Time-frequency distributions (TFDs) allow direction of arrival (DOA) estimation algorithms to be used in scenarios when the total number of sources are more than the number of sensors. The performa ...

Keypoint-based and block-based methods are two main categories of techniques for detecting copy-move forged images, one of the most common digital image forgery schemes. In general, block-based methods suffer from high computational cost due to the large number of image blocks used and fail to handle geometric transformations. On the contrary, keypoint-based approaches can overcome these two drawbacks yet are found difficult to deal with smooth regions. As a result, fusion of these two approaches is proposed for effective copy-move forgery detection. First, our scheme adaptively determines an appropriate initial size of regions to segment the image into non-overlapped regions. Feature points are extracted as keypoints using the scale invariant feature transform (SIFT) from the image. The ratio between the number of keypoints and the total number of pixels in that region is used to classify the region into smooth or non-smooth (keypoints) regions. Accordingly, block based approach using Zernike moments and keypoint based approach using SIFT along with filtering and post-processing are respectively applied to these two kinds of regions for effective forgery detection. Experimental results show that the proposed fusion scheme outperforms the keypoint-based method in reliability of detection and the block-based method in efficiency.

Texture classification is a challenging task due to the wide range of natural texture types and large intra-class variations in texture images, such as different rotations, scales, positions and lighting conditions. Many existing methods for extracting texture features are designed carefully by user for specific applications. The extracted texture features are then used as input to various classification methods, such as support vector machines, to classify the textures. The system performance greatly depends on the feature extractor. Unfortunately, there is no systematic approach for feature extractor design. In this paper, we propose a method called extreme learning machine with multi-scale local receptive fields (ELM-MSLRF) to achieve feature learning and classification simultaneously for texture classification. In contrast to traditional methods, the proposed method learns the features by the network itself and can be applied to more general applications. Additionally, it is fast and requires few computations. Experiments on the ALOT texture dataset demonstrate the attractive performance of ELM-MSLRF even compared with the state-of-the-art algorithms. Moreover, the proposed ELM-MSLRF achieves the best performance on the NORB dataset.

Remote sensing image classification is a very challenging problem and covariance descriptor can be introduced in the feature extraction and representation process for remote sensing image. However, due to the reason that covariance descriptor lies in non-Euclidean manifold, conventional extreme learning machine (ELM) cannot effectively deal with this problem. In this paper, we propose an improved ELM framework which incorporates the collaborative coding to tackle the covariance descriptor classification problem. First, a new ELM-guided dictionary learning and coding model is proposed. Then the iterative optimization algorithm is developed to solve the model. By evaluating the proposed approach on the UCMERCED high-resolution aerial image dataset, we show the effectiveness of the proposed strategy.

We present an algorithm for two-dimensional (2D) direction-of-arrival (DOA) estimation of noncircular sources using an L-shaped sparse array. An L-shaped sparse array consisting of two co-prime arrays is firstly introduced. Then, the fourth-order-cumulants (FOCs) of received data are used to construct a FOC matrix (FOCM), by which we can get the estimations of elevation angles. With the estimated elevation angles, the azimuth angles can be estimated by a low-complexity signal separation algorithm. During the procedure used for estimating azimuth angles, no any eigenvalue decomposition (EVD), peak search and pair-matching procedure need to be implemented. Although the aperture is extended significantly, the computation complexity of proposed algorithm still is acceptable. Compared with some analogous algorithms, our approach shows more attractive estimation performance. A lot of simulation results prove the advantages of proposed DOA estimation technology.

This research proposes an improved narrowband partial adaptive beamformer analysis using a proposed spherical array. Comparison between the fully and the partial adaptive beamformers is given. The study is performed by investigating performance parameters like the beamformer output signal-to-noise ratio and the beamformer output signal-to-interference-plus-noise ratio both in the steady state and along adaptation. Furthermore, computational complexity and convergence speed of the proposed sensor arrangement are also analyzed and examples are given. The results demonstrate that this beamformer considerably reduces the number of complex operations and features faster convergence speed.

Due to the spinning target’s distribution on a continuous scene, it is impossible to guarantee that all scatterers are located exactly on the pre-discretized grid. Off-grid problem will lead to the mismatch of sensing matrix, which severely affect the performance of conventional narrow-band radar imaging based on compressed sensing. By reformatting the signal model and improving the sparse recovery algorithm, a robust narrow-band radar imaging method for off-grid spinning targets is proposed. Firstly, an imaging model, considering the gridding error, is developed, which is more close to the distribution of real target. Secondly, we put forward an improved orthogonal matching pursuit algorithm for the optimization of reconstruction, and introduce the nonlinear least squares method to further improve the reconstruction accuracy of scatterers. Finally, the effectiveness of the proposed method is verified by simulation and real data, and the selection rule of grid size is presented by quantitative analysis.

Fitting a causal dynamic model to an image is a fundamental problem in image processing, pattern recognition, and computer vision. In image restoration, for instance, the goal is to recover an estimate of the true image, preferably in the form of a parametric model, given an image that has been degraded by a combination of blur and additive white Gaussian noise. In texture analysis, on the other hand, a model of a particular texture image can serve as a tool for simulating texture patterns. Finally, in image enhancement one computes a model of the true image and the residuals between the image and the modeled image can be interpreted as the result of applying a de-noising filter. There are numerous other applications within the field of image processing that require a causal dynamic model. Such is the case in scene analysis, machined parts inspection, and biometric analysis, to name only a few. There are many types of causal dynamic models that have been proposed in the literature, among which the autoregressive moving average and state-space models (i.e., Kalman filter) are the most commonly used. In this paper we introduce a 2-D stochastic state-space system identification algorithm for fitting a quarter plane causal dynamic Roesser model to an image. The algorithm constructs a causal, recursive, and separable-in-denominator 2-D Kalman filter model. The algorithm is tested with three real images and the quality of the estimated images are assessed.

One key task in forensic science is to perform criminal investigation through image database retrieval. Of the various images, tire pattern is an important type of image data for crime scene investigation. However, different rotation and direction of tire patterns are often encountered and is insufficient to use the conventional multi-scale texture feature extraction method which is not rotational invariant. To alleviate this problem, the paper proposed two new texture feature extraction methods based on the Radon transform and Curvelet transform. The experiments were conducted using a tire pattern database containing 400 images. The results show that the proposed methods effectively overcome the influences of rotation and significantly improve the retrieval efficiency.

This work addresses the off-grid issue for DOA and frequency estimations when the dictionary based sparse signal recovery concept is adopted. By off-grid, we mean that the true values of signal, angles or frequencies in this case, are not exactly on the sampling grid created by utilizing the discrete dictionary technique. To handle this problem, off-grid is remodelled such that it is represented by an offset matrix that is a sparse matrix. And then, a direct estimate of the offset matrix is developed to compensate the off-grid by utilizing the fact that the offset matrix is a sparse matrix. Finally, by exploring the sparse property of DOAs/frequencies and offset matrix, a joint estimation approach is devised under optimization framework. Numerical studies demonstrate the effectiveness of the proposed approach.