Fault tolerance is a central issue in the design and implementation of interconnection networks for large parallel systems. Connection probability of a network is a good network fault tolerance measure. For a mesh of ...
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Learning in nonstationary environments, also called learning concept drift, has been receiving increasing attention due to increasingly large number of applications that generate data with drifting distributions. Thes...
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Learning in nonstationary environments, also called learning concept drift, has been receiving increasing attention due to increasingly large number of applications that generate data with drifting distributions. These applications are usually associated with streaming data, either online or in batches, and concept drift algorithms are trained to detect and track the drifting concepts. While concept drift itself is a significantly more complex problem than the traditional machine learning paradigm of data coming from a fixed distribution, the problem is further complicated when obtaining labeled data is expensive, and training must rely, in part, on unlabelled data. Independently from concept drift research, semi-supervised approaches have been developed for learning from (limited) labeled and (abundant) unlabeled data; however, such approaches have been largely absent in concept drift literature. In this contribution, we describe an ensemble of classifiers based approach that takes advantage of both labeled and unlabeled data in addressing concept drift: available labeled data are used to generate classifiers, whose voting weights are determined based on the distances between Gaussian mixture model components trained on both labeled and unlabeled data in a drifting environment.
Interactive object segmentation is widely used for extracting any user-interested objects from natural images. A common problem with many interactive segmentation approaches is that the object segmentation quality is ...
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For a graph G, G → (a1, a2, · · ·, ar)v means that in every r-coloring of the vertices in G, there exists a monochromatic ai-clique of color i for some i∈{1, 2, · · ·, r}. The vertex Fo...
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The existence of imbalanced data between one class and another class is an important issue to be considered in a classification problem. One of the well-known data balancing technique is the artificial oversampling, w...
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The existence of imbalanced data between one class and another class is an important issue to be considered in a classification problem. One of the well-known data balancing technique is the artificial oversampling, which increase the size of datasets. In this research, multinomial classification was applied to classify some recorded features obtained from a single ECG (electrocardiograph) sensor. Therefore, a Dirichlet process, a dirichlet distribution of cumulative distribution function of each data partition, was needed to model the distribution of the new generated data by also considering the statistical properties of the previous data. Data balancing process had given the result of 77.21% classification accuracy (CA), and 90.9% area under ROC curve (AUC).
A novel image threshold selection approach based on structural similarity (SSIM) is proposed. The thresholded image is obtained first, then comparison regions are extracted based on the local variance of the neighborh...
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A novel image threshold selection approach based on structural similarity (SSIM) is proposed. The thresholded image is obtained first, then comparison regions are extracted based on the local variance of the neighborhood of the thresholded image. Due to the characteristic of comparison regions, the conventional SSIM expression is simplified as a nonparametric form, and the partial SSIM (PSSIM) is defined. The optimal threshold is selected by maximizing the PSSIM criterion function at last. Besides the introduction of a novel approach, this is also the first attempt to expand the application scope of SSIM to range image thresholding in general. The proposed approach has an advantage over thresholding methods based on the histogram. The method was tested on a variety of images including the synthetic image and real images. Experimental results show that the proposed approach achieves better applicability, preferable ability for extracting object and better anti-noise capability than popular methods.
作者:
Z. ChenJ. G. LiuG. Y. WangIntelligence Control
Institute for Pattern Recognition and Artificial Intelligence and Multi-Spectral Information Processing State Key Laboratory Huazhong University of Science and Technology China Intelligence Control
Institute for Pattern Recognition and Artificial Intelligence and Multi-Spectral Information Processing State Key Laboratory Huazhong University of Science and Technology China
According to the drawback of the traditional circle target extraction algorithm from high resolution remote sensing imagery used by Hough Transform, such as computation complexity, low efficiency and etc, a new circle...
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According to the drawback of the traditional circle target extraction algorithm from high resolution remote sensing imagery used by Hough Transform, such as computation complexity, low efficiency and etc, a new circle target extraction method is proposed in this paper which can extract multiple circle targets with different radius at one time. First, the Average Absolute Difference is implemented to enhance the edge of the circle targets and suppress the noise of the background. Secondly, the locally self-adaptive segmentation algorithm is implemented to obtain the binary image. Thirdly, the thinning algorithm based on model computation is implanted to obtain the single pixel edge of the circle targets and in order to reduce the computation times in the following process. Furthermore, a pruning algorithm is necessary; finally, a modified Hough transform algorithm is proposed to obtain the circle targets. The experimental results demonstrate that the new circle targets algorithm can extract the multiple circle targets quickly and accurately, which has three advantages: low time consuming, high detection rate, robust to noise and fragmentary boundaries.
This paper presents a novel fast single-pass contour tracing algorithm in a binary image. The proposed algorithm is viewed as follow steps: firstly a set of contour segments of all object contours can be generated and...
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ISBN:
(纸本)9781612847719
This paper presents a novel fast single-pass contour tracing algorithm in a binary image. The proposed algorithm is viewed as follow steps: firstly a set of contour segments of all object contours can be generated and traced in a top-down line scan fashion; then all contour segments are employed to be integrated into respective intact contours; finally all results are converted into the chain code as the final output. This algorithm can extract multiple contours of an image in one pass and never lose any outer and inner contour of object region. It is faster on implementation. Experiments results prove those advantages.
According to the feature that the gray distribution of the transition region (locating between the objects and the background) is more scattered than that of the regions of targets or background in an image, this pape...
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According to the feature that the gray distribution of the transition region (locating between the objects and the background) is more scattered than that of the regions of targets or background in an image, this paper proposes a novel method concerning about transition region extraction and segmentation, which is based on local variance of different areas of an image. The experiments indicate that the proposed approach can achieve better segmentation results than the local complexity method (one of the previous methods for extracting transition region).What's more, the novel approach outperforms the local complexity method about more complete and more accurate transition regions, less interference from backgrounds, more detail information of segmented targets, clearer and better segmented targets, easier calculation, and higher processing speed.
In this paper, we denote a color image by a quaternion function, then find edge points by solving the maximum of quaternion fractional directional differentiation(QFDD)'s norm. This method is called edge detection...
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In this paper, we denote a color image by a quaternion function, then find edge points by solving the maximum of quaternion fractional directional differentiation(QFDD)'s norm. This method is called edge detection based on QFDD. Experiments indicate that the method has special advantages. Comparing with Canny, LOG, Sobel, and general fractional differentiation, we discover that QFDD has fewer false negatives in the textured regions and is also better at detecting edges which are partially defined by texture, which means we will obtain better results in the interesting regions by QFDD and these results are more consistent with the characteristics of human visual system.
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