Sparse Representation-based Method Research Articles

A sparse representation-based method for infrared dim target detection under sea–sky background

Automatic detection for infrared (IR) dim targets under complex sea–sky background is a challenging task. To explore an effective solution to the problem, this paper develops a sparse representation-based method by learning a sea–sky background dictionary. This framework is mainly composed of three modules: background dictionary learning, preliminary target localization, and accurate target identification. In the first module, a sea–sky background dictionary is learned from a large number of training samples, which has a good ability to model the cluttered sea–sky background. In the second module, given a test image, it is first divided into a set of patches; then, for each image patch, its sparse representation coefficients are computed over the learned dictionary. By analyzing the sparse reconstruction errors for the image patches, the target candidate areas can be predicted. In the third module, an infrared dim target recognition scheme is applied to those areas to recognize the true dim IR targets. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than several other infrared dim target detection methods.

Penrose demosaicking.

The Penrose pixel layout, an aperiodic pixel layout in rhombus Penrose tiling, has been shown to substantially outperform the existing square pixel layout in super-resolution. However, it was tested only on grayscale images. To study its performance on color images, we have to reconstruct regular color images from Penrose raw images, i.e., images with only one color component at each Penrose pixel, resulting in the problem of demosaicking from Penrose pixels. Penrose demosaicking is more difficult than regular demosaicking, because none of the color components of the reconstructed regular color images are available. Therefore, most of the traditional demosaicking methods do not apply. We develop a sparse representation-based method for Penrose demosaicking. Extensive experiments show that Penrose pixel layout outperforms regular pixel layouts in terms of both perceptual evaluation and S-CIELAB. The Penrose pixel layout is unique among all irregular layouts because it is uniformly three-colorable and it has only two pixel shapes, thick and thin rhombi, making its manufacturing relatively easy.

Weighted joint sparse representation-based classification method for robust alignment-free face recognition

This work proposes a weighted joint sparse representation (WJSR)-based classification method for robust alignment-free face recognition, in which an image is represented by a set of scale-invariant feature transform descriptors. The proposed method considers the correlation and the reliability of the query descriptors. The reliability is measured by the similarity information between the query descriptors and the atoms in the dictionary, which is incorporated into the l0∖l2-norm minimization to seek the optimal WJSR. Compared with the related state-of-art methods, the performance is advanced, as verified by the experiments on the benchmark face databases.

A novel virtual samples-based sparse representation method for face recognition

A limited training set usually limits the performance of face recognition in practice. Even sparse representation-based methods which outperform in face recognition cannot avoid such situation. In order to effectively improve recognition accuracy of sparse representation-based methods on a limited training set, a novel virtual samples-based sparse representation (VSSR) method for face recognition is proposed in this paper. In the proposed method, virtual training samples are constructed to enrich the size and diversity of a training set and a sparse representation-based method is used to classify test samples. Extensive experiments on different face databases confirm that VSSR is robust to illumination variations and works better than many representative representation-based face recognition methods.

Mobile Robot Aided Silhouette Imaging and Robust Body Pose Recognition for Elderly-Fall Detection

This article introduces a mobile infrared silhouette imaging and sparse representation-based pose recognition for building an elderly-fall detection system. The proposed imaging paradigm exploits the novel use of the pyroelectric infrared (PIR) sensor in pursuit of body silhouette imaging. A mobile robot carrying a vertical column of multi-PIR detectors is organized for the silhouette acquisition. Then we express the fall detection problem in silhouette image-based pose recognition. For the pose recognition, we use a robust sparse representation-based method for fall detection. The normal and fall poses are sparsely represented in the basis space spanned by the combinations of a pose training template and an error template. The ℓ1 norm minimizations with linear programming (LP) and orthogonal matching pursuit (OMP) are used for finding the sparsest solution, and the entity with the largest amplitude encodes the class of the testing sample. The application of the proposed sensing paradigm to fall detection is addressed in the context of three scenarios, including: ideal non-obstruction, simulated random pixel obstruction and simulated random block obstruction. Experimental studies are conducted to validate the effectiveness of the proposed method for nursing and homeland healthcare.

