,660 --> 00:00:03,980
So let's explore dilution and erosion.
2
00:00:04,030 --> 00:00:09,670
These are two very important open CVF operations and are quite useful in image processing.
3
00:00:09,760 --> 00:00:12,880
So the best way to explain this is by looking at examples here.
4
00:00:12,940 --> 00:00:14,590
So this is a letter s here.
5
00:00:14,620 --> 00:00:19,660
It's a white letter on a black box all which is what the zeros and ones correspond to.
6
00:00:19,660 --> 00:00:24,600
You can consider the ones to be 255 if you want just a Munteanu open see Visa on it.
7
00:00:24,820 --> 00:00:30,440
So let's look at what erosion does erosion removes pixels at the boundaries of an object of an image.
8
00:00:30,700 --> 00:00:35,920
What it means is that the boundaries of the object the object being a letter s here.
9
00:00:36,170 --> 00:00:40,640
Imagine everything is gone here and the bargees itself.
10
00:00:40,720 --> 00:00:42,590
That's exactly what erosion does.
11
00:00:42,610 --> 00:00:49,570
So we get it out that s dilation whoever does the opposite dilution that adds pixel to the boundaries
12
00:00:49,570 --> 00:00:50,990
of an object here.
13
00:00:51,010 --> 00:00:55,210
So this is why it becomes much Tica in our dilated segment here.
14
00:00:56,200 --> 00:01:01,200
So what about opening Andalus in here opening and closing are really useful functions that combined
15
00:01:01,270 --> 00:01:03,060
dilution and intuition together.
16
00:01:03,340 --> 00:01:10,850
So opening is erosion followed by dilation and closing is the opposite dilution followed by erosion.
17
00:01:11,230 --> 00:01:16,080
As you can imagine an erosion followed by dilution would be very useful in getting rid of noise.
18
00:01:16,300 --> 00:01:19,020
So imagine if it was some little white specks in this image.
19
00:01:19,180 --> 00:01:25,720
If we Rojos images and does become Tendo to one visit or disappear altogether then we'd do a dilution.
20
00:01:25,750 --> 00:01:28,010
We actually preserve the initial image here.
21
00:01:28,020 --> 00:01:30,220
We want to maintain.
22
00:01:30,450 --> 00:01:36,150
So no dilution and erosion while simple actually causes a lot of confusion with first timers who try
23
00:01:36,150 --> 00:01:41,930
to use it at all because there's a false misconception about what it does now.
24
00:01:41,940 --> 00:01:42,540
Yes it does.
25
00:01:42,540 --> 00:01:47,640
Dilution does in fact add pixels to the boundaries of an object and erosion does remove pixels at the
26
00:01:47,640 --> 00:01:48,830
boundaries of an object.
27
00:01:49,050 --> 00:01:56,310
However imagine we have a black X on a white bacterial open even to produce this white as being the
28
00:01:56,310 --> 00:01:57,370
object itself.
29
00:01:57,600 --> 00:02:03,180
So when we run an erosion here it's actually going to erode into this you see erosion is actually going
30
00:02:03,180 --> 00:02:08,450
to have the thickening effect of dilution and the dilution is going to have the opposite effect.
31
00:02:08,460 --> 00:02:13,440
It's going to have the thinning effect that we expect and erosion to have because what's happening here
32
00:02:13,440 --> 00:02:16,900
is that the boundaries of the image are basically in the points here.
33
00:02:17,190 --> 00:02:23,580
So when we direly it we're actually making white bigger on the edges of all P and whatever else happens
34
00:02:23,660 --> 00:02:25,600
if you would here.
35
00:02:25,740 --> 00:02:27,300
So please don't get them confused.
36
00:02:27,300 --> 00:02:30,610
It happens quite often it happened to me actually.
37
00:02:30,630 --> 00:02:32,310
So don't feel bad if it happens to you.
38
00:02:33,980 --> 00:02:39,680
So let's not look at implementing dilution erosion opening and closing you know a code.
39
00:02:39,720 --> 00:02:44,380
So the first thing to note is that we actually need to define a kernel and we define it by using non-place
40
00:02:44,470 --> 00:02:46,690
ones function data type here.
41
00:02:47,070 --> 00:02:49,280
And we have a five by five.
42
00:02:49,350 --> 00:02:50,980
You know exampled.
43
00:02:52,130 --> 00:02:56,080
So to erode function and daily function both follow the same pattern here.
44
00:02:56,340 --> 00:03:01,950
We take that input image couldn't say as we defined here and iterations which is how many times you
45
00:03:01,950 --> 00:03:02,810
run it.
46
00:03:03,030 --> 00:03:05,670
In most cases you would never need to run this more than once.
47
00:03:05,670 --> 00:03:12,840
However if you do run it to a tree or whatever amount of times you actually increase effect it's running
48
00:03:12,840 --> 00:03:15,450
the erosion twice and same image.
49
00:03:15,510 --> 00:03:16,470
So let's run this.
50
00:03:16,480 --> 00:03:19,080
I mean take a look and see what happens.
51
00:03:19,080 --> 00:03:24,760
So this is the open C-v text which I wrote out in Windows beant this is it.
52
00:03:24,770 --> 00:03:25,580
It really did.
53
00:03:25,740 --> 00:03:28,810
As you can see it has the effect of what we anticipated.
54
00:03:28,830 --> 00:03:32,560
It actually erodes the boundaries here.
55
00:03:34,700 --> 00:03:36,150
Making it much tighter.
56
00:03:36,590 --> 00:03:38,000
And let's see dilution now.
57
00:03:40,880 --> 00:03:42,160
Exactly as we anticipated.
58
00:03:42,170 --> 00:03:45,770
So as you can see dilution adds pixels the edges here.
59
00:03:45,770 --> 00:03:50,910
So it actually everything appears much thicker as if it was written with a marker and not a pen.
60
00:03:51,110 --> 00:03:59,910
And that's look at opening is the effects of erosion then dilution.
61
00:03:59,930 --> 00:04:06,180
So as you can see here because just when he wrote it actually disappeared these boundaries here when
62
00:04:06,180 --> 00:04:06,770
we dilated.
63
00:04:06,770 --> 00:04:12,180
No it actually misses these things totally because there's nothing to dilate the underside here.
64
00:04:12,500 --> 00:04:16,470
So that's why opening so that's whole opening has this effect on image here.
65
00:04:16,470 --> 00:04:20,780
So let's look at closing now no closing actually looks quite nice.
66
00:04:20,780 --> 00:04:25,870
And the reason for that is because closing is dilution first then eroding.
67
00:04:25,880 --> 00:04:31,540
So what we did here actually should get us back something to a very original image and actually it does.
68
00:04:31,550 --> 00:04:38,140
If you look at it is actually pretty much to see him just barely noticeable.
69
00:04:38,180 --> 00:04:42,980
So these operations that we've just seen are actually called morphology operations and they're actually
70
00:04:42,980 --> 00:04:48,210
a few more you can take a look at this link to view the eye is open see these official documentation
71
00:04:48,250 --> 00:04:48,950
site.
72
00:04:49,130 --> 00:04:53,050
However they aren't as useful as erudition and erosion in my opinion.
73
00:04:53,090 --> 00:04:57,490
But however you may have a special uses for it so feel free to check them out.
|