In this paper we propose a method for segmenting characters in multispectral images of ancient documents. Due to the low quality of the document images the main idea of our study is to combine the multispectral behavi...
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In this paper, we take the advantages of color contrast and color distribution to get high quality saliency maps. The overall procedure flow of our unified framework contains superpixel pre-segmentation, color contras...
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Multi-atlas segmentation methods will benefit from atlases covering the complete spectrum of population patterns, while the difficulties in generating such large enough datasets and the computation burden required in ...
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In this paper, we employ the concept of characteristic line to show some useful properties of planar homography matrix. These properties relate the characteristic line of a planar homography matrix with Euler angles o...
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Two medieval Slavonic manuscripts are recorded, investigated and analyzed by philologists in collaboration with computer scientists. The aim of the project is to develop algorithms that support the philologists by aut...
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With extracted local features of a given image, computing its global feature under perceptual framework has shown promising performance in object recognition. However, under some tough applications with large intra-cl...
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Histogram-based techniques are commonly used in image retrieval ranging from basic color histograms to sophisticated histograms of various local feature vectors. Our approach considers multidimensional histograms of l...
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Most web page classification algorithms are learning algorithms under the single-instance single-label framework. Multi-Instance Multi-Label learning is a new machine learning framework. MIMLSVM+ algorithm, using dege...
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Content-based image retrieval plays a key role in the management of a large image database. However, the results of existing approaches are not as satisfactory for the gap between visual features and semantic concepts...
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The trained Gaussian mixture model is used to make skincolour segmentation for the input image sequences. The hand gesture region is extracted, and the relative normalization images are obtained by interpolation opera...
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The trained Gaussian mixture model is used to make skincolour segmentation for the input image sequences. The hand gesture region is extracted, and the relative normalization images are obtained by interpolation operation. To solve the proem of hand gesture recognition, Fuzzy-Rough based nearest neighbour(RNN) algorithm is applied for classification. For avoiding the costly compute, an improved nearest neighbour classification algorithm based on fuzzy-rough set theory (FRNNC) is proposed. The algorithm employs the represented cluster points instead of the whole training samples, and takes the hand gesture data's fuzziness and the roughness into account, so the campute spending is decreased and the recognition rate is increased. The 30 gestures in Chinese sign language alphabet are used for approving the effectiveness of the proposed algorithm. The recognition rate is 94.96%, which is better than that of KNN (K nearest neighbor)and Fuzzy- KNN (Fuzzy K nearest neighbor).
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