Endmember extraction is a process to identify the hidden pure source signals from the mixture. In the past decade, numerous algorithms have been proposed to perform this estimation. One commonly used ...assumption is the presence of pure pixels in the given image scene, which are detected to serve as endmembers. When such pixels are absent, the image is referred to as the highly mixed data, for which these algorithms at best can only return certain data points that are close to the real endmembers. To overcome this problem, we present a novel method without the pure-pixel assumption, referred to as the minimum volume constrained nonnegative matrix factorization (MVC-NMF), for unsupervised endmember extraction from highly mixed image data. Two important facts are exploited: First, the spectral data are nonnegative; second, the simplex volume determined by the endmembers is the minimum among all possible simplexes that circumscribe the data scatter space. The proposed method takes advantage of the fast convergence of NMF schemes, and at the same time eliminates the pure-pixel assumption. The experimental results based on a set of synthetic mixtures and a real image scene demonstrate that the proposed method outperforms several other advanced endmember detection approaches
Energy disaggregation or non-intrusive load monitoring addresses the issue of extracting device-level energy consumption information by monitoring the aggregated signal at one single measurement ...point without installing meters on each individual device. Energy disaggregation can be formulated as a source separation problem, where the aggregated signal is expressed as linear combination of basis vectors in a matrix factorization framework. In this paper, an approach based on Sum-to-k constrained non-negative matrix factorization (S2K-NMF) is proposed. By imposing the sum-to-k constraint and the non-negative constraint, S2K-NMF is able to effectively extract perceptually meaningful sources from complex mixtures. The strength of the proposed algorithm is demonstrated through two sets of experiments: Energy disaggregation in a residential smart home; and heating, ventilating, and air conditioning components energy monitoring in an industrial building testbed maintained at the Oak Ridge National Laboratory. Extensive experimental results demonstrate the superior performance of S2K-NMF as compared to state-of-the-art decomposition-based disaggregation algorithms.
If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not ...necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5? The answer is probably a No. Most existing face aging works attempt to learn the transformation between age groups and thus would require the paired samples as well as the labeled query image. In this paper, we look at the problem from a generative modeling perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can directly produce the image with desired age attribute. We propose a conditional adversarial autoencoder (CAAE) that learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously. In CAAE, the face is first mapped to a latent vector through a convolutional encoder, and then the vector is projected to the face manifold conditional on age through a deconvolutional generator. The latent vector preserves personalized face features (i.e., personality) and the age condition controls progression vs. regression. Two adversarial networks are imposed on the encoder and generator, respectively, forcing to generate more photo-realistic faces. Experimental results demonstrate the appealing performance and flexibility of the proposed framework by comparing with the state-of-the-art and ground truth.
Human action recognition based on the depth information provided by commodity depth sensors is an important yet challenging task. The noisy depth maps, different lengths of action sequences, and free ...styles in performing actions, may cause large intra-class variations. In this paper, a new framework based on sparse coding and temporal pyramid matching (TPM) is proposed for depth-based human action recognition. Especially, a discriminative class-specific dictionary learning algorithm is proposed for sparse coding. By adding the group sparsity and geometry constraints, features can be well reconstructed by the sub-dictionary belonging to the same class, and the geometry relationships among features are also kept in the calculated coefficients. The proposed approach is evaluated on two benchmark datasets captured by depth cameras. Experimental results show that the proposed algorithm repeatedly achieves superior performance to the state of the art algorithms. Moreover, the proposed dictionary learning method also outperforms classic dictionary learning approaches.
In this paper, we study the compressed sensing (CS) image recovery problem. The traditional method divides the image into blocks and treats each block as an independent sub-CS recovery task. This ...often results in losing global structure of an image. In order to improve the CS recovery result, we propose a nonlocal (NL) estimation step after the initial CS recovery for denoising purpose. The NL estimation is based on the well-known NL means filtering that takes an advantage of self-similarity in images. We formulate the NL estimation as the low-rank matrix approximation problem, where the low-rank matrix is formed by the NL similarity patches. An efficient algorithm, nonlocal Douglas-Rachford (NLDR), based on Douglas-Rachford splitting is developed to solve this low-rank optimization problem constrained by the CS measurements. Experimental results demonstrate that the proposed NLDR algorithm achieves significant performance improvements over the state-of-the-art in CS image recovery.
