Hi, this is Jonathan Yildon with the University of Vermont, and this webinar is part of the America View series on object-based image analysis. In today's presentation, I'll be giving an overview of some of the image segmentation techniques available within the eCognition software package. I'd like to point out that the true power of object-based image analysis is not the segmentation algorithm itself, but rather the ability to build context through iterations. As humans, we've evolved to make use of extensive contextual information when we identify features, and we can replicate that within the object-based image analysis environment. So with segmentation, when applied to an image, we can generate image objects. But once we have those objects, we may need to go ahead and apply additional segmentation routines, and we also may need to apply fusion classification and morphology routines to refine those image objects. It's not uncommon within a single expert system applied within an object-based image analysis workflow to need to segment five, ten, or even fifty times, and thus it becomes important to understand some of the performance issues associated with the various segmentation algorithms within eCognition. I'd like to give you a quick overview of the data that we'll be working with today. First, I have a vector layer, in this case hydrology polygons. I have a four-band imagery data set, in this case aerial imagery. I also have two LiDAR data sets. One is a LiDAR intensity image. And finally, the other is a LiDAR normalized digital surface model, or NDSM, in which the pixels represent the height of features relative to the ground. Moving over to eCognition, you can see over on the right here in my process tree, I have all the different segmentation examples I'm going to demonstrate today already preloaded. I'd like to point out that I have a little routine here called reset. You're going to see me execute this routine over and over again. Essentially, what I am doing here is I'm deleting any existing segmentation levels or maps, so that I have a clean slate for which to apply the new segmentations. So for segmentation types, let's start by going over the chessboard segmentation. Chessboard segmentation is the most basic type of segmentation you can do with any cognition. It's also the fastest segmentation from a processing standpoint. And all it's doing is cutting up your data into squares. So in this case, I have one meter data. I'm applying the image object domain to the pixel level. I'm creating image objects with a size of 100. So essentially, squares that are 100 meters on each side. And I'm creating a new level called level one. Let's go ahead and execute that. And we see here that we simply have 100 meter squares created over our image. These, of course, don't represent features very well. And you're typically not going to use the chessboard segmentation algorithm in this fashion. It is very useful for incorporating existing thematic data sets. So let's clear our slate here. And now we're going to apply the chessboard segmentation algorithm, once again to the pixel level because we don't have any existing image objects. But here I'm setting the object size to a very high value. I'm calling my new level level one. But what's different than last time is that I'm constraining my operations based on this existing thematic layer I have. This hydrology polygon layer. So while I'm creating these very large chessboard objects, it's going to be constrained by the boundaries of my existing vector data set. This is essentially the mechanism that you use to incorporate vector data into e-cognition by turning them into image objects. Executing that algorithm, we can see here that we now have those hydrology polygons within e-cognition existing as image objects. And of course, at this point in time, I can work with them like any other image object, classifying them, applying additional segmentation algorithms to them, and access all of the wonderful image object attributes that e-cognition provides me. Now the most popular type of segmentation, or I should say probably the most widely used segmentation algorithm with e-cognition and really what gained prominence early on within the software is the multi-resolution segmentation algorithm. So once again, let's clean our slate here. And I'm going to go ahead and open the multi-resolution segmentation algorithm I have here. You can see once again, I'm applying it to the pixel level. So I'm using strictly the pixels, not existing image objects, to create the new objects. I'm creating a new level called level one. And here I'm weighting the data sets that I've used. So I can weight any raster data sets that I have. So while I have some lidar data sets, in this case for multi-resolution segmentation, I'm only using my four bands. And I could choose if I wanted to, to weight something like my red band, or my near and for red band, more than the other bands. I also have a scale parameter here. This is what controls the size of the image objects. Now the scale parameter relates to the type of data you have for one meter data, such as I'm working with here. A scale parameter of 20 is going to produce very different sized objects. And if I was using 30 meter Landsat satellite imagery, I can also weight the shape and compactness of these image objects. Generally, you don't want your shape weight to be too high. The reason being that then it ignores the actual spectral information contained within your data. And the compactness setting is up to you. Now it's easy to spend hours going over and changing the scale parameter and the shape and compactness parameters trying to get the resulting image objects to accurately represent your features of interest. And I'd caution you to keep in mind that segmentation is something that you're going to have to do over and over again as part of this iterative process to build context. So my recommendation is to spend a few minutes playing around with these parameters, and generally you just want to ensure that whatever feature you're interested in that you don't have image objects resulting from your segmentation algorithm that contain more than one feature. So