The automatic detection of visually salient information from abundant video imagery is crucial, as it plays an important role in surveillance and reconnaissance tasks for Unmanned Aerial Vehicle (UAV). A real-time approach for the detection of salient objects on road, e.g. stationary and moving vehicle or people, is proposed, which is based on region segmentation and saliency detection within related domains. Generally, the traditional method specifically depends upon additional scene information and auxiliary thermal or IR sensing for secondary confirmation. However, this proposed approach can detect the interesting objects directly from video imagery captured by optical camera fixed on the small level UAV platform. To validate this proposed salient object detection approach, the 25 Hz video data from our low speed small UAV are tested. The results have demonstrated the proposed approach performs excellently in isolated rural environments.
The high portability of small Unmanned Aircraft Vehicles (UAVs) makes them play an important
role in surveillance and reconnaissance tasks, so the military and civilian desires for UAVs are
constantly growing. Recently, we have developed a real-time video exploitation system for our small
UAV which is mainly used in forest patrol tasks. Our system consists of six key models, including
image contrast enhancement, video stabilization, mosaicing, salient target indication, moving target
indication, and display of the footprint and flight path on map. Extensive testing on the system has
been implemented and the result shows our system performed well.
KEYWORDS: 3D image processing, 3D metrology, Projection systems, Stereoscopic cameras, Cameras, Photogrammetry, 3D image reconstruction, Imaging systems, 3D modeling, 3D imaging standards
Fast and reliable three-dimensional (3-D) measurement of large stack yards is an important job in bulk load-and-unload operations and logistics management. Traditional noncontacting methods, such as LiDAR and photogrammetry, witness difficulties of complex and irregular shape, single texture and weak reflectivity, and so on. In this paper, we propose a videogrammetry and projected-contour scanning method. The surface of a stack yard can be scanned easily by a laser-line projector, and its 3-D shape can be reconstructed automatically by stereo cameras. There are two main technical contributions of this method: 1. corresponding-point matching in stereo imagery based on image gradient and epipolar line; and 2. single projected-contour extraction under constraint of homography and RANSAC (random sampling consensus). The proposed method has been tested by 3-D-reconstruction experiments of sand tables in indoor and outdoor conditions, which showed that about five contours were reconstructed per second on average, and moving-distance error of a standard slab was less than 0.4 mm in the worst direction of the videogrammetric system. In conclusion, the proposed method is effective for 3-D shape measurement of stack yards in a fast, reliable and accurate way.
As a rising navigation technology, vision navigation has many advantages, such as passive measurement, antiinterference,
no accumulation of error and comprehensive parameters, etc. It shows a promising application prospects in
autonomous navigation for UAV. Based on an efficient, reliable and accurate scene matching, a vision altimeter and 3-D
position estimation method are proposed. By matching multiple points between aerial image and reference image, it
estimates UAV's position and height according to photogrammetry. To measure UAV's velocity, a mapless speed
measurement method which tracks ground features between image sequences is introduced. Flight tests had shown the
effectiveness and accuracy of our methods.
KEYWORDS: 3D metrology, 3D modeling, 3D image processing, Projection systems, Cameras, 3D image reconstruction, Stereoscopic cameras, Calibration, Photogrammetry, Reflectivity
Fast and accurate 3D measurement of large stack-yard is important job in bulk load-and-unload and logistics
management. Stack-yard holds its special characteristics as: complex and irregular shape, single surface texture and low
material reflectivity, thus its 3D measurement is quite difficult to be realized by traditional non-contacting methods, such
as LiDAR(LIght Detecting And Ranging) and photogrammetry. Light-section is good at the measurement of small
bulk-flow but not suitable for large-scale bulk-yard yet. In the paper, an improved method based on stereo cameras and
laser-line projector is proposed. The due theoretical model is composed from such three key points: corresponding point
of contour edge matching in stereo imagery based on gradient and epipolar-line constraint, 3D point-set calculating for
stereo imagery projected-contour edge with least square adjustment and forward intersection, then the projected
3D-contour reconstructed by RANSAC(RANdom SAmpling Consensus) and contour spatial features from 3D point-set
of single contour edge. In this way, stack-yard surface can be scanned easily by the laser-line projector, and certain
region's 3D shape can be reconstructed automatically by stereo cameras on an observing position. Experiment proved the
proposed method is effective for bulk-yard 3D measurement in fast, automatic, reliable and accurate way.
In the procedure of multi-source image registration, the invariance between image intensity is not existed. So, nearly all
the methods based on the grey level face many problems. Even on visible satellite images, the problem also exists when
the images were taken in different season or at different time. Point Pattern Relaxation matching as a method to match
features is a promising way, because only the localization of feature point is used. As known, in a plane, any point's
coordinate can linear described with other three non-collinear point' coordinate, and the linear description is invariant to
affine transformation. This paper brings forward a new method to solve the problem of registration between two point
patterns with affine transformation. Badly effect brought by "disturbed points" is removed through changing the way to
compute the support measure. The correctness and validity of the algorithm are verified through simulated patterns and
real images.
Moving target tracking is a basic task in the processing of high speed photography. Despite its widely applications,
Correlation tracking method can not adapt to the rotation and zoom of target and results in accumulation of tracking
error. The Least Squares Image Matching(LSIM) method which is used in photogrammetry is introduced to moving
target tracking, and a Weighted Least Squares Image Matching(WLSIM) based tracking algorithm is proposed. The
WLSIM based algorithm sets weights according to the target's shape for the Least-Squares Image Matching Algorithm,
as a result matching error produced by the background in the tracking window can be avoided. Experimental results are
shown to demonstrate the robustness, efficiency and accuracy of the proposed algorithm.
An algorithm of multiple facula targets recognition based on edge and region search in full field of a frame of image is
presented. Firstly, the image is segmented by binarization and the burr around the targets is removed by morphology
processing. Then every facula target's edge and region is found and numbered in turn. Experimental results on simulated
images and real images are shown to validate the presented algorithm.
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