Digital image processing is an interdisciplinary course, which needs students have a strong background in mathematics. To help them change from passive learning to active learning, teaching reform and innovation of th...
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Digital image processing is an interdisciplinary course, which needs students have a strong background in mathematics. To help them change from passive learning to active learning, teaching reform and innovation of the course "Digital Image processing Experiments" is discussed in this paper. The combined platform of experiments includes TI DSP experimental box and three different kinds of programming languages. The six experimental projects cover the basic theories of digital image processing. The result can offer a significant reference for the teaching innovations in the other related specialties.
In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. Howe...
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In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. However, LDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), can significantly reduce the class separation problem. Furthermore, maximizing the harmonic mean of Kullback-Leibler (KL) divergences of class pairs (MHMD) emphasizes smaller divergences more than MGMD, and deals with the class separation problem more effectively. However, in many applications, labeled data are very limited while unlabeled data can be easily obtained. The estimation of divergences of class pairs is unstable using inadequate labeled data. To take advantage of unlabeled data for subspace selection, semi-supervised MHMD (SSMHMD) is proposed using graph Laplacian as normalization. Quasi-Newton method is adopted to solve the optimization problem. Experiments on synthetic data and real image data show the validity of SSMHMD.
A 5-parameter bundle adjustment method is proposed in this paper for global mosaic of an image sequence. By decomposing the rotation matrix into a 3-parameter rotation axis and a rotation angle, to each image, there a...
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As Magnetic Resonance Imaging (MRI) is an important technology of radiological evaluation and computeraided diagnosis, the accuracy of the MR image segmentation directly influences the validity of following processing...
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The rapid development of the Web technology makes the Web mining become the focus of the current data mining, and the XML technology also becomes the standard of the data exchange on the Web. The paper introduces the ...
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A 5-parameter bundle adjustment method is proposed in this paper for global mosaic of an image sequence. By decomposing the rotation matrix into a 3-parameter rotation axis and a rotation angle, to each image, there a...
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A 5-parameter bundle adjustment method is proposed in this paper for global mosaic of an image sequence. By decomposing the rotation matrix into a 3-parameter rotation axis and a rotation angle, to each image, there are 5 parameters that need estimate for rotation axis, rotation angle and focal length. The proposed method minimizes the distance between feature matches in 3D space so that these 5 parameters are refined gradually for final stitching of all images together. Experimental results suggest that this method is an useful extension to existing research with the advantages of real 3D camera motion, accurate 5-parameter rotation decomposition and reliable 3D feature definition.
A novel and efficient speckle noise reduction algorithm based on Bayesian contourlet shrinkage using contourlet transform is ***,we show the sub-band decompositions of SAR images using contourle transforms,which provi...
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A novel and efficient speckle noise reduction algorithm based on Bayesian contourlet shrinkage using contourlet transform is ***,we show the sub-band decompositions of SAR images using contourle transforms,which provides sparse representation at both spatial and directional ***,a Bayesian contourlet shrinkage factor is applied to the decomposed data to estimate the best value for noise-free contourle *** results show that compared with conventional wavelet despeckling algorithm,the proposed algorithm can achieve an excellent balance between suppresses speckle effectively and preserve image details,and the significant information of origina image like textures and contour details is well ma intained.
The purpose of this study is to present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differenti...
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The purpose of this study is to
present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differential equation （PDE） model, Kramer＇s PDE model. The usefulness of this method is investigated by experimental results. We apply this method to a medical X-ray image. For comparison, the X-ray image is also processed using classic Perona-Malik PDE model and Catte PDE model. Although the Perona-Malik model and Catte PDE model could also enhance the image, the quality of the enhanced images is considerably inferior compared with the enhanced image using Kramer＇s PDE model. The study suggests that the Kramer＇s PDE model is capable of enhancing medical X-ray images, which will make the X-ray images more reliable.
The purpose of this study is to present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differenti...
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The purpose of this study is to present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differential equation (PDE) model, Kramer’s PDE model. The usefulness of this method is investigated by experimental results. We apply this method to a medical X-ray image. For comparison, the X-ray image is also processed using classic Perona-Malik PDE model and Catte PDE model. Although the Perona-Malik model and Catte PDE model could also enhance the image, the quality of the enhanced images is considerably inferior compared with the enhanced image using Kramer’s PDE model. The study suggests that the Kramer’s PDE model is capable of enhancing medical X-ray images, which will make the X-ray images more reliable.
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