I wouldn't want image objects, for example, that include both buildings and trees within that image object if I'm concerned with differentiating those two features. So let's go ahead and execute the multi resolution segmentation algorithm. The first thing you'll notice is when it comes to the processing time for segmentation, the multi resolution segmentation on this image took 40 seconds compared to that initial chessboard segmentation we did, which took less than a second. So once again, multi resolution segmentation is very processor intensive. Of course, the nice thing about it is that we get image objects that are much more suitable for classification. While I'm not going to go into the full process of the iterative loops needed to sort of build context and create more meaningful objects, I do want to point out a segmentation algorithm that's very useful in this process, and that's the spectral difference segmentation algorithm. In order to apply the spectral difference segmentation algorithm, you have to have existing image objects. In this case, we created image objects based on the multi resolution segmentation algorithm. That's why you can see here under the image object domain, I'm applying this algorithm to the image object level. That means two existing image objects. I'm applying that on level one because that was the name of the level I created from the multi resolution segmentation. And here I'm applying a maximum spectral difference of five. And once again, this maximum spectral difference setting is totally dependent on the type of data that you use. And it may be different based on different areas and on different types of data. Much like multi resolution segmentation, I can also choose to weight my layers. So I can change the weight, for example, and perhaps up the influence of my multi resolution segmentation band. I can also choose to include or exclude thematic layers when I'm doing this. So let's go ahead and execute this spectral difference algorithm and take a look at the result. So what happened here is that we merged image objects that were spectrally similar. As you can see, we have a much better representation of this field now because rather than it being comprised of all these tiny little image objects, we have it as a much larger object, allowing us to perhaps use shape and size to classify it rather than simply the spectral information. Let's reset. Going back to a clean slate here. And now we're going to use another segmentation algorithm that breaks things into squares similar to the chessboard segmentation algorithm. Only in this one, we're going to have variable squares based on the heterogeneity of the data. So this is the quadtree-based segmentation algorithm. Once again, because I have a clean slate, I'm applying it to the pixel level here. There are different modes here, but generally you're going to always use the color mode. The scale parameter is similar to multi resolution segmentation. This will create larger image objects as you increase the scale parameter. And you have the opportunity to weight your layers here, but this is strictly a Boolean operation. So yes or no to use those layers. And we're going to click execute here. And so as I mentioned earlier, you get these squares in areas where it's more homogeneous. You're going to have larger squares in areas where there's more heterogeneity. From one feature to the next, you're going to get smaller squares. You're going to notice the quadtree segmentation, especially compared to the multi resolution segmentation, which took 40 seconds. Quadtree segmentation took a little over two seconds. So it's very, very fast. It doesn't produce particularly meaningful objects based on their shape. But I do find that the quadtree segmentation algorithm works very well sometimes for features that you want to classify based strictly on their spectral information. An example is a first pass at extracting impervious surfaces. Because we have image objects, we can do just what we did before with the multi resolution segmentation algorithm and go ahead and apply the spectral difference segmentation algorithm. So very similar to what we did before when we used the spectral difference segmentation algorithm, we're applying it to the image object level. So our existing image objects that we created as part of the quadtree segmentation, those objects are on level one. And this time I set the maximum spectral difference to eight. I can weight my layers here because it's a spectral difference segmentation algorithm. And I can choose to use or not use my thematic layers. And let's click execute. Now I don't think these objects look quite as good as what we achieved using the multi resolution segmentation algorithm followed by the spectral difference segmentation algorithm. But in some cases, particularly for these manmade features here, I think they do quite a good job of representing our features of interest. And if we look at the time, it took two seconds for the quadtree followed by 11.5 seconds for the spectral difference. That's much more efficient than the 40 seconds it took for the multi resolution segmentation followed by the nearly two seconds it took for the spectral difference algorithm. So when you're working with extremely large projects and processing time becomes a concern, you can achieve nearly identical results or results I should say that are perhaps good enough. Sometimes it's using these different segmentation algorithms can be a big time saver. I'm going to introduce you to some of the threshold approaches to segmentation. These are particularly useful when you're working with something like a LIDAR normalized digital surface model. So once again, what we're seeing here is the height of features relative to the ground. As I scroll around to you can see that my buildings, this one in particular is about five to six meters in height. And I have forest canopy that's much taller, up to 20 meters in some cases. So it's often nice as part of my workflow to do an initial segmentation where I just, for example, segment out all those tool features and then work with them separately. And then subsequent approaches do additional segmentations