scholarly journals NCC Based Correspondence Problem for First- and Second-Order Graph Matching

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5117
Author(s):  
Beibei Cui ◽  
Jean-Charles Créput
Keyword(s):  
Input Data ◽  
Cross Correlation ◽  
Graph Matching ◽  
Second Order ◽  
Consensus Algorithm ◽  
Interaction Method ◽  
Matching Algorithm ◽  
Scale Interaction ◽  
First Order ◽  
Normalized Cross Correlation

Automatically finding correspondences between object features in images is of main interest for several applications, as object detection and tracking, identification, registration, and many derived tasks. In this paper, we address feature correspondence within the general framework of graph matching optimization and with the principal aim to contribute. We proposed two optimized algorithms: first-order and second-order for graph matching. On the one hand, a first-order normalized cross-correlation (NCC) based graph matching algorithm using entropy and response through Marr wavelets within the scale-interaction method is proposed. First, we proposed a new automatic feature detection processing by using Marr wavelets within the scale-interaction method. Second, feature extraction is executed under the mesh division strategy and entropy algorithm, accompanied by the assessment of the distribution criterion. Image matching is achieved by the nearest neighbor search with normalized cross-correlation similarity measurement to perform coarse matching on feature points set. As to the matching points filtering part, the Random Sample Consensus Algorithm (RANSAC) removes outliers correspondences. One the other hand, a second-order NCC based graph matching algorithm is presented. This algorithm is an integer quadratic programming (IQP) graph matching problem, which is implemented in Matlab. It allows developing and comparing many algorithms based on a common evaluation platform, sharing input data, and a customizable affinity matrix and matching list of candidate solution pairs as input data. Experimental results demonstrate the improvements of these algorithms concerning matching recall and accuracy compared with other algorithms.

Second-Order Consensus Filtering Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 1621-1624
Author(s):  
Huan Xin Peng ◽  
Wen Kai Wang
Keyword(s):  
Second Order ◽  
Consensus Algorithm ◽  
Distributed Consensus ◽  
Filtering Algorithm ◽  
First Order

In order to improve the accuracy of consensus filters, in this paper, we propose a second-order consensus filtering algorithm based on the pseudo two-hop distributed consensus algorithm, we analyze the performance of the second-order consensus filtering algorithm, and prove that the second-order consensus filtering algorithm is convergent. We analyze the filtering accuracy of the second-order consensus filtering algorithm, and make a comparison for filtering accuracy between the first-order consensus filtering algorithm and the second-order consensus filtering algorithm, simultaneously, simulation is proposed, the results show the second-order consensus filtering algorithm is convergent, and the filtering accuracy is higher than that of the first-order consensus algorithm.

scholarly journals Parsimonious truncated Newton method for time-domain full waveform inversion based on Fourier-domain full-scattered-field approximation

Geophysics ◽  
2021 ◽  
pp. 1-147
Author(s):  
Peng Yong ◽  
Romain Brossier ◽  
Ludovic Métivier
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Cross Correlation ◽  
Waveform Inversion ◽  
Field Approximation ◽  
Second Order ◽  
Vector Product ◽  
First Order ◽  
Full Waveform ◽  
Truncated Newton

In order to exploit Hessian information in Full Waveform Inversion (FWI), the matrix-free truncated Newton method can be used. In such a method, Hessian-vector product computation is one of the major concerns due to the huge memory requirements and demanding computational cost. Using the adjoint-state method, the Hessian-vector product can be estimated by zero-lag cross-correlation of the first-order/second-order incident wavefields and the second-order/first-order adjoint wavefields. Different from the implementation in frequency-domain FWI, Hessian-vector product construction in the time domain becomes much more challenging as it is not affordable to store the entire time-dependent wavefields. The widely used wavefield recomputation strategy leads to computationally intensive tasks. We present an efficient alternative approach to computing the Hessian-vector product for time-domain FWI. In our method, discrete Fourier transform is applied to extract frequency-domain components of involved wavefields, which are used to compute wavefield cross-correlation in the frequency domain. This makes it possible to avoid reconstructing the first-order and second-order incident wavefields. In addition, a full-scattered-field approximation is proposed to efficiently simplify the second-order incident and adjoint wavefields computation, which enables us to refrain from repeatedly solving the first-order incident and adjoint equations for the second-order incident and adjoint wavefields (re)computation. With the proposed method, the computational time can be reduced by 70% and 80% in viscous media for Gauss-Newton and full-Newton Hessian-vector product construction, respectively. The effectiveness of our method is also verified in the frame of a 2D multi-parameter inversion, in which the proposed method almost reaches the same iterative convergence of the conventional time-domain implementation.

