Binocular stereo from Grey-Scale images

Grey-scale images consist of physical measurements of light. Scale-space th eories have been developed to unconfound these measurements from the detect or grid. In this framework, we look into the problem of binocular stereo. O n a sufficiently large scale, a pixel carries information not only of the g rey-value, but of the entire grey-value n-jet, i.e., derivatives up to orde r n. The subject of this paper is to show, in a general context, how the sc ale-space n-jet can be exploited for binocular matching. The analysis leads (under appropriate assumptions) to a direct determination of the local n-j et of the disparity field. The general result is an analysis which could be incorporated into many existing stereo algorithms to improve their use of the grey value data. In the computational scheme presented here, the estima tions are strictly local, but based on image derivatives at a scale where t he image structure is significant. This scale is automatically selected by minimising computational uncertainty. Results are shown as direct computati ons of surface normals on synthetic and real images.