Get Advances in Multimedia, Software Engineering and Computing PDF

By FuHua Shang, YaDong Zhou, HongTao Xie (auth.), David Jin, Sally Lin (eds.)

ISBN-10: 364225988X

ISBN-13: 9783642259883

ISBN-10: 3642259898

ISBN-13: 9783642259890

MSEC2011 is an built-in convention concentrating its concentration upon Multimedia ,Software Engineering, Computing and schooling. within the continuing, you could research even more wisdom approximately Multimedia, software program Engineering ,Computing and schooling of researchers all over the international. the most position of the continuing is for use as an trade pillar for researchers who're operating within the pointed out box. with a purpose to meet excessive commonplace of Springer, AISC sequence ,the association committee has made their efforts to do the subsequent issues. to start with, negative caliber paper has been refused after reviewing path by way of nameless referee specialists. Secondly, periodically assessment conferences were held round the reviewers approximately 5 instances for changing reviewing feedback. eventually, the convention association had numerous initial classes earlier than the convention. via efforts of other humans and departments, the convention may be winning and fruitful.

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Read or Download Advances in Multimedia, Software Engineering and Computing Vol.1: Proceedings of the 2011 MSEC International Conference on Multimedia, Software Engineering and Computing, November 26–27, Wuhan, China PDF

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Extra info for Advances in Multimedia, Software Engineering and Computing Vol.1: Proceedings of the 2011 MSEC International Conference on Multimedia, Software Engineering and Computing, November 26–27, Wuhan, China

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So the model based on the gray information is not suitable for SAR images. 2 (c) 1~2 shows the image segmentation results based on the proposed model, and we can see the building areas are segmented , (a)1 (b)1 (c)1 (d)1 (a)2 (b)2 (c)2 (d)2 Fig. 2. The segmentation results of the interest building areas. (a)1~2 the orginal SAR images; (b)1~2 the segmentation results based on CV model; (c)1~2 the segmentation results based on the proposed model; (d)1~2 the segmentation results after removing flase areas.

To decrease the computationall cost, we randomly select part of ST and S B as the trainning examples for the SVDD claassifier. 2 Image Segmentation Base on SVDD Including Negative Examples In this part, we employ thee SVDD technique to classify the rest patterns into tarrget class and background classs. Given a set X = { xi }i =1 , SVDD aims at defining a m minimum volume hypersphere in the kernel space to include all (or most) of the tarrget patterns in X by minimizing g a cost function. M. Zhang et al.

IEEE Transactions on Systems, Man, and Cybernetics 39, 214–222 (2009) 10. : Target detection and texture segmentation in polarimetric SAR images using a wavelet frame: Theoretical aspects. IEEE Transactions on Geoscience and Remote Sensing 45, 3437–3453 (2007) 11. : Special target detection of the SAR image via exponential wavelet fractal. com Abstract. A method for synthetic aperture radar (SAR) image classification in urban areas based on modified Unit-linking pulse coupled neural networks (Unit-linking PCNN) and texture feature is presented.

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Advances in Multimedia, Software Engineering and Computing Vol.1: Proceedings of the 2011 MSEC International Conference on Multimedia, Software Engineering and Computing, November 26–27, Wuhan, China by FuHua Shang, YaDong Zhou, HongTao Xie (auth.), David Jin, Sally Lin (eds.)


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