Deep neural networks (DNNs) have always been a popular base model in many image classification tasks. However, some recent works suggest that there are some man made images will easily lead DNNs give wrong predictions...
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image registration is a challenging task when illumination changes drastically. In this paper, we propose a novel algorithm, APCurve, to address this problem. APCurve matches all principle curves extracted from the se...
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Independent Component Analysis (ICA) feature extraction is an efficient sparse coding method for noise suppression. However, single channel signal can not be directly applied in ICA feature extraction. In this paper, ...
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This paper presents a novel method for image segmentation. We improved the effect of saliency detection for the purpose of image segmentation. Graph cuts are used to find global optimal segmentation of N-dimensional i...
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image quality evaluation is becoming essential in many imageprocessing problems. This paper proposes a new image quality evaluation approach based on decision fusion method of canonical correlation analysis (CCA). By...
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With the rapid development of artificial intelligence technology, more and more intelligent devices are beginning to be manufactured. At the same time, the demand of the image resolution is getting higher. Super-resol...
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A method for 3D shape reconstruction of deformable surfaces from consecutive frames was presented. In our method, the model of the surface is represented by a triangulated mesh. The constraints for the model, includin...
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The general single image super-resolution methods mainly extract features from the high-resolution (HR) space by the pre-upscaling step at the beginning of the network or from the low-resolution (LR) space before the ...
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By introducing the velocity difference between the preceding car and the car before the preceding one into the optimal velocity model (OVM), we present an extended dynamical model which takes into account the next-nea...
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For medical image classification, annotations for images are laborious and expensive, which is suitable for the application of semi-supervised learning. Mainstream semi-supervised learning methods develop a consistenc...
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