The technology of color filter arrays (CFA) has been widely used in the digital camera industry since it provides several advantages like low cost, exact registration, and strong robustness. The same ...motivations also drive the design of multispectral filter arrays (MSFA), in which more than three spectral bands are used. Although considerable research has been reported to optimally reconstruct the full-color image using various demosaicking algorithms, studies on the intrinsic properties of these filter arrays as well as the underlying design principles have been very limited. Given a set of representative spectral bands, the design of an MSFA involves two issues: the selection of tessellation mechanisms and the arrangement/layout of different spectral bands. We develop a generic MSFA generation method starting from a checkerboard pattern. We show, through case studies, that most of the CFAs currently used by the industry can be derived as special cases of MSFAs generated using the generic algorithm. The performance of different MSFAs are evaluated based on their intrinsic properties, namely, the spatial uniformity and the spectral consistency. We design two metrics, static coefficient and consistency coefficient, to measure these two parameters, respectively. The experimental results demonstrate that the generic algorithm can generate optimal or near-optimal MSFAs in both the rectangular and the hexagonal domains
Accurate event analysis in real time is of paramount importance for high-fidelity situational awareness such that proper actions can take place before any isolated faults escalate to cascading ...blackouts. Existing approaches are limited to detect only single or double events or a specified event type. Although some previous works can well distinguish multiple events in small-scale systems, the performance tends to degrade dramatically in large-scale systems. In this paper, we focus on multiple event detection, recognition, and temporal localization in large-scale power systems. We discover that there always exist "regions" where the reaction of all buses to certain event within each region demonstrates high degree similarity, and that the boundary of the "regions" generally remains the same regardless of the type of event(s). We further verify that, within each region, this reaction to multiple events can be approximated as a linear combination of reactions to each constituent event. Based on these findings, we propose a novel method, referred to as cluster-based sparse coding (CSC), to extract all the underlying single events involved in a multievent scenario. Multiple events of three typical disturbances (e.g., generator trip, line trip, and load shedding) can be detected and recognized. Specifically, the CSC algorithm can effectively distinguish line trip events from oscillation, which has been a very challenging task for event analysis. Experimental results based on simulated large-scale system model (i.e., NPCC) show that the proposed CSC algorithm presents high detection and recognition rate with low false alarms.
This paper addresses the problem of learning over-complete dictionaries for the coupled feature spaces, where the learned dictionaries also reflect the relationship between the two spaces. A Bayesian ...method using a beta process prior is applied to learn the over-complete dictionaries. Compared to previous couple feature spaces dictionary learning algorithms, our algorithm not only provides dictionaries that customized to each feature space, but also adds more consistent and accurate mapping between the two feature spaces. This is due to the unique property of the beta process model that the sparse representation can be decomposed to values and dictionary atom indicators. The proposed algorithm is able to learn sparse representations that correspond to the same dictionary atoms with the same sparsity but different values in coupled feature spaces, thus bringing consistent and accurate mapping between coupled feature spaces. Another advantage of the proposed method is that the number of dictionary atoms and their relative importance may be inferred non-parametrically. We compare the proposed approach to several state-of-the-art dictionary learning methods by applying this method to single image super-resolution. The experimental results show that dictionaries learned by our method produces the best super-resolution results compared to other state-of-the-art methods.
Target detection aims to locate targets of interest within a specific scene. The traditional model-driven detectors based on signal processing have proved to be very effective. However, the detection ...performance of such traditional methods relies heavily on the model assumption, which is limited by the discrepancy with real hyperspectral images (HSIs) data. In this article, a target detection method through tree-structured encoding (TD-TSE) for HSIs is proposed. Instead of modeling the target and the background to extract valid features, we construct a binary tree based on the features of the data itself and segment the HSI to improve the separability of the target and the background. For the purpose of highlighting the target and suppressing the background, a novel measurement of separation, distance on tree, is calculated via binary encoding based on the constructed tree structure, and the detection output can be obtained according to such distance. To further reduce the generalization error resulting from random subsampling, the statistical average of the distances on multiple independent trees is estimated to improve the robustness of TD-TSE. The proposed method is not constrained by any model assumptions, which is fundamentally different from the most widely used hyperspectral target detectors in the field of signal processing. Moreover, the construction of binary trees without any labeled samples and the linear complexity of the proposed method make it highly practical for the hyperspectral data in real scenes. Extensive experiments on three benchmark HSI data sets demonstrate the effectiveness of the proposed TD-TSE for hyperspectral target detection.
Barrier coverage is a critical issue in wireless sensor networks (WSNs) for security applications, which however cannot be guaranteed to be formed after initial random deployment of sensors. Existing ...work on barrier coverage mainly focus on homogeneous WSNs, while little effort has been made on exploiting barrier coverage formation in heterogeneous WSNs where different types of sensors are deployed with different sensing models and costs. In this paper, we study how to efficiently form barrier coverage by leveraging multiple types of mobile sensors to fill in gaps between pre-deployed stationary sensors in heterogeneous WSNs. The stationary sensors are grouped into clusters and a cluster-based directional barrier graph is proposed to model the barrier coverage formation problem. We prove that the minimum cost of mobile sensors required to form a barrier with stationary sensors is the length of the shortest path on the graph. Moreover, we propose a greedy movement algorithm for heterogeneous WSNs to efficiently schedule different types of mobile sensors to different gaps while minimizing the total moving cost. In particular, we formulate the movement problem for homogeneous WSNs as a minimum cost bipartite assignment problem, and solve it in polynomial time using the Hungarian algorithm. Extensively experimental results on homogeneous and heterogeneous WSNs demonstrate the effectiveness of the proposed algorithms.
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