The general problem of computer vision has been investigated for more than twenty years and is still one of the most challenging fields in artificial intelligence. Indeed taking a look at the human visual system can give us an idea of the complexity of any solution to the problem of visual recognition. This general task can be decomposed into a whole hierarchy of problems ranging from pixel processing to high level segmentation and complex objects recognition. Contrasting an image at different representations provides useful information such as edges. First, we introduce an example of low level signal and image processing using the theory of wavelets which provides the basis for multiresolution representation. Like the human brain, we use a multiorientation process which detects features independently in different orientation sectors. So, we end up contrasting images of the same orientation but of different resolutions to gather information about an image. We then develop an interesting image representation using energy zero crossings. We show that this representation is experimentally complete and leads to some higher level applications such as edge and corner finding, which in turn provides two basic steps to image segmentation. We also discuss the possibilities of feedback between different levels of processing.