Top-down Processing in Visual Perception Part III: Artificial Edges

This was supposed to be the concluding chapter in my series on top-down visual processing started in part one and continued in part two, but it got quite a bit longer than I expected and will thus be expanded in an upcoming fourth part. In the first installment I introduced the concept of top-down and bottom-up processing and gave a low-level example of top-down processing in the Necker Cube. In the second part I discussed faces in the context both of anthropomorphizing objects through the visualization of faces as well as a preponderance of optical illusions involving faces.

In this installment I am going to discuss another artifact of top-down visual processing which I am going to call artificial edges (I'm not actually sure if there is a better or more technical term for it, so if anyone knows of one, feel free to let me know). I find this phenomenon interesting from both a physiological aspect (in terms of providing evidence for top-down processing) as well as a machine vision aspect (in terms of duplicating our object recognition abilities). The basic idea is that our brain is fairly good at joining edges which belong to the same object but which have been in some manner obscured (either through occlusion, camouflage, or illumination problems). What is interesting, however, is that our visual processing system is so good at this that we can actually create edges and object boundaries that are not there. Two classic examples of this are shown in figures 1 and 2.

Figure 1

Figure 2

There is not actually a white square in figure 1, but the pieces removed from the black circles give the illusion of a white shape occluding them. Our mind then fills in the boundaries of the square to separate it from the white background to the point where we are able to discern boundary lines that are not actually there (of course, those lines disappear when one focuses on them since there isn't actually a change in hue). Figure 2 shows a somewhat more complicated version of the same phenomenon in which the image of a dalmatian is hidden within the scattered ink blots. The artificial edges created in these images are a consequence of our ability to group objects together and differentiate foreground from background. Part of our ability to do this rests in our expectations of what objects are most likely to appear in an image as well as our expectation of how such objects might be arranged. If figure 2 were shown to an individual who had never seen a dog (or even a dalmatian or similarly coloured dog), he would most likely have a very difficult (if not impossible) time spotting it.

That said, I am going to make a brief digression and point out one of the things that makes psychology such an annoying subject - differences in individual processing. Just as some people mentioned they had a difficult time spotting the old lady in the previous part, my girlfriend told me she does not see four circles occluded by a white square in the first image, but rather she sees four Pac-men biting a square. At the least the square is still there so she doesn't completely spoil my premise.

This ability to group contours and blobs together into expected objects is a massive advantage when it comes to image understanding, and it is one of the biggest problems in machine vision. Outside of tightly controlled circumstances, object contours rarely display consistent properties. This is hard for people to even spot, because our mind automatically accentuates valid contours and minimizes invalid contours as shading and texture.

Figure 3

To demonstrate this, I have included a picture I pulled off the internet of a chrome stapler (figure 3). As one can see, there are plenty of strong and weak edges in this image (when I speak of the strength of an edge, I mean roughly the rate at which pixel intensity changes. For a more thorough explanation, see the Wikipedia article). People have no difficulty picking out the stapler in this image and could easily outline the object if one were to ask, despite the fact that this is a monochromatic image and several of the boundary edges are much weaker than internal edges caused by shadow and geometric variations in the object's surface. For example, if you look at the two rearmost edges of the stapler, you can see that the posterior edge is virtually nonexistent while the lateral edge starts fairly strong near the bulb at the front of the stapler but fades as one moves toward the posterior. Our minds have no problem mentally accentuating that lateral edge along its entire length, however, and recognizing that it is a continuous edge despite its vast variation in edge strength. If you looks at the opposite side, however, you see a continuous dark band that extends the length of the stapler, forming two powerful edges. Neither of these strong edges actually depicts one of the object's boundaries, rather they are an artifact of the object's geometry, the view angle, and lighting. Thus, even a computer system whose sole purpose is to determine if one has a picture of a stapler or not would have a great deal of difficulty with that task without some pretty hefty processing on top of the edge detection (even then it would highly unlikely to be as reliable as a person, and we can recognize far more objects than just staplers) or some ability to constrain the view angle, lighting, and object variation. A great deal of these concepts are actually discussed in Gestalt psychology (if you follow the link to the Wikipedia article, you will see some familar images too. It looks like I could have just acquired links from there rather than searching randomly through the internet if I had looked at Wikipedia earlier).

I had planned on speaking about the ramifications of what I have discussed, but I have already been working on this post on and off for several weeks and it is starting to get cumbersome in length. I will therefore be expanding this series into a fourth post to be published in the not too distant future.

Continue reading in Part IV: Ramifications.