and so forth to separate out perhaps the trees in the buildings. So we reset here. The first one that I'm going to introduce you to is the contrast split algorithm. And in order to do a comparison to the another algorithm known as the multi-threshold segmentation algorithm, I'm going to use a map operation. Map operations are very powerful. And these have been introduced in E-Cognition 8. And they allow you to create essentially a duplicate copy of your data set and apply different segmentation algorithms to each one of their image maps. Automatically, when you start E-Cognition, you're working in a map called main. And you always have to have this main map. But you can run this algorithm known as CopyMap to create a brand new map. It's a duplicate of either all of your data or a subset of your data. So in this case, you can see that I'm running the CopyMap algorithm. My target map is going to be called Map2. And I'm copying over strictly that NDSM layer. And I'm going to choose to execute that. Now if I split my windows horizontally, in one, I can show Map2. And to the other, you're going to notice I'm displaying my main map. And these are also indicated in the lower right corner of each one of your views here. So the first thing is that on Map2, I'm going to use a contrast split segmentation algorithm. Like all the segmentation algorithms, I encourage you to read the reference materials so that you understand all the parameters. But I'm going to go over them quickly here. I'm applying this contrast split algorithm on the pixel level. And here I've specified that I want to use Map2 for that. And I have some other settings here. The main things to keep in mind is I have a new level, level one, that I'm creating as part of this, and that I'm going to be splitting off all objects that are two meters or taller in size. So these are really here the crucial settings in the contrast split algorithm. What's also very important is the layer that I'm using, in this case the normalized digital surface model. And then what's unique about contrast split compared to the other segmentation algorithms we've been using is that it segments and classifies in one fell swoop. So my class for bright objects here is going to be tall, bright objects, those that exceed the minimum threshold, and those that are below that threshold are going to be unclassified. Essentially what this is saying is that everything that's taller than two meters is going to be assigned to this class called tall. So let's go ahead and execute that. That's complete. You can see it took a little over 30 seconds to run that contrast split algorithm. And if we flicker back and forth here between our classification and the image objects, you can see that the contrast split algorithm essentially split off all of our tool features based on the normalized digital surface model. Now in our main map, we're going to apply an algorithm that generates very similar results, only much more quickly. And this is the multi threshold segmentation algorithm. Once again, this is an algorithm that's new in e-cognition version eight. And it's a bit simpler and one, like I said, the main benefit is that it's a lot faster than the contrast split segmentation algorithm. So I'm applying this on the pixel level. I'm using my main map here. And I'm using my normalized digital surface model. And down below here, I have my thresholds. So my first threshold is a cutoff at two. And I think below two goes to the unclassified category. Things between two and 255, which is the maximum value of my eight bit LiDAR normalized digital surface model are going to be assigned to the class tall. Let's execute multi threshold. Let's first compare the times over 30 seconds for a contrast split to a little over one second for the multi threshold segmentation algorithm. Putting these two side by side, we can zoom in here and we can see it looks like we've achieved identical results, segmenting out buildings, tree canopy, and tall features such as power lines from this data set. The main difference here being that the multi threshold segmentation algorithm is clearly much more efficient. So while you might find certain situations where contrast split works well, if you're looking to simply partition features based on the layer values in a single data set, I recommend using the multi threshold segmentation algorithm. Now let's look at a case where we want to take existing image objects and perhaps refine them and create image objects within those image objects. First of all, let's reset here to start a clean slate. Now I'm going to go ahead and remove the split from my view so that we have a single view and we'll move back to a three layer true color display.  So once again here, essentially incorporating the thematic layer image objects. You can see those here. And in the next step, I'm going to apply this image segmentation algorithm using image object domains that I create new image objects but only for these hydrology features. So the first thing that I need to do is I need to classify these image objects as such. So here I'm using the assign class algorithm and I'm simply going in and accessing the attribute table of my hydrology features here to say that if they have a value in the attribute table greater than equal to zero for this field calm ID, I'm going to assign them to the class water. We can see now that we have features classified as water simply incorporating the classification from our thematic layer. Now I may want to cut up those water features because I noticed that not all those water features appear to be actual water features. Some are digitizing errors. So I might want to reevaluate them by creating image objects within side of them. So here I'm going to apply a multi resolution segmentation algorithm. Obviously we used the pixel level as the image object domain. In this case, we're going to use the image object level. So that means we're going to be working off existing image objects. We're going to constrain the operation so that we only do it on objects classified as water. I'm using level one using similar layer weights and shaping compactness criterion as we did before. And let's click execute to take a look at how this works.  