This paper proposes an algorithm that solves planar homography by iterative linear optimization. We iteratively employ direct linear transformation (DLT) algorithm to robustly estimate the homography induced by a give...
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This paper proposes an algorithm that solves planar homography by iterative linear optimization. We iteratively employ direct linear transformation (DLT) algorithm to robustly estimate the homography induced by a given set of point correspondences under perspective transformation. By simple on-the-fly homogeneous coordinate adjustment we progressively minimize the difference between the algebraic error and the geometric error. When the difference is sufficiently close to zero, the geometric error is equivalently minimized and the homography is reliably solved. Backward covariance propagation is employed to do error analysis. The experiments prove that the algorithm is able to find global minimum despite erroneous initialization. It gives very precise estimate at low computational cost and greatly outperforms existing techniques.
This paper explores two types of multistate Hopfield neural networks, based on commutative quaternions that are similar to Hamilton's quaternions but with commutative multiplication. In one type of the networks, t...
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This paper explores two types of multistate Hopfield neural networks, based on commutative quaternions that are similar to Hamilton's quaternions but with commutative multiplication. In one type of the networks, the state of a neuron is represented by two kinds of phases and one real number. The other type of the networks adopts the decomposed form of commutative quaternion, i.e., the state of a neuron consists of a combination of two complex values. We have investigated the stabilities of these networks, i.e., the energies monotonically decreases with respect to the changes of the network states.
Let H be a real Hilbert *** that T is a nonexpansive mapping on H with a fixed point, G is a L-Lipschitzian mapping on H with coefficient L > 0, and F : H → H is a k- Lipschitzian and η-strongly monotone operator...
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Let H be a real Hilbert *** that T is a nonexpansive mapping on H with a fixed point, G is a L-Lipschitzian mapping on H with coefficient L > 0, and F : H → H is a k- Lipschitzian and η-strongly monotone operator with k > 0, η > O. Let 0 2, 0 2/2)/L = τ/L. We pointed out the relationship between Yamada's method and viscosity iteration and proved that the sequence {x η } generated by the iterative method x η+1 = α n γG(x n ) + (I - μα n F)Tx n converges strongly to a fixed point x̃ ∈ F ix (T), which solves the variational inequality ((γG - μF)x̃, x-x̃) ≤ 0, for x ∈ F ix (T).
Vision plays the most important role in human perception, which is limited to only the visual band of the electromagnetic spectrum. Therefore, the need for Radar imaging systems, to recover some sources that are not w...
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Vision plays the most important role in human perception, which is limited to only the visual band of the electromagnetic spectrum. Therefore, the need for Radar imaging systems, to recover some sources that are not within human visual band, is raised. This paper presents a new algorithm for Synthetic Aperture Radar (SAR) images segmentation based on thresholding technique. Generally, segmentation of a SAR image falls into two categories;one based on grey levels and the other based on texture. The present paper deals with SAR images segmentation based on grey levels. We developed a new formula using Minimum Cross Entropy Thresholding (MCET) method for estimating optimal threshold value based on Gamma distribution to analyzing data on images;that means histogram of SAR images is assumed to be a mixture of Gamma distributions. The proposed method is iterative which decreases the number of operation to converge tends to the optimal solution. It is applied on bi-modal and multimodal scenarios. The results obtained are promising.
iterative image reconstruction algorithms have many advantages over analytical image reconstruction algorithms. The OSEM(ordered subsets EM) iterative algorithm has enjoyed considerable interest for computed tomograph...
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iterative image reconstruction algorithms have many advantages over analytical image reconstruction algorithms. The OSEM(ordered subsets EM) iterative algorithm has enjoyed considerable interest for computed tomography due to its acceleration of the ML-EM *** with the conventional OSEM algorithm,the OS method RAMLA(row action ML algorithm) can not only bring about significant acceleration in the iterative reconstruction, but also outperform OSEM in convergence ***,the algorithm requires a judicious choice of a user-specified relaxation parameter,which is sometimes very *** this paper,we present an accelerated ordered subsets reconstruction algorithm for X-ray cone-beam *** algorithm is founded on RAMLA approach, but avoids the problem of user-specified relaxation ***,by increasing the step size of the correction factor,the algorithm achieves a great deal of acceleration in convergence *** advantages of the method are verified by the experiment of the 3D image iterative reconstruction of FORBILD's head phantom.
