Low-light stereo vision is a challenging problem because images captured in dark environment usually suffer from strong random noises. Some widely adopted algorithms, such as semiglobal matching, mainly depend on pixel-level information. The accuracy of local feature matching and disparity propagation decreases when pixels become noisy. Focusing on this problem, we proposed a matching algorithm that utilizes regional information to enhance the robustness to local noisy pixels. This algorithm is based on the framework of ADCensus feature and semiglobal matching. It extends the original algorithm in two ways. First, image segmentation information is added to solve the problem of incomplete path and improve the accuracy of cost calculation. Second, the matching cost volume is calculated with AD-SoftCensus measure that minimizes the impact of noise by changing the pattern of the census descriptor from binary to trinary. The robustness of the proposed algorithm is validated on Middlebury datasets, synthetic data, and real world data captured by a low-light camera in darkness. The results show that the proposed algorithm has better performance and higher matching rate among top-ranked algorithms on low signal-to-noise ratio data and high accuracy on the Middlebury benchmark datasets.
Small anomaly detection in ocean evironment is an important problem in airborne remote sensing image processing, especially in hyperspectral data. Traditional algorithms solve this problem by finding the pixels have different appearance pattern with the background. However, these algorithm are not suitable for real-time applications. In this paper, we propose to learn the hyperspectral model of the seawater and localize the targets whose spectral feature do not well fit the trained model. This algorithm only uses historical information and is suitable to be used on airborne line-scanning data. Since hyperspectral property of ocean water is relatively stable, we use Gaussian mixture model to encode the statistical features of the background. Experimental results demonstrated that the proposed algorithm significantly improves processing efficiency in comparison with conventional methods, and maintains high accuracy with regard to other methods.
Target detection and tracking important in many applications including intelligent monitoring system, defense system and terminal guidance system. Aiming to solve the problem of simulated target tracking, this paper proposes an adaptive algorithm which uses the fusion of the spectral and morphological features of multispectral image to realize the target tracking based on the Particle Filter. Firstly, the target area is manually initialized in the multispectral image and the spectral and texture features of the target are extracted. Secondly, we build the adaptive tracking model of multiple features under the framework of Particle Filter. We validate the effectiveness of the proposed approach on the MATLAB platform. The results show that the proposed approach achieves accurate and stable multispectral target tracking in complex scenes by improving the efficiency of particles usage under defective tracking conditions, which is of great theoretical and practical values for the application of multispectral target tracking technology.
Using coated mosaic video spectrometer to collect multispectral image which reduce the spectral information redundancy and data volume greatly and achieve real-time data transmission conditions. The mosaic video spectrometer imaging technique use a similar mosaic template to capture all the pixels and output a two-dimensional multi-spectral image with dozens of spectral information. The image is divided into a certain size of matrix in its field, and each pixel in the pixel matrix is only for one wavelength information response and every pixel response for different wavelength. The size of the pixel matrix block depends on the number of spectral segments, which results in a low spatial resolution of the single spectral segment image and the spectral information of each pixel absenting severely. Therefore, to reconstruct the complete multi-spectral image, we must estimate and interpolate the missing spatial information and spectral information by demosaicking multispectral image. In this paper, we present a novel demosaicking method to produce the high resolution multispectral image and reconstruct missing spectrum information in high accuracy. The proposed method computes the first-and second-order derivatives of the original single multispectral image to measure the geometry of edges in the image and the spectrum value of missing pixel. Two metrics are used to evaluate the generic algorithm, including the structural similarity index-measurement system (SSIM) for reconstruction performance and the procession time. Experimental results show that the demosaicked images present higher SSIM (more than 0.9) and comparable calculated time performance as traditional ways. This algorithm brings the greatest advantage that make up for the weakness of mosaick multispectral image and reduce the data transmission process cost and storage needs.
With the development of computer vision and image processing technology, vision measurement has been paid more and more attention. In the aviation field, estimating the relative attitude of aircraft using computer vision is important in aircraft flight-refueling, target tracking and positioning. However, the existing methods to measure the attitude of aircraft have some problems. In this paper, we propose to use binocular vision measurement method to acquire the attitude data of aircraft. This method has the advantages of simple realization and high practical value, which can also be widely used in visional measurement applications.
Large aperture static interferometer spectrometer (LASIS) use the method of push-boom to get the geometric and spectral characteristics of ground target, the particularity of principle requires the movement of satellite must be in the same direction with spectrometers detectors. Drift angle of satellite leading to abnormal image shifts in the column direction which should be perpendicular to the detector and can seriously affect the spectrum recovery precision of collected data. This paper analyzes the influence mechanism of drift angle for spectrum recovery precision. Simulation based on the actual on-orbit data analyses the effects of different drift angle of relative mean deviation and relative secondary deviation rehabilitation of the spectrum, besides the influence of spectral angle similarity. These studies have shown that, when the lateral deviation due to the drift angle on the across track is less than 0.3 pixel, the effect for the relative mean deviation of the inversive spectra will be no more than 7%. when the lateral deviation due to the drift angle on the across track is larger than one pixel, even though the resampling correction is proceeded, the restored spectral data cube still shows an relative mean error more than 10%, which seriously affect the availability of spectral data.
We propose an approach to correct the data of the airborne large-aperture static image spectrometer (LASIS). LASIS is a kind of stationary interferometer which compromises flux output and device stability. It acquires a series of interferograms to reconstruct the hyperspectral image cube. Reconstruction precision of the airborne LASIS data suffers from the instability of the plane platform. Usually, changes of plane attitudes, such as yaws, pitches, and rolls, can be precisely measured by the inertial measurement unit. However, the along-track and across-track translation errors are difficult to measure precisely. To solve this problem, we propose a co-optimization approach to compute the translation errors between the interferograms using an image registration technique which helps to correct the interferograms with subpixel precision. To demonstrate the effectiveness of our approach, experiments are run on real airborne LASIS data and our results are compared with those of the state-of-the-art approaches.