If we take a look at our image objects, I think that's going to be probably surprising because we only generated image objects within our existing image objects, in this case within our water objects. Once again, because we constrain the operation to those water objects by focusing on the image object level and the class water. So this took over a minute despite the fact that when we ran the multi resolution segmentation created more image objects on the pixel level before it only took us about 40 seconds. One interesting fact about eCognition is that when you use the image object domain for multi resolution segmentation, it actually decreases performance. So I'm going to give you a couple ways to get around that. The first is going to be using sub levels. So let's reset here to get a clean slate. First thing that I'm going to do here once again is create image objects based on my thematic layer. We can see those image objects here. Once again assigning them to that water class. And now I'm going to do a multi resolution segmentation but I'm going to do it on the image object level only I'm going to create a new level level sub that's below my main level here, level one. Excellent, now that's complete. You're going to notice that it ran a little bit faster, 51 seconds as opposed to a minute for the multi resolution segmentation. The challenge here of course is that our image objects are on a level called sub below our main level. So now we need to incorporate those image objects into our water polygons here. So I'm going to execute an algorithm called convert to sub objects. I'm going to do this on level one and focus the image object domain on those water features. So I execute that, you can see that what I've done is I've incorporated those image objects that were created on the sub level into my water polygons. And so I can now go ahead and run a delete image object level algorithm to remove that sub level. So that gives me a little bit of a performance boost there saving about close to 10 seconds overall. Now there's another way of doing this and that's using the map concept that I first showed you earlier. So let's once again, let's do a clean slate here. Walking through the steps, very similar to what we did before here. There's the chessboard segmentation. Now let's classify those features as water. Only now I'm going to copy this existing map over to map two here. And now on map two, I'm going to perform a multi resolution segmentation. Right away you'll notice that in terms of the time it took to do the segmentation, nearly identically to what we achieved before when we first ran the multi resolution segmentation on the pixel level. So let's go over to map two here. On map two you're going to notice that we have image objects not constrained by existing thematic layers. So going over to the main map, what we're going to do now is we're going to create a sub level on the main map. So that's this level here. And now we're going to synchronize those two maps. So essentially passing the image objects over from map two to this level sub only for those features in the class water. There we go, we can see that we have our image objects. Now we're going to use convert to sub objects to pass those image objects up to the main level and then finally delete the sub level. What we end up with is the exact same results we had in the first two examples. We have image objects created from a multi resolution segmentation within our water features. The difference is that we've saved roughly 20 seconds by using this map approach. So with any cognition, you may often find that routines that use many more algorithms such as this map concept of multi resolution segmentation while they may on the surface look like they're going to be more processor intensive because you're using several, perhaps even tens of algorithms are actually big time savers. Now let's look at a way where you could really save time by not using the multi resolution segmentation algorithm first of all and combining the quadtree segmentation algorithm with a multi resolution segmentation region grow. So here we're going to reset again. First of all, doing the same exact procedures we did before creating those image objects for our water polygons and classifying them based on their thematic object attributes. Now instead of using a multi resolution segmentation I'm going to use a quadtree based segmentation as we did with the multi resolution segmentation I'm using the image object domain to constrain the image segmentation operations. So I'm focusing on the image object level on class water and you can see here I have my scale parameter and image objects weight set. I'm going to go ahead and execute and in less than two seconds I have quadtree objects. Now the problem of course as we know with quadtree objects is they often don't represent features of interest very well but I can apply an algorithm called multi resolution segmentation region grow and this works very similar to the multi resolution segmentation algorithm with the exception is that it's growing objects into one another using the multi resolution segmentation algorithm. So I'm focusing here in the image object domain, level one my class filter is water here. You can see that I have image layer weights just as I would. If I was using the multi resolution segmentation algorithm I've got a scale parameter and I also have my shape and compactness criteria. Very important if you're using this generally you want to set the loops and cycles to loop while something changes. This will continue to merge and grow those objects together using the multi resolution segmentation algorithm while something changes. Also very important you want to constrain your class filters. I want class water and I only want to grow it into other water features. This will constrain my multi resolution segmentation region grow within the existing water features. There we go we have a very fast operation there and now I might want to go ahead and execute a spectral difference segmentation to perhaps clean things up a bit. The end result here is that we have image objects that I think do an excellent job