The QUEST large area CCD camera

We have designed, constructed, and put into operation a very large area CCD camera that covers the field of view of the 1.2 m Samuel Oschin Schmidt Telescope at the Palomar Observatory. The camera consists of 112 CCDs arranged in a mosaic of four rows with 28 CCDs each. The CCDs are 600 x 2400 pixel Sarnoff thinned, back-illuminated devices with 13 µm x 13 µm pixels. The camera covers an area of 4.6° x 3.6° on the sky with an active area of 9.6 deg_2. This camera has been installed at the prime focus of the telescope and commissioned, and scientific-quality observations on the Palomar-QUEST Variability Sky Survey were started in 2003 September. The design considerations, construction features, and performance parameters of this camera are described in this paper

Design, Implementation and Evaluation of Hardware Vision Systems Dedicated to Real-Time Face Recognition

Human face recognition is an active area of research spanning several disciplines such as image processing, pattern recognition, and computer vision. Most researches have concentrated on the algorithms of segmentation, feature extraction, and recognition of human faces, which are generally realized by software implementation on standard computers. However, many applications of human face recognition such as human-computer interfaces, model-based video coding, and security control (Kobayashi, 2001, Yeh & Lee, 1999) need to be high-speed and real-time, for example, passing through customs quickly while ensuring security. For the last years, our laboratory has focused on face processing and obtained interesting results concerning face tracking and recognition by implementing original dedicated hardware systems. Our aim is to implement on embedded systems efficient models of unconstrained face tracking and identity verification in arbitrary scenes. The main goal of these various systems is to provide efficient robustness algorithms that only require moderated computation in order 1) to obtain high success rates of face tracking and identity verification and 2) to cope with the drastic real-time constraints. The goal of this chapter is to describe three different hardware platforms dedicated to face recognition. Each of them has been designed, implemented and evaluated in our laboratory

FPGA-based real-time moving target detection system for unmanned aerial vehicle application

Moving target detection is the most common task for Unmanned Aerial Vehicle (UAV) to find and track object of interest from a bird's eye view in mobile aerial surveillance for civilian applications such as search and rescue operation. The complex detection algorithm can be implemented in a real-time embedded system using Field Programmable Gate Array (FPGA). This paper presents the development of real-time moving target detection System-on-Chip (SoC) using FPGA for deployment on a UAV. The detection algorithm utilizes area-based image registration technique which includes motion estimation and object segmentation processes. The moving target detection system has been prototyped on a low-cost Terasic DE2-115 board mounted with TRDB-D5M camera. The system consists of Nios II processor and stream-oriented dedicated hardware accelerators running at 100 MHz clock rate, achieving 30-frame per second processing speed for 640 × 480 pixels' resolution greyscale videos

Stereoscopic wide field of view imaging system

A stereoscopic imaging system incorporates a plurality of imaging devices or cameras to generate a high resolution, wide field of view image database from which images can be combined in real time to provide wide field of view or panoramic or omni-directional still or video images

Forum Bildverarbeitung 2022

Bildverarbeitung verknüpft das Fachgebiet die Sensorik von Kameras – bildgebender Sensorik – mit der Verarbeitung der Sensordaten – den Bildern. Daraus resultiert der besondere Reiz dieser Disziplin. Der vorliegende Tagungsband des „Forums Bildverarbeitung“, das am 24. und 25.11.2022 in Karlsruhe als Veranstaltung des Karlsruher Instituts für Technologie und des Fraunhofer-Instituts für Optronik, Systemtechnik und Bildauswertung stattfand, enthält die Aufsätze der eingegangenen Beiträge

Compact, modular and in-plane AOSLO for high-resolution retinal imaging

The adaptive optics scanning laser ophthalmoscope (AOSLO) was first developed in 2002 and since then the technology has been adopted in several laboratories around the world, for both clinical and psychophysical research. There have been a few major design implementations of the AOSLO. The first used on-axis tilted spherical mirrors in a planar arrangement, and the second minimized the build up of astigmatism present in the first design by using a non-planar arrangement. Other designs have avoided astigmatism by using custom-made toroidal mirrors or by using lenses on-axis, rather than mirrors. We present a new design implementation for an AOSLO that maintains a planar optical alignment without the build up astigmatism using compact, reconfigurable modules based on an Offner relay system. We additionally use an off-the-shelf digital oscilloscope for data capture and custom-written Python code for generating and analyzing the retinal images. This design results in a compact system that is simple to align and, being composed of modular relays, has the potential for additional components to be added. We show that this system maintains diffraction-limited image quality across the field of view and that cones are clearly resolved in the central retina. The modular relay design is generally applicable to any system requiring one or more components in the pupil conjugate plane. This is likely to be useful for any point-scanned system, such as a standard scanning laser ophthalmoscope or non-ophthalmic confocal imaging system