Localization of chess-board vertices is a common task in computer vision, underpinning many applications, but relatively little work focusses on designing a specific feature detector that is fast, accurate and robust. In this paper the "Chess-board Extraction by Subtraction and Summation" (ChESS) feature detector, designed to exclusively respond to chess-board vertices, is presented. The method proposed is robust against noise, poor lighting and poor contrast, requires no prior knowledge of the extent of the chessboard pattern, is computationally very efficient, and provides a strength measure of detected features. Such a detector has significant application both in the key field of camera calibration, as well as in Structured Light 3D reconstruction. Evidence is presented showing its robustness, accuracy, and efficiency in comparison to other commonly used detectors both under simulation and in experimental 3D reconstruction of flat plate and cylindrical objects.

In this paper we present an evolution of the single-pixel camera architecture, called 'pushframe', which addresses the limitations of pushbroom cameras in space-based applications. In particular, it is well-suited to observing fast moving scenes while retaining high spatial resolution and sensitivity. We show that the system is capable of producing colour images with good fidelity and scalable resolution performance. The principle of our design places no restriction on the spectral range to be captured, making it suitable for wide infrared imaging.

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Pushframe parallellized single pixel camera imaging utilizes scanning motion to apply linear sampling masks to rapidly compressively sense a scene. We demonstrate strongly performing static binarized noiselet mask designs, tailored for pushframe hardware.