A joint sparse representation-based method for double-trial evoked potentials estimation

In this paper, we present a novel approach to solving an evoked potentials estimating problem. Generally, the evoked potentials in two consecutive trials obtained by repeated identical stimuli of the nerves are extremely similar. In order to trace evoked potentials, we propose a joint sparse representation-based double-trial evoked potentials estimation method, taking full advantage of this similarity. The estimation process is performed in three stages: first, according to the similarity of evoked potentials and the randomness of a spontaneous electroencephalogram, the two consecutive observations of evoked potentials are considered as superpositions of the common component and the unique components; second, making use of their characteristics, the two sparse dictionaries are constructed; and finally, we apply the joint sparse representation method in order to extract the common component of double-trial observations, instead of the evoked potential in each trial. A series of experiments carried out on simulated and human test responses confirmed the superior performance of our method.

A Sparse Representation-Based Deployment Method for Optimizing the Observation Quality of Camera Networks

Deployment is a critical issue affecting the quality of service of camera networks. The deployment aims at adopting the least number of cameras to cover the whole scene, which may have obstacles to occlude the line of sight, with expected observation quality. This is generally formulated as a non-convex optimization problem, which is hard to solve in polynomial time. In this paper, we propose an efficient convex solution for deployment optimizing the observation quality based on a novel anisotropic sensing model of cameras, which provides a reliable measurement of the observation quality. The deployment is formulated as the selection of a subset of nodes from a redundant initial deployment with numerous cameras, which is an ℓ0 minimization problem. Then, we relax this non-convex optimization to a convex ℓ1 minimization employing the sparse representation. Therefore, the high quality deployment is efficiently obtained via convex optimization. Simulation results confirm the effectiveness of the proposed camera deployment algorithms.

Sparse representation-based color visualization method for hyperspectral imaging

In this paper, we designed a color visualization model for sparse representation of the whole hyperspectral image, in which, not only the spectral information in the sparse representation but also the spatial information of the whole image is retained. After the sparse representation, the color labels of the effective elements of the sparse coding dictionary are selected according to the sparse coefficient and then the mixed images are displayed. The generated images maintain spectral distance preservation and have good separability. For local ground objects, the proposed single-pixel mixed array and improved oriented sliver textures methods are integrated to display the specific composition of each pixel. This avoids the confusion of the color presentation in the mixed-pixel color display and can also be used to reconstruct the original hyperspectral data. Finally, the model effectiveness was proved using real data. This method is promising and can find use in many fields, such as energy exploration, environmental monitoring, disaster warning, and so on.

A new sparse representation-based classification algorithm using iterative class elimination

In this paper, we propose a sparse representation-based classification method using iterative class elimination strategy for face recognition. The proposed method aims to represent a test sample as a linear combination of the most competitive training samples and exploits an optimal representation of training samples from the classes with major relevant contributions. We interpret the sparse representation problem as an information fidelity problem. In the context of our proposed method, an important goal is to select a subset of variables for accomplishing one objective: the provision of a descriptive representation for sparse class knowledge structure. We develop an iterative class elimination algorithm to achieve this goal. First, the contribution in presenting the test sample of any of the specified classes is, respectively, calculated by adding up the total contribution of all training samples of this class, and then a certain class that meets the smallest score requirement to this test sample is eliminated. Second, a similar procedure is iteratively carried out for the set of remaining training samples from rest classes, and this procedure is repeatedly performed till the predefined termination condition is satisfied. The final remaining training samples are used to produce a best representation of the test sample and to classify it. Therefore, the proposed algorithm is an iterative method that alternates between sparse representation and a process of updating the training atoms to better fit the test data. This is helpful to accurately classify the test sample. Experimental results conducted on the ORL, FERET, and AR face databases demonstrate the effectiveness of the proposed method.

Denoising human cardiac diffusion tensor magnetic resonance images using sparse representation combined with segmentation

Cardiac diffusion tensor magnetic resonance imaging (DT-MRI) is noise sensitive, and the noise can induce numerous systematic errors in subsequent parameter calculations. This paper proposes a sparse representation-based method for denoising cardiac DT-MRI images. The method first generates a dictionary of multiple bases according to the features of the observed image. A segmentation algorithm based on nonstationary degree detector is then introduced to make the selection of atoms in the dictionary adapted to the image's features. The denoising is achieved by gradually approximating the underlying image using the atoms selected from the generated dictionary. The results on both simulated image and real cardiac DT-MRI images from ex vivo human hearts show that the proposed denoising method performs better than conventional denoising techniques by preserving image contrast and fine structures.