In this paper we proposed a novel path integral method using multilevel Metropolis sampling to extract the contours of interested objects of medical images, which is a quantum statistical approach inspired by the esse...
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In this paper we propose a method for segmenting characters in multispectral images of ancient documents. Due to the low quality of the document images the main idea of our study is to combine the multispectral behavi...
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ISBN:
(纸本)9781617388767
In this paper we propose a method for segmenting characters in multispectral images of ancient documents. Due to the low quality of the document images the main idea of our study is to combine the multispectral behavior and contextual spatial information. Therefore we utilize a Markov Random Field model using the spectral information of the images and stroke properties to include spatial dependencies of the characters. The whole process is parameter free since we calculate the stroke properties and the Gaussian parameters for the imaging model automatically. The study shows the effectiveness of using multispectral data for a computer aided analysis of ancient text documents. We compare the results of the proposed segmentation method to traditional methods based on color images as well as gray level images.
Contour extraction is a key issue in many medical applications. A novel statistical approach based on quantum mechanics to extract contour of the interested object of medical images was proposed in this paper. The nat...
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Simulating the soft tissue deformation based on bone-related planning is one of the most difficult issues in generating realistic motion of soft tissues. The difficulty of this problem arises from unclear boundary con...
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A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation. An iterative method is introd...
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A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation. An iterative method is introduced to solve this equation efficiently. The unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density method. Computer simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm. Copyright.
One of the important tasks in sensor networks is classifying moving vehicles. Fusion of large amount of sensor measurements can improve network performance and reduce the consumption of sensor network resource. We stu...
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One of the important tasks in sensor networks is classifying moving vehicles. Fusion of large amount of sensor measurements can improve network performance and reduce the consumption of sensor network resource. We study using continuous measurements of multiple sensor nodes to improve the classification performance by spatio-temporal fusion and fault detection. Time series decisions of single sensor node are aggregated to make a reliable classification estimation. A fusion center combines local classification decisions and evaluates the correctness of these decisions. A correctness status is sent back to each sensor node. Based on the status, sensor nodes can adjust their temporal fusion result. Simulation results demonstrate the validity of our method.
This paper presented improved Sparse A-Star Search (SAS) algorithm to pursue a fast route planner for Unmanned Aerial Vehicles (UAVs) on-ship applications. Our approach can quickly produce 3-D trajectories composed by...
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Based on quantum-behaved particle swarm optimization (QPSO), a novel path planner for unmanned aerial vehicle (UAV) is employed to generate a safe and flyable path. The standard particle swarm optimization (PSO) and q...
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Content-based image retrieval plays a key role in the management of a large image database. However, the results of existing approaches are not as satisfactory for the gap between visual features and semantic concepts...
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ISBN:
(纸本)9781617388767
Content-based image retrieval plays a key role in the management of a large image database. However, the results of existing approaches are not as satisfactory for the gap between visual features and semantic concepts. Therefore, a novel scheme is here proposed. First, to tackle the problem of large computational cost involved in a large image database, a pre-filtering processing is utilized to filter out the most irrelevant images while keeping the most relevant ones. Second, the relevance between the query image and the remaining images is measured and the obtained relevance scores are stored for a later refinement processing. Finally, a semi-supervised learning algorithm is utilized to refine candidate ranking by taking into account both thepairwise information of unlabeled images and the relevance scores between the input query image and unlabeled images. Experiments conducted on a typical Corel dataset demonstrate the effectiveness of the proposed scheme.
A new path planning method for UAV in static workspace is presented. The method can find a nearly optimal path in short time which satisfies the UAV kinematic constraints. The method makes use of the skeletons to cons...
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