KEYWORDS: Clouds, 3D modeling, Fractal analysis, 3D acquisition, Target recognition, Statistical analysis, Data modeling, 3D metrology, Soil science, Information technology
The traditional discernible criteria for a 2D target are mostly based on Johnson criterion, to overcome the limitations of the Johnson criterion and fill the gap in a 3D point cloud, a novel discernible criterion has been proposed for the 3D point cloud. Based on the multifractal spectrum, the spatial distribution of the 3D point cloud is described. By analyzing the multifractal spectra at different resolutions, feature trend and the final discernible resolution are concluded. The experimental results show that the limiting resolution of T90, F15C is 585mm, the limiting resolution of T90 and Rexton is 517mm, and the limiting resolution of F15C and Rexton is 541mm. The proposed discernible criteria can provide theoretical support for limit identification resolution of 3D point cloud target.
Point cloud registration in military scenarios is pivotal to automatic object reconstruction and recognition. This paper proposes 1) a multi-scale binary feature representation called mLoVS (multi-scale local voxelized structure) and 2) a “min-pooling” based feature matching technique for accurate registration of tank point clouds. The key insight of our method is that traditional fixed-scale feature matching methods either suffer from limited shape information or data missing caused by occlusion, while the multi-scale way provides a flexible matching choice. In addition, the binary nature of our feature representation can alleviate the increased time budget required by multi-scale feature matching. Experiments on several sets of tank point clouds confirm the effectiveness and overall superiority of our method.
Image dehazing is one of the most important image processing methods and it is widely used in daily application. A large number of dehazing algorithms have been proposed in recent years. In order to solve the problem that the dehazing performances of the classic dark channel prior methods for hazy images with the sun are bad, we propose a dehazing method for removing mist interference in images with the sun in the sky. An atmospheric scattering model is proposed to estimate the scattered light of the sun in the hazy circumstance. We also propose a gradient-based transmission map estimation method to estimate the refined transmission map accurately while reducing the computational complexity. The effectiveness of our method is confirmed by extensive experiments over a wide variety of images.
In this paper, we proposed a novel three-dimension local surface descriptor named RPBS for point cloud representation.
First, points cropped form the query point within a predefined radius is regard as a local surface patch. Then pose
normalization is done to the local surface to equip our descriptor with the invariance to rotation transformation. To
obtain more information about the cropped surface, multi-view representation is formed by successively rotating it along
the coordinate axis. Further, orthogonal projections to the three coordinate plane are adopted to construct two-dimension
distribution matrixes, and binarization is applied to each matrix by following the rule that whether the grid is occupied, if
yes, set the grid one, otherwise zero. We calculate the binary maps from all the viewpoints and concatenate them
together as the final descriptor. Comparative experiments for evaluating our proposed descriptor is conducted on the
standard dataset named Bologna with several state-of-the-art 3D descriptors, and results show that our descriptor
achieves the best performance on feature matching experiments.
Image matching is at the base of many image processing and computer vision problems, such as object recognition or structure from motion. Current methods rely on good feature descriptors and mismatch removal strategies for detection and matching. In this paper, we proposed a robust image match approach based on ORB feature and VFC for mismatch removal. ORB (Oriented FAST and Rotated BRIEF) is an outstanding feature, it has the same performance as SIFT with lower cost. VFC (Vector Field Consensus) is a state-of-the-art mismatch removing method. The experiment results demonstrate that our method is efficient and robust.
This paper attempts to develop an unsupervised learning approach for airplane detection in remote sensing images. This novel airplane detection method is based on circle-frequency filter and cluster-based co-saliency detection. Firstly, the CF-filter method is utilized as the coarse detection to detect target airplanes with some false alarms. Then, we collect all the detected targets and use cluster-based co-saliency detection to enhance the real airplanes and weaken the false alarms, so that most of the false alarms can be eliminated. Experimental results on real remote sensing images demonstrate the efficiency and accuracy of the proposed method.
