Human eyes are made of gel-like material. It is interesting then, that together with a 3-pound brain composed predominantly of fat and water, we are capable of the feat of vision. Yes, we don’t have super-vision, and aren’t capable of zooming in on objects in the distance, but our eyes are magical. Eyes are able to focus instantaneously, and at objects as closer as 10cm, and as far away as infinity. They also automatically adjust for various lighting conditions. Our vision system is quickly able to decide what an object is and perceive 3D scenes.
Computer vision algorithms have made a lot of progress in the past 40 years, but they are by no means perfect, and in reality can be easily fooled. Here is an image of a refrigerator section in a grocery store in Oslo. The context of the content within the image is easily discernible. If we load this image into “Google Reverse Image Search” (GRIS), the program says that it is a picture of a supermarket – which is correct.
Now what happens if we blur the image somewhat? Let’s say a Gaussian blur with a radius of 51 pixels. This is what the resulting image looks like:
The human eye is still able to decipher the content in this image, at least enough to determine it is a series of supermarket shelves. Judging by the shape of the blurry items, one might go so far to say it is a refrigerated shelf. So how does the computer compare? The best it could come up with was “close-up”, because it had nothing to compare against. The Wolfram Language “Image Identification Program“, (IIP) does a better job, identifying the scene as “store”. Generic, but not a total loss. Let’s try a second example. This photo was taken in the train station in Bergen, Norway.
GRIS identifies similar images, and guesses the image is “Bergen”. Now this is true, however the context of the image is more related to railway rolling stock and the Bergen station, than Bergen itself. IIP identifies it as “locomotive engine”, which is right on target. If we add a Gaussian blur with radius = 11, then we get the following blurred image:
Now GRIS thinks this scene is “metro”, identifying similar images containing cars. It is two trains, so this is not a terrible guess. IIP identifies it as a subway train, which is a good result. Now lets try the original with Gaussian blur and a radius of 21.
Now GRIS identifies the scene as “rolling stock”, which is true, however the images it considers similar involve cars doing burn-out or stuck in the snow (or in one case a rockhopper penguin). IIP on the other hand fails this image, identifying it as a “measuring device”.
So as the image gets blurrier, it becomes harder for computer vision systems to identify, whereas the human eye does not have these problems. Even in a worst case scenario, where the Gaussian blur filter has a radius of 51, the human eye is still able to decipher its content. But GRIS thinks it’s a “photograph” (which *is* true, I guess), and IIP says it’s a person.