This chapter addresses primary visual perception, detailing how visual information comes about and, as a consequence, which visual properties provide particularly useful information about the environment. The brain extracts this information systematically, and also separates redundant and complementary visual information aspects to improve the effectiveness of visual processing. Computationally, image smoothing, edge detectors, and motion detectors must be at work. These need to be applied in a convolutional manner over the fixated area, which are computations that are predestined to be solved by means of cortical columnar structures in the brain. On the next level, the extracted information needs to be integrated to be able to segment and detect object structures. The brain solves this highly challenging problem by incorporating top-down expectations and by integrating complementary visual information aspects, such as light reflections, texture information, line convergence information, shadows, and depth information. In conclusion, the need for integrating top-down visual expectations to form complete and stable perceptions is made explicit.