This paper focuses on route planning, especially for unmanned aircrafts in marine environment. Firstly, new heuristic information is adopted such as threat-zone, turn maneuver and forbid-zone based on voyage heuristic...
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This paper focuses on route planning, especially for unmanned aircrafts in marine environment. Firstly, new heuristic information is adopted such as threat-zone, turn maneuver and forbid-zone based on voyage heuristic information. Then, the cost function is normalized to obtain more flexible and reasonable routes. Finally, an improved sparse A* search algorithm is employed to enhance the planning efficiency and reduce the planning time. Experiment results showed that the improved algorithm for aircraft in maritime environment could find a combinational optimum route quickly, which detoured threat-zones, with fewer turn maneuver, totally avoiding forbid-zones, and shorter voyage.
Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents...
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Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents a general moving objects recognition method using global features of targets. Targets are extracted with an adaptive Gaussian mixture model and their silhouette images are captured and unified. A new objects silhouette database is built to provide abundant samples to train the subspace feature. This database is more convincing than the previous ones. A more effective dimension reduction method based on graph embedding is used to obtain the projection eigenvector. In our experiments, we show the effective performance of our method in addressing the moving objects recognition problem and its superiority compared with the previous methods.
Accurate detection of moving objects is an important step in stable tracking or recognition. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, the correlation...
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Accurate detection of moving objects is an important step in stable tracking or recognition. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, the correlation between neighboring pixels can be used to achieve high levels of detection accuracy in the presence of dynamic background. However, color similarity between foreground and background will cause many foreground pixels to be misclassified. In this paper, an adaptive foreground model is exploited to detect moving objects in dynamic scenes. The foreground model provides an effective description of foreground by adaptively combining the temporal persistence and spatial coherence of moving objects. Building on the advantages of MAP-MRF (the maximum a posteriori in the Markov random field) decision framework, the proposed method performs well in addressing the challenging problem of missed detection caused by similarity in color between foreground and background pixels. Experimental results on real dynamic scenes show that the proposed method is robust and efficient.
Palmprint verification is a relatively new but promising personal authentication technique for its high accuracy and fast matching speed. Two dimensional (2D) palmprint recognition has been well studied in the past de...
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ISBN:
(纸本)9781424469840;9781424469857
Palmprint verification is a relatively new but promising personal authentication technique for its high accuracy and fast matching speed. Two dimensional (2D) palmprint recognition has been well studied in the past decade, and recently three dimensional (3D) palmprint recognition techniques were also proposed. The 2D and 3D palmprint data can be captured simultaneously and they provide different and complementary information. 3D palmprint contains the depth information of the palm surface, while 2D palmprint contains plenty of textures. How to efficiently extract and fuse the 2D and 3D palmprint features to improve the recognition performance is a critical issue for practical palmprint systems. In this paper, an efficient joint 2D and 3D palmprint matching scheme is proposed. The principal line features and palm shape features are extracted and used to accurately align the palmprint, and a couple of matching rules are defined to efficiently use the 2D and 3D features for recognition. The experiments on a 2D+3D palmprint database which contains 8000 samples show that the proposed scheme can greatly improve the performance of palmprint verification.
In this paper, a system based on the novel Maximum Variance Projection (MVP) is proposed to improve the performance of protein subcellular localization prediction. Firstly, the protein sequences are quantized into a h...
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In this paper, a system based on the novel Maximum Variance Projection (MVP) is proposed to improve the performance of protein subcellular localization prediction. Firstly, the protein sequences are quantized into a high dimension space using a new representation approach Position-Specific Score Matrix (PSSM). However, the problems caused by such representation are computation complexity and complicated classifier design. To sort out this problem, a new dimension reduction algorithm, MVP, is introduced. It extracts the essential features from the high dimension feature space. Then, K-Nearest Neighbor (K-NN) classifier is employed to recognize the subcellular localization of proteins according to the new features after dimension reduction. A good experimental result is obtained based on the jackknife dataset.
In this paper, a novel method of fingerprint minutiae extraction on grey-scale images is proposed based on the Gabor phase field. The novelty of our approach is that the proposed algorithm is performed on the transfor...
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In this paper, a novel method of fingerprint minutiae extraction on grey-scale images is proposed based on the Gabor phase field. The novelty of our approach is that the proposed algorithm is performed on the transform domain, i.e. the Gabor phase field of the fingerprint image. This is different from most existing minutiae extraction methods, in which the minutiae are usually extracted from the binarized and thinned fingerprint image. Experimental results on benchmark data sets demonstrate that the proposed algorithm has promising performances.
In most cases, echocardiographers examine three-dimensional (3D) or four-dimensional (4D) echocardiographic images by studying several important cross sections which are defined as the optimal cross sections. It is a ...
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ISBN:
(纸本)9781424467754;9781424467761
In most cases, echocardiographers examine three-dimensional (3D) or four-dimensional (4D) echocardiographic images by studying several important cross sections which are defined as the optimal cross sections. It is a time-consuming and tedious task for echocardiographers to manually search the optimal cross sections by existing real-time 3D echocardiography (RT3DE) systems or off-line 3D softwares. To assist doctors' diagnosis, we design a registration-based method to automatically detect the optimal cross sections in three-dimensional echocardiographic (3DE) images. Firstly, a normal end-diastolic (ED) volumetric data is chosen, in which its optimal cross section is predetermined. Secondly, instead of the whole ED volume, only part of it is selected as the template. Finally, all the optimal cross sections of a 3DE image are detected by aligning the 3DE image with the template image. As a preliminary study, we performed this method on 28 normal apical datasets and the error rate is 15%. The results show good prospects for implementing automatic detection, which might offer better repeatability and save time. Furthermore, it can be a prior procedure for measurement and the computer-aided diagnosis.
As a fundamental biological problem, revealing the protein folding mechanism remains to be one of the most challenging problems in structural bioinformatics. Prediction of protein folding rate is an important step tow...
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We address the problem of 3D human pose estimation in a single real scene image. Normally, 3D pose estimation from real image needs background subtraction to extract the appropriate features. We do not make such assum...
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We address the problem of 3D human pose estimation in a single real scene image. Normally, 3D pose estimation from real image needs background subtraction to extract the appropriate features. We do not make such assumption, In this paper, a two-step approach is proposed, first, instead of applying background subtraction to get the segmentation of human, we combine the segmentation with human detection using an ISM-based detector. Then, silhouette feature can be extracted and 3D pose estimation is solved as a regression problem. RVMs and ridge regression method are applied to solve this problem. The results show the robustness and accuracy of our method.
To address two challenging problems in infrared target tracking, target appearance changes and unpre- dictable abrupt motions, a novel particle filtering based tracking algorithm is introduced. In this method, a novel...
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To address two challenging problems in infrared target tracking, target appearance changes and unpre- dictable abrupt motions, a novel particle filtering based tracking algorithm is introduced. In this method, a novel saliency model is proposed to distinguish the salient target from background, and the eigenspace model is invoked to adapt target appearance changes. To account for the abrupt motions efficiently, a two- step sampling method is proposed to combine the two observation models. The proposed tracking method is demonstrated through two real infrared image sequences, which include the changes of luminance and size, and the drastic abrupt motions of the target.
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