The angle of view under which a scene is acquired and the distance of the objects from the camera (namely the perspective effect) contribute to associate a different information content to each pixel of an image. The perspective effect in fact must be taken into account when processing images in order to weigh each pixel according to its information content; this differentiate processing turns the use of a SIMD machine, such as the MMX based computers, to a knotty problem.
To cope with this problem a geometrical transform (Inverse Perspective Mapping, IPM) has been introduced; it allows to remove the perspective effect from the acquired image, remapping it into a new 2-dimensional domain (the remapped domain) in which the information content is homogeneously distributed among all pixels, thus allowing the efficient implementation of the following processing steps with a SIMD paradigm. Obviously the application of the IPM transform requires the knowledge of the specific acquisition conditions (camera position, orientation, optics,...) and some assumption on the scene represented in the image (here defined as a-priori knowledge). Thus the IPM transform can be of use in structured environments, where, for example, the camera is mounted in a fixed position or in situations where the calibration of the system and the surrounding environment can be sensed via other kind of sensor.
Assuming the road in front of the vision system is planar, the use of IPM allows to obtain a bird's eye view of the scene (fig. 2).