of representing features of interest. The advantage of course with taking this quadtree multi resolution segmentation grow approach is if you look at the times on all of these algorithms they're all less than two seconds. And here's a little graph that I put together that illustrates some of the time savings we can get by using that quadtree multi resolution segmentation grow on the current level. You can see that it's overall it took less than five seconds. If we look at the map approach where we split off our data and ran multi resolution segmentation on a separate map then incorporated that. That was more than five times longer at just over 25 seconds. If we looked at doing the multi resolution segmentation on the sub level that took 35 seconds. And finally if it was multi resolution segmentation on the current level that took close to 45 seconds. So I'd like to stress once again that although multi resolution segmentation algorithm generally produces the most meaningful objects. If you're doing very, very large data set processing and you become concerned with time particularly if you're using lots of different segmentation algorithms in the course of your rule set. You might want to consider some of these alternate methods to segmentation particularly when you want to work within an existing image object domain. Hope you enjoyed this webinar. If you're interested in working with the data.


All right, so let's take a look at problem 1.6. And here's an important idea. If you want to learn anything about anything, you'll need to read about it. Watching videos, attending lectures, that sort of thing, those are very helpful. But the printed word conveys far more information in a far smaller space. Consider how long it took me to convey the information in this one paragraph, versus how quickly you could read the paragraph. Now, multiply that by 1,000, and you'll have an understanding of why the printed word is so very important. So here's what the printed word has to say about the Venn Diagram. So this is from your book. And this describes the Venn Diagram. And one important thing here is this sentence at the very end. You should always assume that there are other elements in and out of the set that aren't shown. And what that means is if I draw a set this way, I have to assume there are elements over here in the outside and there's elements all over the set inside as well, which means that the elements I see, and even if I draw in some elements, the elements I draw in aren't all that there are. And there are going to be, we should always assume, there are other elements that we're not drawing in every place that they can be. And this has some important implications. Now, if you really want to understand Venn Diagrams, don't watch the rest of the video. You can't learn mathematics by watching somebody else solve problems. So again, if you want to learn about Venn Diagrams, solve this problem. If you don't want to learn about Venn Diagrams, you could watch somebody else solve the problem. So let's take a look at this. So draw a Venn Diagram with some sets where A and B can never have column and elements. And every element of C is also an element of A. So to begin with, we'll draw two sets A and B. So we'll draw a circle representing a set and see if they meet the requirements. So I'll draw two random circles like this and let's see. So I have to assume there are elements someplace, every place, even if I don't show them. So if I draw the circles this way, the thing to notice is that if I put an element of A, if I put down an element in A, that is automatically going to be an element of B. Except A and B are supposed to never have common elements. So I know that this diagram can't possibly be correct. So we should try a different drawing. So I'll draw a different set of circles. So I'll draw something like this. And while here I can put something in here that is not an element of B, but because I should assume there are elements even if I haven't drawn them, I could put an element right here. And that element, if I put an element in here, that is in A, but it's also in B. And it's a common element of A and B. And I know that A and B could never have common elements. So I'll try a different circle. Well, what if our circles look like this? So if I put something in A, no matter where I put it, it can't be in B. And if I put something in B, no matter where I put that element, it can't be in A. Since it's not possible for something in A to be in B, or vice versa, then I have drawn two circles of N diagram that shows that A and B can never have common elements. Well, let's throw in that third requirement. L for element of C is also an element of A. So let's draw a third set C. And to include both, I'll draw this ellipse. And so maybe it looks something like this. I'll draw something like that and check to see if it meets the requirements. Every element of C is also an element of A. Well, the problem is, again, I can put something any place in here. I have to assume that there are elements even though I haven't drawn them. If I put an element in C, that is not going to be necessarily an element of A. So this drawing doesn't work. Well, I'll try a different drawing. So maybe I'll draw something like that. And if I put something in C, well, if I happen to put it here, then it's going to be an element of A. But I could put it here because, again, even though you don't see them, you should assume that there are other elements. So this element, if there's an element here, it is not an element of A. So this doesn't fit that requirement. Well, maybe something like that. And if I put something in C, any element of C can't be an element of A. So this doesn't work. And so that means we should try a few other possibilities. Maybe it looks like this. Well, now let's see if I do that. Well, it fails. Every element of C is an element of A. In fact, no elements of C are going to be an element of A. How about over here? Again, I have to assume that there are elements even if I don't draw them. So maybe there's one over here, which is not an element of A. And so again, it fails this requirement. Every element of C is also an element of A. And maybe I draw something like that. And that seems to work. If I put an element in C, that's automatically going to be an element of A. And let's go ahead and check. A and B can never have common elements. Figure that out. Every element of C is also an element of A. That also works. And it looks like this is a good diagram.


