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As we know, the problem of image matching is difficult and important in the field of computer vision. In this paper we present a novel matching algorithm based on local invariant feature description. Firstly, feature points are detected by difference of Gaussian. Secondly, the Haar-wavelet responses within a feature point neighborhood are projected into four directions, and then a 64-dimensional vector...
In this paper, a novel hash-based image content authentication scheme for tamper detection and localization is proposed. The original image is divided into sub-blocks whose size can be tuned with the accuracy of tamper localization, and the measurements which are obtained in the compressed sensing (CS) process contain all the information of the subblocks. Moreover, singular value decomposition (SVD)...
Traditional adaptive beamformers have robustness just for specific error condition. They suffer performance degradation in the presence of multiple errors such as sample covariance matrix estimation error and steering vector mismatch. In this article, a new robust adaptive beamforming algorithm based on jointly estimating the covariance matrix and steering vector mismatch is proposed to overcome both...
In this work, we put forward a new adaptation criterion, namely the hybrid criterion (HC), which is a mixture of the traditional mean square error (MSE) and the maximum correntropy criterion (MCC). The HC criterion is developed from the viewpoint of the least trimmed squares (LTS) estimator, a high breakdown estimator that can avoid undue influence from outliers. In the LTS estimator, the data are...
This paper concerns with the fault detection problem for a class of networked control systems with delays and data losses. Considering the stochastic characteristic of the delays and data losses, discrete-time Markov jump linear system models with partially known transition probabilities are established. Based on the obtained models, fault detection filters are designed, and the addressed fault detection...
Nowadays images have been easily reproduced and distributed globally very fast. For the copyright protection, the copy detection is an important issue. Any modification on image copying processes can be viewed as attacks. The attacks include adding some small information as well as modifying images. In addition, the content-based copy detection techniques are relied on the image properties which could...
Recently, neural minor component analysis (MCA) has attracted much attention. It has features of high calculation speed and strong fault tolerant property. In this paper, neural MCA network is applied in adaptive beam forming. In order to get over the disadvantages that MCA based beam former cannot form null steering beams at the direction of interference and avoid stagnation behavior of algorithm...
Adversarial learning is the study of machine learning techniques deployed in non-benign environments. Example applications include classifications for detecting spam email, network intrusion detection and credit card scoring. In fact as the gamut of application domains of machine learning grows, the possibility and opportunity for adversarial behavior will only increase. Till now, the standard assumption...
Image processing is one of the important areas of research, which provides efficient solutions to many real and industrial problems. Texture analysis is the most important field in image processing because all objects are textured in real world. In this work, we propose a new texture segmentation method based on the dynamic segmentation architecture. This architecture decomposes the image into blocks...
The plausibility and robustness of an inferential control system entirely depend on the prediction accuracy of the estimator used as the feedback element. This paper is based on a previously proposed Gaussian process inferential controller that employs Gaussian process soft sensor as an estimator. The paper enhances the robustness and the reliability of the control system, particularly, during sensor...
Effective recognition of objects calls for the appropriate selection of feature descriptor. In this paper, we generalize the "extended local ternary patterns" (ELTP) to form a novel and compact set of features named center-symmetric extended local ternary patterns (CS-ELTP). The newly defined CS-ELTP follows a simplified encoding procedure and has a lower dimension for a fixed neighborhood...
In response to the urgent need for learning tools tuned to big data analytics, the present paper introduces a feature selection approach to efficient clustering of high-dimensional vectors. The resultant method leverages random sampling and consensus (RANSAC) arguments, originally developed for robust regression tasks in computer vision, to yield novel dimensionality reduction schemes. The advocated...
In wireless dynamic spectrum access (DSA) networks, orthogonal frequency-division multiplexing (OFDM) is an attractive modulation technique to enable coexistence of primary (legacy) and secondary users. In these systems, the use of cognitive radio (CR) promises to achieve high levels of efficiency in spectrum management. Focused on the operation of a secondary transmitter, emphasis is placed on the...
We study two approaches to distributed compressed sensing for in-network data compression and signal reconstruction at a sink. Communication to the sink is considered to be bandwidth-constrained due to the large number of devices. By using distributed compressed sensing for compression of the data in the network, the communication cost (bandwidth usage) to the sink can be decreased at the expense...
The minimum variance distortionless response (MV-DR) beamformer is very sensitive to the steering vector mismatch. Such mismatch can lead to serious degradation of the beamforming performance especially at high signal-to-noise ratio (SNR). In this paper, a new robust beamformer based on the DOA matrix is proposed to solve the steering vector mismatch. Through the left eigendecomposition of the DOA...
This paper proposes a robust minutiae based fingerprint image hashing technique. The idea is to incorporate the orientation and descriptor in the minutiae of fingerprint images using SIFT-Harris feature points. A recent shape context based perceptual hashing method has been compared against the proposed technique. Experimentally, the proposed technique has been shown to deliver better robustness against...
In this paper we propose a robust sparse based visual tracking method by exploiting local representations in a particle filter framework. We construct a Multi-level Local Dictionary which consists of positive templates and negative templates for discriminative model, Which divide the positive and negative dictionary into two levels called static templates and dynamic templates, respectively, thus...
This paper proposes a framework based on harmonic mean normalized Laplace-Beltrami spectral descriptor for non-rigid 3D shape retrieval. A series of experiments show harmonic mean normalization is suited to classification of stretched shapes, and is robust to isometric transformation, holes, local scaling, noise, shot noise and sampling. To better distinguish among shapes with fine or rough details,...
In this paper we extend our previous work on strategies for automatically constructing noise resilient SVM detectors from click through data for large scale concept-based image retrieval. First, search log data is used in conjunction with Information Retrieval (IR) models to score images with respect to each concept. The IR models evaluated in this work include Vector Space Models (VSM), BM25 and...
In this paper, we consider the Robust Nurse Assignment Problem. This consists in finding the maximum number of absences of qualified nurses still permitting an optimal treatment of patients, leading us to the notion of critical jobs. We introduce the Bipartite Complete Matching Vertex Interdiction Problem as the graph formulation of this problem. We show that it can be solved in polynomial time thanks...
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