Abstract

The thermal infrared imager TIR onboard Hayabusa2 has been developed to investigate thermo-physical properties of C-type, near-Earth asteroid 162173 Ryugu. TIR is one of the remote science instruments on Hayabusa2 designed to understand the nature of a volatile-rich solar system small body, but it also has significant mission objectives to provide information on surface physical properties and conditions for sampling site selection as well as the assessment of safe landing operations. TIR is based on a two-dimensional uncooled micro-bolometer array inherited from the Longwave Infrared Camera LIR on Akatsuki (Fukuhara et al., 2011). TIR takes images of thermal infrared emission in 8 to 12 ?m with a field of view of \\(16 \\times 12^{\\circ}\\) and a spatial resolution of \\(0.05^{\\circ}\\) per pixel. TIR covers the temperature range from 150 to 460 K, including the well calibrated range from 230 to 420 K. Temperature accuracy is within 2 K or better for summed images, and the relative accuracy or noise equivalent temperature difference (NETD) at each of pixels is 0.4 K or lower for the well-calibrated temperature range. TIR takes a couple of images with shutter open and closed, the corresponding dark frame, and provides a true thermal image by dark frame subtraction. Data processing involves summation of multiple images, image processing including the StarPixel compression (Hihara et al., 2014), and transfer to the data recorder in the spacecraft digital electronics (DE). We report the scientific and mission objectives of TIR, the requirements and constraints for the instrument specifications, the designed instrumentation and the pre-flight and in-flight performances of TIR, as well as its observation plan during the Hayabusa2 mission. Document type: Part of book or chapter of book

Abstract

Proceeding of: Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005 Computer Vision can provide a great deal of assistance to Intelligent Vehicles. In this paper an Advanced Driver Assistance Systems for Vehicle Detection is presented. A geometric model of the vehicle is defined where its energy function includes information of the shape and symmetry of the vehicle and the shadow it produces. A genetic algorithm finds the optimum parameter values. As the algorithm receives information from a road detection module some geometric restrictions can be applied. A multi-resolution approach is used to speed up the algorithm and work in realtime. Examples of real images are shown to validate the algorithm. Publicado Document type: Part of book or chapter of book