A (Clear) Line Between Computer Vision and Image Processing
I believe Gonzalez and Woods are competent enough to rely on their opinion:


  There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vision, start. Sometimes a distinction is made by defining image processing as a
  discipline in which both the input and output of a process are images.We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields a single number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence (AI) whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis (also called image understanding) is in between image processing and computer vision.


So I would say, that primary difference is in goals, not methods. For example, if the goal is to enhance image for later use by humans, than this may be called image processing. And if the goal is to emulate human vision (be it object recognition, defect detection or automatic driving), then it is closer to computer vision. Note, however, that emulating human vision by definition may also require image enhancement, so in most real cases computer vision relies on image processing. 

Image understanding (feature extraction) may be equally used in both - pure image processing and computer vision.