This paper will discuss a method for presenting a surveillance image captured by an active camera such as one that actively tracks a moving subject as an image that is easily understood by an observer. Active camera s...
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The scope of this paper is the challenging task of classifying terrestrial images of buildings, automatically. Straight line segments and their connectivity incorporate significant information about object shapes. Man...
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Outliers are data values that lie away from the general cluster of other data values. Detecting the outliers of a dataset is an important research topic for data cleaning and finding new useful knowledge in many resea...
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This paper presents a novel approach to compute DCT-I, DCT-III, and DCT-IV. By using a modular mapping and truncating, DCTs are approximated by linear sums of discrete moments computed fast only through additions. Thi...
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Scale is a major concept in many sciences concerned with human activities and physical processes occurring in the world, and directly related to many investigations of spatial objects, including the procedure of spati...
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Scale is a major concept in many sciences concerned with human activities and physical processes occurring in the world, and directly related to many investigations of spatial objects, including the procedure of spatial data mining. In this paper, we attempt to apply the spatial data mining to the field of coal mining, and the technical notion is to generate patterns or rules by means of different scale databases that depict the same subject. The whole research procedure gives readers an understanding of how processes operate at different scales and how they can be linked across scales. At the same time, our study actually presents a new method of image mining as well.
Autonomous model building is a crucial trend in model based methods like AAMs. This paper introduces an approach that deals with non-linearities by detecting distinct sub-parts in the data. Sub-models each representin...
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ISBN:
(纸本)1901725294
Autonomous model building is a crucial trend in model based methods like AAMs. This paper introduces an approach that deals with non-linearities by detecting distinct sub-parts in the data. Sub-models each representing an individual sub-part are derived from a minimum description length criterion. Thereby the resulting clique of models is more compact and obtains a better generalization behavior than a single model. The proposed AAM clique generation deals with non-linearities in the data in a generic information theoretic manner reducing the necessity of user interaction during training.
A method used for recognition and understanding of airfield based on mathematical morphology is proposed in this paper. The new approach can he divided into three steps. First, to extract the typical geometric structu...
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A method used for recognition and understanding of airfield based on mathematical morphology is proposed in this paper. The new approach can he divided into three steps. First, to extract the typical geometric structure features of airfield, a segmentation method called recursive Otsu algorithm is employed on an airfield image. Second, thinning and shrinking algorithms are utilized to obtain the contour of airfield with single pixel and to remove diffused small particles. Finally, Radon transform is adopted to extract two typical and important components, primary and secondary runways of airfield exactly. At the same time, region growing algorithm is exploited to get the other components such as parking apron and garages. The experimental results demonstrate that the proposed method gives good performance.
Conventional clustering algorithms are designed for a single independent dataset, i.e. Fuzzy C-Means (FCM) clustering algorithm. In the real world, a dataset is independent of other datasets but sometimes can be coope...
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Spectral Angle Mapper (SAM) model has got wide applications in hyperspectral Remote Sensing (RS) information processing. But Spectral Angle couldn't achieve satisfied performance in some cases because of its sensi...
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Spectral Angle Mapper (SAM) model has got wide applications in hyperspectral Remote Sensing (RS) information processing. But Spectral Angle couldn't achieve satisfied performance in some cases because of its sensitivity to noises and uncertainty. Based on the analysis to traditional SAM algorithm, four types of errors and their impacts to spectral angle are investigated. In order to reduce the impacts of above errors, some improved algorithms are proposed and experimented. The first improved algorithm is grouping spectral angle algorithm. In this new algorithm all bands are divided into two sets by odd and even bands, that means two additional sub-vectors are created in addition to the original spectral vector. So three spectral angles will be computed and the minimum of three indexes is used as final index. The second improved algorithm is normalized spectral angle. In this way spectral angle is computed to the normalized vectors of two original vectors. Two approaches are used to normalize the spectral vector, and spectral angle is computed to the normalized vectors. This algorithm is able to decrease the impacts of random errors. The third algorithm is intersected spectral angle. Spectral angle is calculated by a spectral displacement strategy in this approach. That means a given displacement to change the corresponding bands of two spectral vectors is used and a spectral angle to the displaced vectors will be got. By this displacement strategy the impacts of band offset is reduced. Finally some experiments are used to test those improved algorithms. It proves that those new approaches can reduce and control the errors and improve the precision and reliability of similarity measure.
Noise is a common phenomenon in many real-world optimizations. It has long been argued that evolutionary algorithm (EA) should be relatively robust against it. As a novel computing model in evolutionary computations, ...
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Noise is a common phenomenon in many real-world optimizations. It has long been argued that evolutionary algorithm (EA) should be relatively robust against it. As a novel computing model in evolutionary computations, estimation of distribution algorithm (EDA) is also encountered with it. This paper initially presents three dynamic models of EDA under the additively noisy environment with three different selection methods (proportional selection method, truncation selection method and tournament selection method). We verify that when the population size is infinite, EDA can converge to the global optimal point. This concept establishes the theoretic foundation for optimization of noisy fitness functions with EDA
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