Why is computer vision so difficult? Because it is the difference between seeing and perceiving.
Modern cameras can see the world nearly as good as we humans do, but they are not able to comprehend what they see. It is like me looking at a piece of modern art. I can see it, but I sure won’t understand it. I can identify lines, squares and other shapes, but that wont help me uncover the meaning of the picture. (I know, that modern art is not only about comprehending and the meaning of the pictures, but I think you get my point.)
But computers don’t even know the concept of squares or any other shapes. They have to learn from examples or be taught by humans. But how do you describe a circle, so that a computer can recognise it, even when it is the wheel of a bike leaning against a wall? And I can look at hundreds of pictures of modern art, but that wont help me if nobody tells me what (features) to look for. And writing algorithms to automatically extract the right features from pictures to help the computer recognise scenes, objects and people is what the researchers in computer vision are working on.
Of course there are also other topics. Fusing image data with laser rangefinders is important for the field of robot vision and in image retrieval they are also interested on features that describe the technical or aesthetic quality of the pictures. Not per se vision, but still always important are faster and more accurate learning algorithms and optimization of the algorithms involved. Image processing is always very cpu time and memory expensive.