scholarly journals The Algorithm and Structure for Digital Normalized Cross-Correlation by Using First-Order Moment

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1353 ◽  
Author(s):  
Chao Pan ◽  
Zhicheng Lv ◽  
Xia Hua ◽  
Hongyan Li
Keyword(s):  
Systolic Array ◽  
Fast Algorithm ◽  
Cross Correlation ◽  
Inner Product ◽  
Order Moment ◽  
Mathematical Tool ◽  
Arbitrary Length ◽  
First Order ◽  
Normalized Cross Correlation ◽  
Systolic Structure

Normalized cross-correlation is an important mathematical tool in digital signal processing. This paper presents a new algorithm and its systolic structure for digital normalized cross-correlation, based on the statistical characteristic of inner-product. We first introduce a relationship between the inner-product in cross-correlation and a first-order moment. Then digital normalized cross-correlation is transformed into a new calculation formula that mainly includes a first-order moment. Finally, by using a fast algorithm for first-order moment, we can compute the first-order moment in this new formula rapidly, and thus develop a fast algorithm for normalized cross-correlation, which contributes to that arbitrary-length digital normalized cross-correlation being performed by a simple procedure and less multiplications. Furthermore, as the algorithm for the first-order moment can be implemented by systolic structure, we design a systolic array for normalized cross-correlation with a seldom multiplier, in order for its fast hardware implementation. The proposed algorithm and systolic array are also improved for reducing their addition complexity. The comparisons with some algorithms and structures have shown the performance of the proposed method.

Second-Order Flocking Control with Communication Noise

2014 ◽  
Vol 511-512 ◽  
pp. 1077-1080
Author(s):  
Huan Xin Peng ◽  
Bin Liu ◽  
Wen Kai Wang
Keyword(s):  
Second Order ◽  
Consensus Algorithm ◽  
Distributed Consensus ◽  
First Order ◽  
Convergence Performance ◽  
The Impact ◽  
Better Than

In the paper, we analyze the distributed flocking algorithms with communication noise. Under the Boid model, flocking algorithm with communication noise is easy to diverge. In order to improve the convergence performance of flocking algorithms with communication noise, and overcome the impact brought by communication noise on flocking algorithm, in the paper, a distributed flocking algorithm based on SO-DCT distributed consensus algorithm is proposed. The second-order flocking algorithm under the Boid model is analyzed, and simulations are done. Results show that the second-order distributed flocking algorithm can reach cohesion, and its convergence performance is better than that of the first-order distributed flocking algorithm, moreover, the impact of communication noise on the second-order flocking algorithm is smaller.

Research of Dynamic Target Tracking Algorithm

2013 ◽  
Vol 448-453 ◽  
pp. 3601-3604
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Jie Li
Keyword(s):  
Target Tracking ◽  
Cross Correlation ◽  
Search Algorithm ◽  
Automatic Recognition ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Matching Algorithm ◽  
Normalized Cross Correlation ◽  
Dynamic Target ◽  
Correlation Matching

An improved correlation matching algorithm is proposed in order to overcome some shortcomings of detecting the position of object accurately. A tracking algorithm with normalized cross correlation is introduced. In order to enhance the match speed, we have adopted pyramid search algorithm. The experimental results show that the algorithm has characteristics including automatic recognition of the object; permitting tracking and prediction when the object become shaded; the algorithm makes adaptive decision of varied object during the process of tracking.

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