To overcome the limitations of conventional long-focus camera calibration, this paper proposes an improved algorithm for calibrating multi-perspective long-focus cameras based on the parallel perspective projection model. The algorithm employs an affine approximation projection model to describe the imaging process of long-focus cameras. By capturing images of the same static scene from different viewpoints and utilizing multi-view images and bundle adjustment, the algorithm jointly estimates camera parameters, including focal length, distortion coefficients, and rotation matrices, facilitating rapid, flexible, and accurate calibration of long-focus cameras. Finally, the proposed algorithm is compared with the Zhang Zhengyou calibration algorithm by calibrating a Canon EOS 60D18-200 camera with a 100mm focal length. The precision and stability of the proposed algorithm are verified through comparison, laying a foundation for subsequent tasks in computer vision, such as 3D reconstruction and image fusion.
Lunar dust is widely distributed on the lunar surface as tiny particles ranging from 30 nm to 20 μm. Currently, extensive research has been conducted on the physical, chemical, and hazardous properties of lunar dust. However, few studies focused on its motion patterns and related mechanisms. This paper aims to utilize non-contact imaging method for the first time to record and analyze the trajectory and velocity of lunar dust movement. The fundamental principle of this paper is to use a high-sensitivity camera to image the dust particles in the detection area within a certain exposure time, and to obtain the intensity of the scattered light from the particles in the detection area at that exposure time based on the grayscale value. In terms of velocity measurement, the spatial motion trajectory of lunar dust was recorded by the single frame multi-exposure imaging method, and the related algorithm based on gray value distribution of the motion trajectory image was used to calculate its velocity. In considering the small particle size of lunar dust would cause weak light scattering energy, during the construction of the principle prototype, the strong pulse laser technology was used to increase the illumination, and the sCMOS camera with a far-field optical lens was adopted to improve the signal detection sensitivity. The results showed that the prototype can identify particles from 0.3 μm to 20 μm, measure velocity from 1m/s to 100m/s, and the minimum detectable dust concentration is 1000pc/ft3. This study provides technical reserves for lunar dust environment exploration in future lunar exploration projects.
For the demand of inland water quality monitoring, a ground-based multi-spectral imaging method has been developed. By means of developing an instrument which can gather the multi-spectral data of the waterbody, the method can be used for real-time monitoring the contamination of inland waters, such as cyanobacteria bloom and phytoplankton. The research is focused on the technology of high-resolution multi-spectral data extraction and the theory of contaminant inversion model. Four branches of light beams of simple spectrum are obtained with spectral filters and are recorded by four groups of lens and detectors respectively. The four interested wavelength is chosen as 565 nm, 620 nm, 660 nm, 750 nm, according to the typical reflection peaks and dips of the contamination with a spectral resolution of 15 nm. The optical design features a field of view of 25.2×19.3 degree with a 16mm focal lens. The camera’s resolution is 1628×1236 with the pixel size of 4.4 microns that reaches the spatial resolution of 0.945 arc min. The multi-spectral image is obtained through out-door experiments by monitoring the inland lake—Dianchi at a distance of 5 kilometers. After data revision, we can identify the constituent of the underwater contaminant and explain the pollution situation of cyanobacteria bloom in a certain period quantitatively. The inversion and extraction accuracy can reach at least 85%. And the long-term observation can explore the seasonal pattern of cyanobacteria bloom outbreak.
In this paper, a method based on spectral clustering and the discrete wavelet transform (DWT) is proposed, which is based on the problem of the high degree of space-time redundancy in the current multispectral image compression algorithm. First, the spectral images are grouped by spectral clustering methods, and the clusters of similar heights are grouped together to remove the redundancy of the spectra. Then, wavelet transform and coding of the class representative are performed, and the space redundancy is eliminated, and the difference composition is applied to the Karhunen-Loeve transform (KLT) and wavelet transform. Experimental results show that with JPEG2000 and upon KLT + DWT algorithm, compared with the method has better peak signal-to-noise ratio and compression ratio, and it is suitable for compression of different spectral bands.
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