Object recognition from images is one of the essential problems in automatic imageprocessing. In this paper we focus specifically on nearest neighbor methods, which are widely used in many practical applications, not...
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In this paper, we establish a correspondence between the incremental algorithm for computing AT-models [8,9] and the one for computing persistent homology [6,14,15]. We also present a decremental algorithm for computi...
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With the explosion of protein sequences generated in the Post-Genomic Age, it is urgent to develop an automated method to predict protein quaternary structure. To explore this problem, we adopted an approach based on ...
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The safety operation of steel cable is vital and the cable should be maintained through regular inspection. Magnetic flux leakage (MFL) method is a popular inspection technique. For the online nondestructive testing (...
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A magnetic sensor based on giant magnetoimpedance effect (GMI) was developed. The basic element of the sensor is a Fe-based nanocrystalline ribbon of composition Fe73.5Cu1Nb3Si13.5B9. A sensitivity of 0.6691 V/Oe for ...
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Existing approaches for automatic image annotation usually suffer from two issues: (1) lacking a good quality distance metric for image semantic similarity measure; (2) rarely considering the correlation between label...
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Existing approaches for automatic image annotation usually suffer from two issues: (1) lacking a good quality distance metric for image semantic similarity measure; (2) rarely considering the correlation between labels assigned to each image. In this paper, we aim to resolve both of the problems simultaneously in a novel unified framework. Specifically, a proper distance metric is learned based on the structural SVM in a discriminative manner, which can optimize the ranking of the images induced by distances from a test image. Subsequently, a collaborative label propagation algorithm is leveraged to model the correlation between class labels in an explicit manner. Also, the learned metric is embedded in the propagation model. The integration of the two components leads to more accurate annotation results. The experiments conducted on the Corel dataset demonstrate the effectiveness of the proposed unified framework.
Deblurring camera-based document image is an important task in digital document processing, since it can improve both the accuracy of optical character recognition systems and the visual quality of document images. Tr...
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Coherence-enhancing diffusion (CED), based on analysis of oriented structures, has been extensively used in imageprocessing. This diffusion filtering can keep some junctions and close broken linear structures, but it...
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In this paper, we propose a novel metric for image quality assessment based on the ratio of Non-shift Edge (rNSE), whose elegance lies in succinctness and effectiveness. In this metric, an image is filtered by the LOG...
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In this paper, we propose a novel metric for image quality assessment based on the ratio of Non-shift Edge (rNSE), whose elegance lies in succinctness and effectiveness. In this metric, an image is filtered by the LOG operator, who acts like the classical receptive field, and the edge points are detected as the zero-crossings of the filtered image. Then the binary Non-shift Edge (NSE) map is derived to represent the strong edge structure remained in the distorted image. The perceptual quality is calculated by the ratio of NSE. The performance of rNSE in the scale-threshold plane shows similar frequency and threshold selectivity. Comparing with the existing well-designed metrics, the proposed rNSE performs equivalently in accuracy and consistency.
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