This is Orson Welles, speaking from London, the Black Museum, a repository of death.  Where everyday objects, a kitchen knife, a roller skate, a violin string, all are touched by murder. There's a 32-caliber bullet. It's a familiar object. Brass cartridge case, snob lead and nose. Not very pretty to look at. Interesting, this bullet, Sergeant. Notice the back of the cartridge case. Yes, sir, it's for a centre-fire weapon. The firing pin of the pistol must strike the centre of the cartridge. Right. But the weapon in which we found this bullet was a rimfire revolver. The firing pin could strike only the edge of the cartridge. That little fact, Sergeant, saved at least one life, I'd say. Well, today that centre-fire bullet can be seen in the Black Museum. As of the criminal investigation department of the London Police, we bring you the dramatic stories of the crimes recorded by the objects in Scotland Yard's gallery of death. The Black Museum. Scotland Yard's Museum. Museum of Murder. The Allies' death. So orderly, so well-kept, row and row. A macabre record of the violence of many generations. Each object in its place, each marked, tagged, carded with name and place and date. Each object in this room enjoyed in its life one supreme moment, when the eyes of the world were upon it. And it played its silent part in a case of murder. The guy has a jeweled ring, fit for a princess, but born of a mind like the Borges.  Shake hands with your enemy, shortly thereafter, he dies. A bathtub. Yes, a bathtub. A white, smooth, shining in the dimness. Once a man floated face down in this tub, he slipped or he had been pushed. Was the mark on his skull caused by striking his head as he slipped, or by a blunt weapon? In any case, he drowned. Ah, I see. The 32-caliber bullet. Even here, lying so quietly, so sombrely, it's ugly. This object was made for killing. The object he never did kill. That's the story. It began on a London street. A dark sedan drove slowly into the stream of traffic. Three young men in it. One seems quite a bit younger than the other. Even so, the car continues to cruise along. You've got everything straight, Rod? Sure, sure. Do you think I'm a goon? You just want to be sure, kid. That's all. Now, what did we tell you? I'll stay in the car till you wigwag is all clear. Then I'll go in with you. Hey, there's a chance to park the old bus. Oh, kinky. Ah, watch the fenders now. Nobody to ask for your life since a town like this, eh? Sure, sure. Just kidding. That's all. What's the matter? Are you edgy? Cold as ice, that's me. Yeah. Ice is what we're going to get. All set. Let's go. Two young men leave an automobile, stroll up the street, hands in pockets, cigarettes dangling loosely from their mouths. They walk past a busy jewellery store. Pretty crowded. Maybe we ought to wait. Could be. No use taking chances. They stroll a few steps farther on. They turn, start back, quite casually. Hey, what's the kid doing? The guy's going into the store. Come on. A goon must have thought we'd make the signal. Got your gun? What else? Still down now? Here we go. What's the idea? You wigwag, didn't you? Never mind. Let's do it. Right. All right, everybody. This is a hold-up. Over against that wall. Everybody. These guns are loaded. Nobody talks, I'll give it to him. Rob, get the junk on the counters. You there. Stand still. You've got the button. Rock the car. Stop the engine. Ready, spread.  Run for it, kid. Back way. I can't get the car out. I can't. Come on, dad. Keep moving. Stop you. Stop. Stop. You're the speed. Stop. The black sedan stayed in its place. Three young men disappeared and the teeming streets disappeared completely. A few hours later, in a quiet office in Scotland Yard, Inspector Bowers and Detective Sergeant Wood looked at what they had. Not very much of anything, have we, Sergeant? No, sir. Descriptions which could be any over 100 men and two bullets. A 45 bullet from the woodwork in the jewellery store and a 32 which killed that fellow. But no guns to go with them. If those boys were smart at all, they'd ditch the guns. Funny. You'd expect a lot more shooting, you know, something like that. They were conserving ammunition. Oh, well, I don't know. Circularize the descriptions and what there is of them. That's about all for the moment. Yes, sir. Three men, youngish, white handkerchiefs... And that was all. Now, where do you start, among 8 million people, searching for three youngish men who may be miles away by now? Well, the answer is you don't start. You wait. You have patience. And sometimes your patience is rewarded. Sometimes sooner than you expect. This is Wilson, expector. He's the cabbie who called in this morning. Ah, how do you do, Wilson? Sit down. Thank you, sir. Thanks very much. Wilson may have something on the jewellery store killing, sir. Well, let's hear it, Wilson. Well, sir, I never thought about it until I've seen the papers this morning. All about the old up and that poor fellow who got himself killed yesterday. Go on, Wilson. Well, sir, I was taking me along Queensbury Road, about a block from the place where it all happened, when a fellow with a white thing round his neck hops on the running board. I thought it was odd affair, so I waved him off. Then I see him going to that big building, the Brook Building, I think it is, sir. You know, corner of Queensbury and Mason. Oh, you're certain of this, Wilson? Oh, yes, sir. Anything else? I'm not so sure of this, Inspector, but I think I saw another fellow running the building, after the one that jumped onto my cab. Oh, very good, Wilson. Well, you may have been quite helpful. Thank you very much. Thanks. Um, get your hat, Sergeant. We're going exploring. The reward of patience. And sooner than expected, but only a bare and vague beginning. This kind of investigation takes long hours and hard work. And endless, endless questions. Up and down the