A sparse and low-rank matrix decomposition-based method is proposed for anomaly detection in hyperspectral data. High-dimensional data are decomposed into low-rank and sparse matrices representing background and anomalies, respectively. The problem of the decomposition process is defined from the dictionary learning point of view. Therefore, our way of obtaining these matrices differs from previous studies. It aims to find a correct partition of the data and separate anomaly pixels from the background. After decomposition, Mahalanobis distance is applied to the sparse part of the data to get anomaly locations. Three hyperspectral data sets are used for evaluation. Experimental results suggest that anomaly detection performance of the proposed method surpasses those of the state-of-the-art methods.
Early detection of fires is an important aspect of public safety. In the past decades, devices and systems have been developed for volumetric sensing of fires using non-conventional techniques, such as, computer vision based methods and pyro-electric infrared sensors. These systems pose an alternative for more commonly used point detectors, which suffer from transport delay in large and open areas. The ubiquity of computing and recent developments on novel hardware alternatives, like memristor crossbar arrays, promise an increase in the number of deployments of such systems. Existing video-based methods have been developed for the analysis of uncompressed spatio-temporal sequences. In order to respond the growing demand of such systems, techniques specifically aimed at analyzing compressed domain video streams should be developed for early fire detection purposes. In this paper, a Markov model and wavelet transform based technique is proposed to further improve the current state-of-the-art methods for video smoke detection by detecting signs of smoke existence in the MJPEG2000 compressed video.
Hyperspectral data is composed of a set of correlated band images. In order to efficiently compress the hyperspectral imagery, this inherent correlation may be exploited by means of spectral decorrelators. In this paper, a fractional wavelet transform based method is introduced for spectral decorrelation of hyperspectral data. As opposed to regular wavelet transform which decomposes a given signal into two equal-length sub-signals, fractional wavelet transform is carried out by decomposing the signal corresponding to the spectral content into two sub-signals with different lengths. Sub-signal lengths are adapted to data to achieve a better spectral decorrelation. Performance results pertaining to AVIRIS datasets are presented in comparison with existing regular wavelet decomposition based compression methods.
A hyperspectral image compression method is proposed using an online dictionary learning approach. The online learning mechanism is aimed at utilizing least number of dictionary elements for each hyperspectral image under consideration. In order to meet this “sparsity constraint”, basis pursuit algorithm is used. Hyperspectral imagery from AVIRIS datasets are used for testing purposes. Effects of non-zero dictionary elements on the compression performance are analyzed. Results indicate that, the proposed online dictionary learning algorithm may be utilized for higher data rates, as it performs better in terms of PSNR values, as compared with the state-of-the-art predictive lossy compression schemes.
In this paper, an online adaptive decision fusion framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several sub-algorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular sub-algorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing orthogonal projections onto convex sets describing sub-algorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system is developed to evaluate the performance of the algorithm in handling the problems where data arrives sequentially. In this case, the oracle is the security guard of the forest lookout tower verifying the decision of the combined algorithm. Simulation results are presented.
Shadows constitute a problem in many moving object detection and tracking algorithms in video. Usually, moving
shadow regions lead to larger regions for detected objects. Shadow pixels have almost the same chromaticity as the
original background pixels but they only have lower brightness values. Shadow regions usually retain the underlying
texture, surface pattern, and color value. Therefore, a shadow pixel can be represented as a.x where x is the actual
background color vector in 3-D RGB color space and a is a positive real number less than 1. In this paper, a shadow
detection method based on two-dimensional (2-D) cepstrum is proposed.
A novel method to detect flames in infrared (IR) video is proposed. Image regions containing flames appear as bright regions in IR video. In addition to ordinary motion and brightness clues, the flame flicker process is also detected by using a hidden Markov model (HMM) describing the temporal behavior. IR image frames are also analyzed spatially. Boundaries of flames are represented in wavelet domain and the high frequency nature of the boundaries of fire regions is also used as a clue to model the flame flicker. All of the temporal and spatial clues extracted from the IR video are combined to reach a final decision. False alarms due to ordinary bright moving objects are greatly reduced because of the HMM-based flicker modeling and wavelet domain boundary modeling.