Spatial features of hyperspectral imagery (HSI) have gained an increasing attention in the latest years. Considering deep convolutional neural network (CNN) can extract a hierarchy of increasingly spatial features, this paper proposes an HSI reconstruction model based on deep CNN to enhance spatial features. The framework proposes a new spatial features-based strategy for band selection to define training label with rich information for the first time. Then, hyperspectral data is trained by deep CNN to build a model with optimized parameters which is suitable for HSI reconstruction. Finally, the reconstructed image is classified by the efficient extreme learning machine (ELM) with a very simple structure. Experimental results indicate that framework built based on CNN and ELM provides competitive performance with small number of training samples. Specifically, by using the reconstructed image, the average accuracy of ELM can be improved as high as 30.04%, while performs tens to hundreds of times faster than those state-of-the-art classifiers.

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%.

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.

This paper presents a novel computer-aided diagnosis (CAD) system for the diagnosis of breast cancer based on extreme learning machine (ELM). In view of a mammographic image, it is first eliminated interference in the preprocessing stages. Then, the preprocessed images are segmented by the level set model we proposed. Subsequently, a model of multidimensional feature vectors is built. Since not every feature vector contributes to the improvement of performance, feature selection is done by the combination of support vector machine (SVM) and extreme learning machine (ELM). Finally, an optimal subset of feature vectors is inputted into the classifiers for distinguishing malignant masses from benign ones. We also compare our breast mass classification approach based on ELM with several state-of-the-art classification models, and the results show that the proposed CAD system not only has good performance in terms of specificity, sensitivity and accuracy, but also achieves a significant reduction in training time compared with SVM and particle swarm optimization-support vector machine (PSO-SVM). Ultimately, our system achieves the better performance with average accuracy of 96.02% which indicates that the proposed segmentation model, the utilization of selected feature vectors and the effective classifier ELM provide satisfactory system.

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.