Traditionally, the algorithm of ID3 takes the information gain as a standard of expanding attributes. During the process of selection of expanded attributes, attributes with more values are usually preferred to be sel...
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In this paper, we discuss how link stability and number of mobile nodes impact on end-to-end delay in mobile ad hoc network (MANET). We find that their relationship shows differently when there is a path between any a...
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The paper presented a novel discriminative model for efficient and effective recognition and simultaneous semantic segmentation of objects in images. The images are first segmented to give 'super-pixels'. Then...
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ISBN:
(纸本)9781450304603
The paper presented a novel discriminative model for efficient and effective recognition and simultaneous semantic segmentation of objects in images. The images are first segmented to give 'super-pixels'. Then the super-pixels are merged together and semantically labeled using a Condition Random Field (CRF) model. The use of a conditional random field allows us to incorporate different cues in a single unified model. The test on the standard dataset shows that compared with existing systems, the proposed system produces a detailed segmentation of a test image into coherent regions, with a semantic label associated with each region in the image. Copyright 2010 ACM.
Traditional Chinese text chunking approach is to identify many phrases using only one model, and the same features are used to identify these phrases too. So the helpful features of each phrase are ignored. In fact, d...
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This paper investigates an approach of predicting and estimating pose accuracy of two-ocular optical tracker in augmented reality systems. Gauss-distributed error covariance matrix propagation model was adopted to ded...
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This paper investigates an approach of predicting and estimating pose accuracy of two-ocular optical tracker in augmented reality systems. Gauss-distributed error covariance matrix propagation model was adopted to deduce the error propagation formulas from camera image errors to final output pose errors of the tracker, and the variation characteristics of the tracker pose error were analyzed through experiments under static and dynamic tracking states. Experimental results are consistent with the predicted data, which confirms the dominant effect of camera extrinsic parameter errors on tracker pose accuracy and validates the usefulness of our proposed approach to predicting and estimating pose accuracy.
Accurate head pose tracking is a key issue to accomplish precise registration in indoor augmented reality systems. This paper proposes a novel approach based on multi-sensor data fusion to achieve optical tracking of ...
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This paper investigates the role of semantic diversity and locality of crossover operators in Genetic Programming (GP) for Boolean problems. We propose methods for measuring and storing semantics of subtrees in Boolea...
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ISBN:
(纸本)9781450300728
This paper investigates the role of semantic diversity and locality of crossover operators in Genetic Programming (GP) for Boolean problems. We propose methods for measuring and storing semantics of subtrees in Boolean domains using Trace Semantics, and design several new crossovers on this basis. They can be categorised into two classes depending on their purposes: promoting semantic diversity or improving semantic locality. We test the operators on several wellknown Boolean problems, comparing them with Standard GP Crossovers and with the Semantic Driven Crossover of Beadle and Johnson. The experimental results show the positive effects both of promoting semantic diversity, and of improving semantic locality, in crossover operators. They also show that the latter has a greater positive effect on GP performance than the former. Copyright 2010 ACM.
In the field of imbalance learning and cost sensitive learning, minimization of the classification error rate is not an appropriate approach due to class skew and cost distributions. Thus the area under the ROC Curve ...
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In the field of imbalance learning and cost sensitive learning, minimization of the classification error rate is not an appropriate approach due to class skew and cost distributions. Thus the area under the ROC Curve (AUC) has been widely utilized to assess the performance of the classifiers in such cases. The Maximum AUC Linear Classifier (MALC), aiming at maximizing AUC directly, is a nonparametric linear classifier. MALC is based on the analysis of Wilcoxon-Mann-Whitney statistic of each single feature and on greedy pairwise combinations of the features. This paper finds that the MALC searches the solution in a much constrained resolution space. Furthermore, the heuristic method for guiding the structure of the classifier is worthy of notice. In this paper the Enhanced MALC (EMALC) is proposed. In the EMALC, two modifications are presented. Modification 1 aims at extensive searching in the solution space. Modification 2 modifies the way that MALC guides to induce the structure of the classifier. Experimental studies are carried out on a broad range of real world dataset. And the proposed methods have shown significant effect.
An example-based approach for facial portrait style learning is proposed. By learning a training set with the same style, this method can generate new portraitures which are similar to this style. To obtain facial fea...
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An example-based approach for facial portrait style learning is proposed. By learning a training set with the same style, this method can generate new portraitures which are similar to this style. To obtain facial features with high quality, there are two key elements in this paper: Using an Inhomogeneous Markov Random Field Model (MRF) and a nonparametric sampling scheme to learn the statistical relationship between the original images and the corresponding drawings, by identifying the facial contour area, the facial structure is trained from examples independently. Therefore, the output portraits can obtain more details with a clear and complete facial contour, while reducing the noise. Furthermore, an improved multi-samples texture synthesis method is also proposed to speed up the texture synthesis process without loss of the detail. Experimental results show that this approach is more efficient especially in the large image size and can generate satisfying new portraits of the desired styles.
This paper presents a feature extraction method for hand gesture based on multi-layer perceptron. The feature of hand skin color in the YCbCr color space is used to detect hand gesture. The hand silhouette and feature...
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This paper presents a feature extraction method for hand gesture based on multi-layer perceptron. The feature of hand skin color in the YCbCr color space is used to detect hand gesture. The hand silhouette and features can be accurately extracted in means of binarizing the hand image and enhancing the contrast. Median and smoothing filters are integrated to remove the noise. Combinational parameters of Hu invariant moment, hand gesture region, and Fourier descriptor are created to form a new feature vector which can recognize hand gesture. To confirm the robustness of this proposed method, a dataset including 3500 images is built. Experimental results demonstrate that our system can successfully recognize hand gesture with 97.4% recognition rate.
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