A novel authentication watermarking scheme for images is proposed in this paper, which holds accuracy location and high security at the same time. In the scheme, different keys are selected for different host data, an...
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The framework of a second order morphology algorithm is proposed to enhance the dim small infrared target in sea clutter background with strong detector noise. First, a morphological filters bank is given. Each filter...
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Blurred images are caused by many factors such as defocus, motion, and atmospheric turbulence. Due to the unknown various factors that cannot be distinguished in the blurred image, it is necessary to propose a unified...
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Simulating and rendering realistic ocean is always one of the most popular and difficult tasks in computer graphics and oceanography. However, because of the restriction of computer software and hardware conditions, m...
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We have recently introduced an incremental learning algorithm, Learn ++ .NSE, designed to learn in nonstationary environments, and has been shown to provide an attractive solution to a number of concept drift problems...
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We have recently introduced an incremental learning algorithm, Learn ++ .NSE, designed to learn in nonstationary environments, and has been shown to provide an attractive solution to a number of concept drift problems under different drift scenarios. However, Learn ++ .NSE relies on error to weigh the classifiers in the ensemble on the most recent data. For balanced class distributions, this approach works very well, but when faced with imbalanced data, error is no longer an acceptable measure of performance. On the other hand, the well-established SMOTE algorithm can address the class imbalance issue, however, it cannot learn in nonstationary environments. While there is some literature available for learning in nonstationary environments and imbalanced data separately, the combined problem of learning from imbalanced data coming from nonstationary environments is underexplored. Therefore, in this work we propose two modified frameworks for an algorithm that can be used to incrementally learn from imbalanced data coming from a nonstationary environment.
Object recognition is challenging problem in computer vision due to appearance variation and presence of visual clutter and occlusions. Recently manifolds are thought to be fundamental for visual perception, and manif...
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Focus on the image compressing problem of unmanned aerial vehicle with high compression ratio, fixed compressing ratio and low computational complexity requirement, a low-complexity image-sequence compressing algorith...
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In this paper, the knowledge modeling, architecture design and detailed implementation of an ontology-based knowledge base for target recognition in remote sensing images is presented. Knowledge base is a critical com...
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Roadmap methods were widely used in route planning fields, both for robots and unmanned aircrafts. Traditional roadmap is constituted by connecting the vertexes of convex obstacle, which is related to the locations of...
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Image Segmentation is a basal research in image processing domain. The outcome of segmentation has an immense effect on the posterior image processing. Focusing on gray-level image adaptive segmentation, in this paper...
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ISBN:
(纸本)9781424451920;9781424451937
Image Segmentation is a basal research in image processing domain. The outcome of segmentation has an immense effect on the posterior image processing. Focusing on gray-level image adaptive segmentation, in this paper, a new gray-level image segmentation algorithm using 2-D histogram thresholding based on local entropy (GLLE) is proposed. Some comparative experiments using typical image are implemented and the results are shown at the end of this paper. The results of comparative experiment demonstrate that the GLLE method can segment the target area much more intactly than the classical methods. Furthermore, the GLLE method also has the ability of anti-illumination.
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