A new proximity algorithm for an active contour model is proposed in this *** order to derive a mathematical form of the energy,the level set method is *** the new energy,a penalty term is introduced to make sure that...
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ISBN:
(数字)9781510630765
ISBN:
(纸本)9781510630758
A new proximity algorithm for an active contour model is proposed in this *** order to derive a mathematical form of the energy,the level set method is *** the new energy,a penalty term is introduced to make sure that the level set function can be restricted in the interval [-1,1].By introducing this term,the energy still keeps convex and is easy to construct its minimization *** on the proximity operator and the corresponding theories,we deduce a proximity algorithm to minimize the *** results demonstrate that the proposed model is powerful in its segmentation ability and *** comparisons with other popular algorithms show that the proposed algorithm is more efficient.
The fractional-order total variation(TV) image denoising model has been proved to be able to avoid the "blocky effect''. However, it is difficult to be solved due to the non-differentiability of the fract...
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The fractional-order total variation(TV) image denoising model has been proved to be able to avoid the "blocky effect''. However, it is difficult to be solved due to the non-differentiability of the fractional-order TV regularization term. In this paper, the proximity algorithm is used to solve the fractional-order TV optimization problem, which provides an effective tool for the study of the fractional-order TV denoising model. In this method, the complex fractional-order TV optimization problem is solved by using a sequence of simpler proximity operators, and therefore it is effective to deal with the problem of algorithm implementation. The final numerical procedure is given for image denoising, and the experimental results verify the effectiveness of the algorithm. (C) 2015 Elsevier Inc. All rights reserved.
In the process of using knowledge graphs to assist deep learning in logical reasoning, there are problems with weak discriminative and generalisation abilities, as well as insufficient stability. A model for mathemati...
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This paper evaluates the bluetooth low energy (BLE) positioning systems using the sparse-training data through the comparison experiments. The sparse-training data is extracted from the database including enough data ...
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This paper evaluates the bluetooth low energy (BLE) positioning systems using the sparse-training data through the comparison experiments. The sparse-training data is extracted from the database including enough data for realizing the highly accurate and precise positioning. First, we define the sparse-training data, i.e., the data collection time and the number of smartphones, directions, beacons, and reference points, on BLE positioning systems. Next, the positioning performance evaluation experiments are conducted in two indoor environments, that is, an indoor corridor as a one-dimensionally spread environment and a hall as a two-dimensionally spread environment. The algorithms for comparison are the conventional fingerprint algorithm and the hybrid algorithm (the authors already proposed, and combined the proximity algorithm and the fingerprint algorithm). Based on the results, we confirm that the hybrid algorithm performs well in many cases even when using sparse-training data. Consequently, the robustness of the hybrid algorithm, that the authors already proposed for the sparse-training data, is shown.
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