hallways of the large office building, in and out of business offices. Did you notice any commotion in the hall yesterday, around three o'clock? Did a man with a white scarf around his neck take your elevator around three o'clock yesterday? Did you hear any running, or anything like that, around three o'clock yesterday? On and on, upstairs, downstairs, nothing. No one noticed anything, nothing irregular, no commotion. How does that? Did the porter notice anything? Two fellows. One of them had a raincoat, I think. Both of them had white rags around their collars, sort of. Oh, you're sure of this? Oh, yes, sir. They seemed in a hurry, sort of. Where did they go? Which office you mean? That's right. Well, I wouldn't know, sir. They went up the stairs, just as I was putting some empty waste cans on the service level. Did you see the men again? No, you mention it. Yes, sir, I did. Where? On the third floor it was. I was taking my mop to the hall. One of them was sort of half sitting on the stair rail. The other fellow was looking out of the hall window that opens onto Queensbury, sir. They didn't have the white rags, sir, but they were the same fellows. Anything else? No, sir. Not that I remember. Oh, Pop Saunders might know something, Inspector. Who's this Saunders? He drives a van, sir. He's been making deliveries to this place every afternoon for years. Ah, thank you. Right, well, let's go, Saunders. Maybe Mr. Saunders does know something. We have your address. They reach the street outside, just as a large, rather decrepit truck driven by an equally decrepit elderly man pulled up to the Kurds' terminal. You, Pop Saunders? That's me. Who are you? Sergeant Wood, Mr. Cotton Yard. This is Inspector Bowers. Yes? Have I done something? No, but you may have seen something that'll help us. Yes, sir. Were you here at this time yesterday? Yes, sir. I'm always here. This time of day. Well, did you notice anyone with a white scarf go into the building? Running, perhaps? Oh, those two. Yes, sir, I did. Funny thing, they seemed in such a hurry. One had made a raincoat. They came out later. No scarves and no raincoat. Now the search is on for a raincoat. Scarves or handkerchiefs can go into pockets, not raincoats. The brook building swarms with police every nook and cranny is inspected in the cellar, in a dark corner. This must be it, Inspector. Every other raincoat in the building is accounted for. Not a mark on it. Nothing. Try ripping the lining, Sergeant. Nothing, sir, except the manufacturer's stock tag under the armpit. Not much, but a little at any rate. Check the manufacturer from there to the jobber who bought the lot in which that code was packed. From the jobber to the retailer. It's times like this, I thank heaven for rationing. At least they have to keep a record of the clothing coupons and the names of everyone who buys anything. A theme, a tenuous trail and a mountain of sales slips to go through. But nothing strikes a chord of memory. And then the sergeant thinks of something. Inspector, when they make out these records, don't they write the last names first? Yeah, you're right, they do. Oh, well, back over it again. Here, how about this, Sergeant? Mac Stanley. The other way, Stanley Mac. Remember that name, Sergeant? Stanley Mac. Not a usual name. Yes, sir, I do. Didn't he testify at a trial about two years ago? He did. The trial of Jack Georgetown. He was sent away for armed robbery, first offence, light sentence. Pal Georgetown ought to be out by now. All right, let's get his dossier over here, Sergeant, and then invite him in for a talk. It may be interesting if nothing else. Well, today that centrefire bullet can be seen in the Black Museum. It was a long chance. One man buys a raincoat.  The defendant is sent to prison for armed robbery. Under circumstances similar to the case under investigation, the inspector in charge sends for the convicted man's dossier and fines. Well, so Jack Georgetown was released from prison six days before the Queensbury killing. Well, let's have the young man into the yard, shall we, Sergeant? You'll be able to find him easily enough, I think. If not, we can suspect the flight because of guilt, can't we? Send a pickup order out for him. Jack Georgetown was found. Found quite easily in London. Look, you have nothing on me, Inspector, and you know it. Maybe, maybe not. Show, um, Mr. Georgetown the garment. Here it is, Georgetown. Do you recognise it? There's a million raincoats like that. Yeah, but not bought by Stanley Mack. Oh. Oh, that coat. You know it? Yeah. Yeah, I didn't have nothing against the rain last week when I got out, so Stan gave me the coat. Oh, where did you lose it? I didn't lose it. I never lost nothing, except a little time recently. Yeah, we know about that. I'll figure you did.  Okay. Okay, I said. Right, now, since you didn't lose this raincoat, how is it that we found it? I, uh, I loaned it to a fella. How should I know what he did with it? Oh, who had it? I don't remember. Now, you receive a raincoat as a gift against the rain. Within a matter of days, you'll loan it to someone, but you don't remember to whom. That's right. I don't remember. You try, Sergeant. Yes, sir. Speak up, man. Who had that coat? I don't remember. Well, maybe I can refresh your memory. Your coat's hooked up with a murder. Now, who had it? I don't remember. I don't remember. That was the refrain. I don't remember. How could they make him remember? Finally they let him go. There was nothing to hold him on. Inspector Bowers said... I want a tale on him, Sergeant. 24 hours a day. We'll pick up his friends one by one and see where he leads us. One of those friends was a boy named Rodney Hamilton. Let me alone. I never did nothing. How old are you, Hamilton? 