Although hyperspectral images (HSIs) captured by satellites provide much information in spectral regions, some bands are redundant or have large amounts of noise, which are not suitable for image analysis. To address this problem, we introduce a method for reconstructing the HSI with noise reduction and contrast enhancement using a matting model for the first time. The matting model refers to each spectral band of an HSI that can be decomposed into three components, i.e., alpha channel, spectral foreground, and spectral background. First, one spectral band of an HSI with more refined information than most other bands is selected, and is referred to as an alpha channel of the HSI to estimate the hyperspectral foreground and hyperspectral background. Finally, a combination operation is applied to reconstruct the HSI. In addition, the support vector machine (SVM) classifier and three sparsity-based classifiers, i.e., orthogonal matching pursuit (OMP), simultaneous OMP, and OMP based on first-order neighborhood system weighted classifiers, are utilized on the reconstructed HSI and the original HSI to verify the effectiveness of the proposed method. Specifically, using the reconstructed HSI, the average accuracy of the SVM classifier can be improved by as much as 19%.

In order to improve the robustness of the distribution fields(DF)as an object model in object tracking,we propose a mutli-feature fusion framework for the distribution fields.In the original DF-based method,the density histogram was used to estimate the DF of a pixel,but the structural information was ignored.For effective representation of the structural information in the DFs,a special type of coding for the featured points which contain structural information is merged into the DFs.Experiments show that the new method outperforms the original method and four other state-of-the-art tracking algorithms for some challenging video clips.

This study explores the use of the graphics processing units (GPUs) for performing the two-dimensional discrete wavelet transform (DWT) of images. The study of fast wavelet transforms has been driven both by the enormous volumes of data produced by modern cameras and by the need for real-time processing of these data. With the emergence of general computing on GPUs, many time-consuming applications have started to reap the associated benefits. In the implementation of a GPU-based DWT, two approaches are used according to the published works, which are the row–column (RC) approach and the block-based (BB) approach. Most state-of-the-art techniques are based on the RC approach, which utilises the parallelism between different rows and columns; few works are based on the BB approach, which explores the parallelism between different blocks of the image. Although easy to implement, resource usage of the RC approach is usually related to the image size. Another shortcoming of the RC approach lies in the fact, according to the author's analysis, that more global memory access is required. The authors thus select the BB approach in this study. Experiment results show that the proposed BB approach outperforms the RC approach, being 99× faster than a native CPU implementation for 4096 × 4096 images.

A novel approach to mammographic image segmentation, termed as PCNN-based level set algorithm, is presented in this paper. As well known, it is difficult to robustly achieve mammogram image segmentation due to low contrast between normal and lesion tissues. Therefore, Pulse Coupled Neural Network (PCNN) algorithm is firstly employed to achieve mammary-specific and mass edge detection for subsequently extracting contour as the initial zero level set. The proposed scheme accurately obtains the initial contour for level set evolution, which does not suffer from the drawback that level set method is sensitive to the initial contour. Especially, an improved level set evolution is performed to segment the images and get the final results. A preliminary evaluation of the proposed method performs on a known public database, namely MIAS, which demonstrates that the proposed framework in this paper can potentially obtain better masses detection results than traditional CV and VFC model in terms of accuracy.

Extreme learning machine (ELM) is of great interest to the machine learning society due to its extremely simple training step. Its performance sensitivity to the number of hidden neurons is studied under the context of hyperspectral remote sensing image classification. An empirical linear relationship between the number of training samples and the number of hidden neurons is proposed. Such a relationship can be easily estimated with two small training sets and extended to large training sets to greatly reduce computational cost. The kernel version of ELM (KELM) is also implemented with the radial basis function kernel, and such a linear relationship is still suitable. The experimental results demonstrated that when the number of hidden neurons is appropriate, the performance of ELM may be slightly lower than the linear SVM, but the performance of KELM can be comparable to the kernel version of SVM (KSVM). The computational cost of ELM and KELM is much lower than that of the linear SVM and KSVM, respectively.