The matching area selection is the foundation of gravity gradient aided navigation. In this paper, a gravity gradient matching area selection criterion is proposed, based on the principal component analysis (PCA) and analytic hierarchy process (AHP). Firstly, the features of gravity gradient are extracted and nine gravity gradient characteristic parameters are obtained. Secondly, combining PCA with AHP, a PA model is built and the nine characteristic parameters are fused based on it. At last, the gravity gradient matching area selection criterion is given. By using this criterion, gravity gradient area can be divided into matching area and non-matching area. The simulation results show that gravity gradient position effect in the selected matching area is superior to the matching area, and the matching rate is greater than 90%, the position error is less than a gravity gradient grid.
Submarines in the underwater sailing need a safe, reliable, high accurate, and covert well navigation system. Inertial navigation system (INS) is the core of underwater navigation. But the inertial navigation system gathers information based gyroscope, accelerometer and other sensors. In accordance to Newton's laws of mechanics, their own speed, location and other information is calculated by integral recursion. Since the recursive work of INS, positioning error gradually increases with time elapsing. Gravity and gravity gradient aided navigation as a passive autonomous navigation are more and more focused on, Selection of the gravity gradient matching area is one of the key to gravity gradient matching navigation. Earth's marine area is enormous, underwater environment is complex. Take advantage of multi-feature information fusion of gravity gradient full tensor, one hand a wider range of matching area can be got, to gain wider path planning area. on the other hand, the positioning accuracy of assisted navigation system can be inproved.
The researches on calibration of star sensor rarely involve the exterior parameters and image distortion of the optical
system. In order to get more accurate interior-exterior parameters of the optical system, this paper proposes exact
calibration model and algorithm based on interior-exterior parameters. Basing on analyzing the imaging model of star
sensor, the principle of the star sensor calibration is as follow: firstly, the two-step method is used to get the initial
interior-exterior parameters; then Levenberg-Marquardt optimization algorithm is utilized to get the global optimal
solution. Experiments show that the angular distance of stars can be reduced from 57" to 5.2" after calibration. In
addition, the calibration method can effectively eliminate the coupling of the interior-exterior parameters, achieve higher
measurement accuracy, and significantly improve the recognition rate of the star map.
Star image blurred by aircraft vibration decreases location accuracy and probability of the star extraction. In this paper,
first, the influence of aircraft vibration on the star image captured by star sensors is analyzed, and the mathematical
model is deduced and established. Then, in order to overcome the adverse effects of star extraction and stabilize the
accuracy of star sensor in high dynamic environment, a restoration method for blurred star image using Richardson-Lucy
(RL) method is introduced. The experimental results indicate that the proposed method can effectively improve the star
image signal-to-noise ratio and the extraction accuracy.
A novel adaptive aircraft detection method based on level set processing and circle-frequency filter is proposed in this paper. First, the SBGFRLS (Selective Binary and Gaussian Filtering Regularized Level Set) method is used twice to find airport region of interest (ROI) and candidate aircraft areas by local segmentation and global segmentation, respectively, so that sizes of those possible target areas can be computed. Then, the circle-frequency (CF) filter method is utilized adaptively to detect target aircrafts in the airport ROI via the mean radius estimated by sizes of those candidate areas obtained before. Experimental results on real remote sensing airport images demonstrate the efficiency and accuracy of the proposed method.
Subject to limited resolution for targets in many satellite images, low-resolution airplane detection is still difficult and challenging, which plays an important role in remote sensing. In this paper, we propose a new method to detect lowresolution airplanes in satellite images. First, the image is preprocessed by combing the unsharp contrast enhancement (UCE) filtered image and the original image. Second, the Local Edge Distribution (LED), which is susceptible to objects owning clustered edges, e.g., airplane, is calculated to acquire the target candidate regions while restraining large background area. Then, a multi-scale fused gradient feature image is computed to characterize the shapes of targets instead of the original image to overcome the influence from the self-shadow and different coating colors of airplanes. After that, a designed airplane shape filter with a modulated item is used to detect and locate real targets, in which the modulated item can effectively measure the degree of coincidence between the patch region and the airplane shape. Finally, coordinates of target centers are computed in the filtered image. Experimental results demonstrate that the proposed algorithm is effective and robust for detecting low-resolution airplanes in satellite images under various complex backgrounds.
