Download e-book for kindle: Advanced Concepts for Intelligent Vision Systems: 16th by Sebastiano Battiato

By Sebastiano Battiato

ISBN-10: 3319259024

ISBN-13: 9783319259024

ISBN-10: 3319259032

ISBN-13: 9783319259031

This ebook constitutes the completely refereed lawsuits of the sixteenth overseas convention on complex recommendations for clever imaginative and prescient structures, ACIVS 2015, held Catania, Italy, in October 2015. The seventy six revised complete papers have been rigorously chosen from 129 submissions.

Acivs 2015 is a convention targeting recommendations for construction adaptive, clever, secure and safe imaging structures. the focal point of the convention is on following subject: low-level photo processing, video processing and digital camera networks, movement and monitoring, safety, forensics and biometrics, intensity and 3D, photo caliber development and evaluate, type and popularity, multidimensional sign processing, multimedia compression, retrieval, and navigation.

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Read Online or Download Advanced Concepts for Intelligent Vision Systems: 16th International Conference, ACIVS 2015, Catania, Italy, October 26-29, 2015. Proceedings PDF

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Additional info for Advanced Concepts for Intelligent Vision Systems: 16th International Conference, ACIVS 2015, Catania, Italy, October 26-29, 2015. Proceedings

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The results of ringing suppression after resampling with a factor of 2 using regularization-based algorithm [8] are shown in Table 3. We base on the fact that for the problem of image resampling the ringing parameter d equals to the scale factor [14]. For the images ‘barbara’ and ‘clown’ from Table 3, the proposed method does not show satisfactory results in the areas containing a lot of parallel lines due to texture corruption during downsampling process. Table 1. Results of the ringing suppression (d = 2) after high frequency cut-off, SSIM.

N xi 0 . V. Umnov et al. t. X 0,inf ≤ α. This problem can be solved using any sparse dictionary learning algorithm. In our work we use the K-SVD algorithm [1] for dictionary learning and OMP algorithm [10] for sparse coding. 3 Ringing Suppression Method The proposed method requires a set of images for learning. We split all images into overlapping 6 × 6 blocks and process each block separately. The images from the learning set are used to create a set of corresponding original blocks and blocks with ringing effect.

In order to improve the ringing suppression performance we add several modifications to the joint sparse coding approach. Regularization. In our preliminary experiments we have found that the method performs better when different sparsity parameter is used in learning and testing phase (αlearn = 10 and αtest = 2). We suppose that it is caused by the fact that the blocks with ringing effect are less informative than the ringing free blocks. Thus in the testing phase (when the method builds the representation only from the block with ringing) stronger regularization makes the method more stable.

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Advanced Concepts for Intelligent Vision Systems: 16th International Conference, ACIVS 2015, Catania, Italy, October 26-29, 2015. Proceedings by Sebastiano Battiato

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