17, and you've got no right. We happen to know that you've been away to a reform school, Hamilton. You were caught snatching ladies' purses two years ago. So what? I did me time. You don't keep very good company, son. Georgetown has been out of prison about a week. Well, we like each other. I'll pick me home, friends. Where were you a day before yesterday? About 2.30 in the afternoon. Home. In bed. Oh, sleeping late, huh? I was sick. Had a fever. We can check that, you know. Go ahead, check it. I was at home in bed. All afternoon, all morning and all night. I was sick. Yes, we know you said that before. Now, look here. Well, it didn't do any good. The boy was obviously afraid. But whether of the police or the many knew it was impossible to tell. Still, the patience of Scotland Yard was rewarded once again. A report came in from detectives assigned to follow both Georgetown and Hamilton. That they'd been present at a party in a bar, given by a third young man named Matty Canvass. Doss her on Canvass, Inspector. Ah, thank you. So, nice lad. Stole a car when he was 11. Convicted assault with intent to rob. Convicted jewel robbery. Convicted armed robbery and assault. Nice little trio. Our three friends, aren't they, Sergeant? No, not exactly. Do you want Canvass brought in, sir? Yes. Do that. Oh, and, uh, Sergeant, bring the other two along. But keep them separated. Don't let any one of them know that we have the other two. Nothing highly dramatic. Nearly routine. Scotland Yard routine. Recognizable as standard, practiced by any policeman in any large city. Bring them in for questioning. But it always helps to have a few facts. Facts on which to base the questions. Who this in mind, and knowing their underworld, Detective Bowers and Sergeant Wood repaired to that certain bar where Matty Canvass held parties for his friends. Nothing much here. Well, there may be. Keep your ears open. Well, another pint of ale, please. Very right, you are, sir. Oh, and now, Larry, let me wait on the door. Sit yourself, Miguel. Sit yourself. Oh, well. Down the hatch, fine. Frequent the bar. Wait. Keep your ears open. You hear they picked up Matty. Yeah. And the other two. Rod two? That kid? Well, I for one ain't sorry. Here's a place of bad name.  Not bright. Canvass ain't bright, that's for sure. It should still get bunked out of 5,000 quids where the stuff gets mad. Works the kid like that. They're rod. Oh, well, so it goes, I will say. Oh, not a couple of pints, please, miss. Oh, yeah, sure. Do you know a lad named Canvass? Me? I don't know nobody. How about a kid named Hamilton? You think I'll keep track of everybody who comes in here? Yeah, that would be difficult. I, um, thought I heard you mention Mr. Canvass by name. So did my friend here. Then you thought wrong. We don't encourage conversation with the elder. No, I don't suppose you do. Um, care to have a drink on us? I'm not certain that I drinks with cops, mister. The inspector and the sergeant have fill up the buyer. They check descriptions of the men seen at hold ups and robberies prior by some months to the tragedy in Queensborough Street. In one case, a description tallied with Matty Canvass. Mr. Canvass has invited out of his cell and into the inspector's office. All right, Canvass, you've been identified on the Davis job. You'll do a good long stretch for that. You may as well tell us what you know about the Queensbury shooting. I don't know nothing. Turn King's evidence, Canvass, and you'll get off easily. Otherwise, it's a hanging matter. You can't pin that one on me. I was miles away. I can prove it.  Rod? Talk? Not in your life. He's too scared somebody will cut his liver out. All right, Canvass, if that's the way you want it, take him back, Sergeant. We'll give him plenty of time to think. No luck with Georgetown either, but the kid has a chance he'd break. Just a chance. Inspector took it.  I never had a gun. Maybe they're framing you, lad. After all, you're pretty young, 17. You won't hang. They will. So naturally, they say it was you. I didn't. I never touched a gun. Canvass says you did. That you had the 32 all the time. I never did. They can't say it. They can't. But they do. Both of them told me. Looks to us like they got together to put the job on you. Well, why shouldn't they? They might have to die. There were only two guns. Stay at them. You've got to believe that. You've got to. We don't have to believe anything except evidence. If you had the guns, if I told you where they were, I said it. I didn't mean to talk. I did. I did. Keep talking, lad. It's the only way to save your neck. My neck? If you held that gun and pulled that trigger. All right. We threw the guns away. Both of them. The .45 Medi-Ed and the .32 Jacket. We threw them in the river from the, from the key at the Lancashire Wharf. If you can find them, you'll see. Maybe they'll be fingerprints. You've got to find them guns. You've got to. The .45 fired the bullet we found in the woodwork. The .32 killed the man in the street. And it's funny. Half the cartridges in the .32 were rimfire. The other half was centerfire.  If they had, chances are there'd have been a lot more murder. And today, if you're interested, you can find that centerfire bullet in a place of honor in the Black Museum. Orson Welles will be back with you in just a moment. It's an accepted fact in police work that 80% of the occupants of our prison either would talk there or talk themselves there. In this case, it was patient police routine, plus the talking of young Rod Hamilton, which put Rod behind bars for the rest of his life and brought Georgetown and Canvas to the 13 steps and the rope one morning at eight o'clock. There's a post script of this story. The use of a gun by an English criminal is rare. Nor in England do the police go armed in their normal course of duty. The Hamilton case was one of the more obvious symptoms of a post war disease of violence. The death of Georgetown and Canvas was not about its effect on this disease. For days, weeks, and even months after their execution, pistols, guns, knives, many of them perhaps innocent wartime souvenirs, others of more dubious origin, were found by the police on rubbish heaps, disused gardens on the mud banks of the River Thames. The criminal world of London had come to terms with their traditional enemies at Scotland Yard, and perhaps after all, the innocent man had not died in vain. The bullet that killed him, the Centifier bullet, remains in its customary place in the Black Museum. And now, until we meet next time in the same place, I'll tell you another story about the Black Museum. Our main is always obediently yours.