Gravity gradient is a tensor with five mutual independent components. Five gravity gradient components are complementary. Combining the gravity gradient full tensor, more detail information is contributed to gravity gradient matching aided position. Gravity gradient full tensor fusion matching aided position method is proposed in this paper. The matching strategy is particle filtering (PF) and fusion strategy is weighted fusion on the confidence coefficient of each gravity gradient component. Simulations have been done and results showed that full tensor fusion matching aided position method is more effective than the aided position method based on single gravity gradient component.
Traditional learning-based boundary extraction algorithms classify each pixel edge separately and then get boundaries from the local decisions of a classifier. However, we propose a supervised learning method for boundary extraction by using edgelets as boundary elements. First, we extract edgelets by clustering probabilities of boundary. Second, we use features of edgelets to train a classifier that determines whether an edgelet belongs to a boundary. The classifier is trained by utilizing edgelet features, including local appearance, multiscale features, and global scene features such as saliency maps. Finally, we use the classifier to decide the probability that the edgelet belongs to the boundary. The experimental results in the Berkeley Segmentation Dataset demonstrate that our algorithm can improve the performance of boundary extraction.
Using star tracker to perform space surveillance is a focal point of research in aerospace engineering. However, autonomous attitude determination with star trackers in missions is a challenging task, because of spacecraft attitude dynamics and false stars. We present a novel star pattern recognition algorithm to resolve these problems. The algorithm defines a star pattern, called a flower code, composed of angular distances and circular angles. Then, a three-step strategy is adopted to find the correspondence of the sensor pattern and the catalog pattern, including initial lookup table match, cyclic dynamic match, and validation. A number of experiments are carried out on simulated and real star images. The simulation results show that the proposed method provides improved performance, especially on robustness against false stars. Also, the results for real star images demonstrate the reliability of the method for ground-based measurements.
KEYWORDS: Digital video recorders, Data modeling, Virtual reality, Telecommunications, Data communications, Systems modeling, Computer networks, Prototyping, Network architectures, Control systems
There exist irreconcilable conflict between mass data and network transfer bandwidth in traditional distributed virtual reality (DVR) systems, which has severely limited the widespread application of DVR techniques. In order to solve these problems, a novel DVR system architecture based on streaming techniques is proposed. The topology of this system is a hybrid architecture (using peer-to-peer model to transfer control information, using hierarchy model to transfer data information), which is helpful to overcome the communication bottle-neck, and the object-oriented data structure is also designed to fit for streaming techniques. In order to take full advantage of streaming techniques, multithreading and dynamic buffer can be applied to reduce network time-delay. In this paper, the implements of these techniques are introduced in detail, the result of experiments prove that: the DVR system based on streaming techniques can notably reduce the hardware requirements, increase the client number and decrease the system time-delay, as a result make the internet based desktop DVR system available.
This paper presents a new method to simulate virtual ocean wave surface. One of the widely used methods for simulating ocean wave is making use of wind-wave spectrums, which is mostly based on linear wave theories. The ocean waves produced in this way can reflect the statistical characteristics of the real ocean well, on the other hand the waves does not look like actual ocean surface, they just look like superposition of sine/cosine curves. In order to overcome this shortcoming of traditional method, the new method proposed in this paper take account of the effect of the random wind velocity field over ocean surface. In practice, this method can simulate the natural environment of ocean more accurately than traditional method; in theory, the method increases the nonlinear factors of ocean waves. The virtual ocean wave simulated by this way is not only accord with statistical characteristics, but also looks like real ocean wave, it can be widely used in VR applications.
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