The global expansion of the Web brings the global computing;and the increasing number of problems with increasing complexity & sophistication also makes collaboration desirable. In this paper, we presented a seman...
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Description logics are widely used to express structured data and provide reasoning facility to query and integrate data from different databases. This paper presents a many-sorted description logic MDL to represent r...
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Service-oriented computing is a new computing paradigm that utilizes services as fundamental elements for developing applications. Service composition plays a very important role in it. This paper focuses on service c...
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Syntax-based statistical translation model is proved to be better than phrasebased model, especially for language pairs with very different syntax structures, such as Chinese and English. In this talk I will introduce...
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This paper illustrates the ICT Statistical Machine Translation system used in the evaluation campaign of the International Workshop on Spoken Language Translation 2010. We participate in the DIALOG tasks for Chinese-t...
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A new incident source with different angles was constructed for dealing with wide-angle scattering problems. Considering the impendence matrix in method of moments (MOM） is independent from incident angles, the equiv...
A new incident source with different angles was constructed for dealing with wide-angle scattering problems. Considering the impendence matrix in method of moments (MOM） is independent from incident angles, the equivalent relationship between induced current and the measured CS-current was build, while the CS-current can be computed directly under the new incident source. Finally, we can reconstruct the induce current by utilizing the theory of compressive sensing (CS). Compared with traditional MOM, the computational complexity can be greatly reduced.
This paper describes the compressed sampling using the block wavelet transform which has more flexibility in reconstruction the images. Compressed sampling is considered for signals and images that are sparse in a wav...
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ISBN:
(数字)9781424465163
ISBN:
(纸本)9781424465132
This paper describes the compressed sampling using the block wavelet transform which has more flexibility in reconstruction the images. Compressed sampling is considered for signals and images that are sparse in a wavelet basis. We propose a process of the image by sampling the data far below Nyquist rate in terms of compressed sampling, which shows that image data can be reconstructed from an extremely small set of measurements than what is generally considered necessary. A novel wavelet based interframe compression scheme has been developed and put into practice. It is based on a unique block wavelet transform that we have developed. BWT based interframe compression is very efficient in both compression and speed performance. The compression performance of our image codec hold a slightly lower PSNR value produces a more visually pleasing result. This implementation also preserves the scalability of the wavelet embedded coding technique.
In this paper, we propose a process of the radar image data by Random sampling. The sampling rate is far lower than Nyquist-Shannon sampling theorem, which shows that image data can be reconstructed from an extremely ...
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In this paper, we propose a process of the radar image data by Random sampling. The sampling rate is far lower than Nyquist-Shannon sampling theorem, which shows that image data can be reconstructed from an extremely small set of measurements than what is generally considered necessary. Compressive Sampling is considered for signals and images that are sparse in a wavelet basis. A low complexity compression method for high resolution image based on block partition in wavelet region is proposed. The image is partitioned into blocks in wavelet domain and then compressed separately with CS. The bit rates for each block is allocated according to the texture complexity of the block. The proposed method eliminates the "block effect" caused by traditional block partition in pixel domain, and solves the problem caused by traditional block partition that the areas with simple texture are good in reconstruction quality while those with complicated texture are too poor to be used because of the uneven distribution of texture complexity. The experimental results show that the compression performance of the proposed method is quite similar to the results obtained by global compression. This compression method is particularly suitable for the high resolution remote sensing image which sparse in a wavelet domain.
In order to improve the accuracy of the image annotation, an automatic image annotation method based on mutual K-nearest neighbor graph (MKNN) is proposed. The proposed algorithm describes the relationship between low...
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In order to improve the accuracy of the image annotation, an automatic image annotation method based on mutual K-nearest neighbor graph (MKNN) is proposed. The proposed algorithm describes the relationship between low-level features, annotation words and image by a mutual K-nearest neighbor graph. Semantic information is extracted by exploiting the mutual relationship of two nodes in the mutual K-nearest neighbor graph. Inverse document frequency (IDF) is introduced to adjust the weights of edges between the image node and its annotation word's node, which overcomes the deviation caused by high-frequency words. Experimental results in Corel image dataset show that the proposed algorithm improves effectively the image annotation performance..
Although many methods of refining initialization have appeared, the sensitivity of K-Means to initial centers is still an obstacle in applications. In this paper, we investigate a new class of clustering algorithm, K-...
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