Underwater binocular detection is a detection method using binocular stereo vision. Binocular stereo vision is increasingly used in robot vision, aerial mapping, reverse engineering, military applications, medical imaging and industrial detection. The AUV (Autonomous Underwate Vehicle) equipped with binocular optical system restores the real underwater three-dimensional world through binocular stereo detection results, and becomes an important means for underwater resource detection and underwater target search. Due to the imaging characteristics of underwater images, there are a large number of weak texture and repeated texture areas, and the current underwater stereo matching algorithm has poor matching effect in these areas. To solve this problem, this paper proposes a multi-cost fusion BP(belief propagation) algorithm based on image segmentation information to improve the matching effect of such regions. Experimental results of Middlebury data and real underwater binocular image data show that the proposed algorithm improves the matching effect of weak texture and non-texture regions.
A technique for calibrating the spectral radiance responsivity of the CCD imaging spectrometer with an internally
illuminated integrating sphere is described. The spectral radiance of the integrating sphere is obtained by two steps.
Firstly, a Spectralon panel diffuser and an ultraviolet spectrometer are combined into a new spectral radiometer which
transfers the spectral irradiance of a NIST standard of spectral irradiance to that of the receiving aperture of the
integrating sphere. Subsequently, the spectral radiance of the integrating sphere is derived from heat transfer theory for
Lambertian radiators. The overall uncertainty of determining the spectral radiance of the integrating sphere is ±2.3%. On
the basis of known spectral radiance, the radiance calibration of an available Czerny-Turner imaging spectrometer in our
laboratory has been completed in 200-400nm with an uncertainty of about ±2.7%.
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