Recognition and photogrammetric tasks require models capable of detecting and measuring complex features normally found in all kind of natural and artificial scenes. Corners are special features in images, and are of great use in computing camera calibration, tracking and reconstruction. Basically, a corner is defined as the junction point of two or more straight-line edges. Previous methods devoted to corner detection are based on parametric models. However, a drawback from previous approaches is the cumbersome of the proposed models. This paper presents a new multi-corner detector based on a unit step edge function (USEF) that defines a straight line edge. Several USEFs are combined to produce complex corners using simple operators as addition and multiplication. As well as previous methods we search model parameters characterizing completely single gray-value structures by means of least squares fit of the model to the observed image intensities. Examples and experimental results illustrate the quality and efficacy of the detectors.