Real-time multi-object detection has been an elusive goal of automated target recognition (ATR) scenarios that employ on- board millimeter-scale processors at low light levels and reduced power requirements. This paper discusses an adaptation of the wedge-and -strip anode to yield design analyses for a silicon wedge-and-strip detector (WESD). In practice, a WESD could compute the centroid of an incident light-spot and, with appropriate prefiltering can locate centroids of multiple light-spots. Due to on-chip processing and a nondemanding geometry, the WESD approach drastically reduces electronics fabrication complexity and cost. Based on results of preliminary design and analysis, significant cost reductions over existing massively parallel vision processors (MPVPs) are foreseen for object location via centroid computation. Algorithms and simulations are expressed in image algebra, a rigorous, concise, computationally complete notation that unifies linear and nonlinear mathematics in the image domain. Developed at University of Florida over the past decade, image algebra is a unifying language for image and signal processing that has been implemented on numerous workstations and parallel computers. Thus, our algorithms are rigorous and widely portable. Circuit analysis emphasizes effects of capacitance and electrode configuration on WESD spatial resolution. Simulation results show that the WESD has centroid computation accuracy comparable to equivalent-resolution array detectors supported by on-board MPVPs. Additional technical discussion pertains to the feasibility of space- division multiplexing for multi-object detection and location at video rates.