We introduce a class of mixed nonlinear variational inclusion for fuzzy mappings in Hilbert spaces. By using the resolvent operator technique for maximal monotone mapping, we construct some new iterative algorithms fo...
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We introduce a class of mixed nonlinear variational inclusion for fuzzy mappings in Hilbert spaces. By using the resolvent operator technique for maximal monotone mapping, we construct some new iterative algorithms for solving this class of variational inclusions. We prove the existence of solution for this kind of variational inclusions and the convergence of iterative sequences generalized by the algorithms in Hilbert spaces.
At present, the probability of selecting "the peer next door" as an overlay neighbour in Kademlia is fairly small. Prior research has been concerned with reducing the lookup latency by means of proximity nei...
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At present, the probability of selecting "the peer next door" as an overlay neighbour in Kademlia is fairly small. Prior research has been concerned with reducing the lookup latency by means of proximity neighbour and route selection, but focused on recursive routing algorithms. This work leverages location data about peers and extends Kademlia's iterative routing algorithm to reduce cross-network traffic at the level of the distributed hash table. Evaluation with real-world measurement data gives evidence that locality of traffic tends to reduce lookup latencies as well. In turn, mechanisms that aim at reducing lookup latencies do not necessarily reduce cross-network traffic to the same extent.
A method for the application of the MIMO iterative waterfilling algorithm for broadband channels is presented. The modified game-theoretic problem formulation and its Nash equilibrium are presented, along with conditi...
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A method for the application of the MIMO iterative waterfilling algorithm for broadband channels is presented. The modified game-theoretic problem formulation and its Nash equilibrium are presented, along with conditions for its existence and uniqueness and for the convergence of the distributed algorithm. The method is verified using a simple design example. The performance of the MIMO iterative waterfilling algorithm in the presence of errors in the channel state information (CSI) is also presented.
To find the corresponding target points among multi-images of the same scene is a premise of three dimensional (3D) reconstruction automatically from multi-view images. The geometrical constraints among multi-images o...
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To find the corresponding target points among multi-images of the same scene is a premise of three dimensional (3D) reconstruction automatically from multi-view images. The geometrical constraints among multi-images of the same scene are analyzed and it is concluded that the error matching cannot be removed if only using 2D information. In this paper, we present an iterative fractional step matching algorithm: (1) pre-matching the target point using constraints of epipolar geometry firstly; (2) removing the error matching points using the principle of the uniqueness of a point in 3D space; (3) bundle adjustment is added to refine the camera external parameters. After the external parameters of every image refined, repeat step (1) to step (3) until matching occurred at the same point between the adjacent iteration. Real experiments showed that error matching can be effectively removed using our algorithm, resulting in improved efficiency and accuracy in 3D reconstruction.
In this work, an energy based acoustic source localization task in a wireless sensor network (WSN) is considered. Based on data gathered from field experiments, it is revealed that the acoustic energy gathered at sens...
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In this work, an energy based acoustic source localization task in a wireless sensor network (WSN) is considered. Based on data gathered from field experiments, it is revealed that the acoustic energy gathered at sensor nodes exhibits a heavy-tail, non-Gaussian characteristic and should be fitted into a contaminated Gaussian model. This property renders conventional least square and maximum likelihood based location estimation methods ineffective. Leveraging the distributed, in-network processing nature of a WSN, a novel de-centralized robust acoustic source localization (DRASL) algorithm is proposed. With the DRASL, local sensor nodes receive sensor readings broadcast from neighboring sensors and independently compute local location estimates using a light-weight iterative Nonlinear Reweighted Least Square (INRLS) algorithm. The local location estimate then will be relayed to a fusion center where the final location estimate is obtained as a weighted average of the local estimates. The potential advantage of this algorithm is validated using extensive simulation in a real-world operation scenario. It is show that its performance is superior than existing methods while promising to be more energy efficient.
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