• Title, Summary, Keyword: reconstruction error estimation

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High accuracy online 3D-reconstruction by multiple cameras

  • Oota, Yoshikazu;Pan, Yaodong;Furuta, Katuhisa
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1749-1752
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    • 2005
  • For online high accurate reconstruction of an object from an visual information, a linear reconstruction method for multiple images is popular. Basically this method needs many cameras or many different screen shots from different view points. This method, however, has the benefit of less calculation and is adequate for a real time application by comparing other popular method. In this paper, online reconstruction system using more than three cameras is treated. An evaluation method of cameras' position, and of the number is derived for the linear reconstruction method. To decrease errors that are caused from skew of lens, positional error between corresponding points is taken into consideration on the evaluation. The proposed evaluation method enables estimation of the adequate number of cameras and then of feasible view locations. Additionally, repeating search of epipolar lines enables estimation of the hidden point. Comparing with result of an average error analysis, it was confirmed that the proposed methods works effectively.

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Design of Lazy Classifier based on Fuzzy k-Nearest Neighbors and Reconstruction Error (퍼지 k-Nearest Neighbors 와 Reconstruction Error 기반 Lazy Classifier 설계)

  • Roh, Seok-Beom;Ahn, Tae-Chon
    • Journal of Korean Institute of Intelligent Systems
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    • v.20 no.1
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    • pp.101-108
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    • 2010
  • In this paper, we proposed a new lazy classifier with fuzzy k-nearest neighbors approach and feature selection which is based on reconstruction error. Reconstruction error is the performance index for locally linear reconstruction. When a new query point is given, fuzzy k-nearest neighbors approach defines the local area where the local classifier is available and assigns the weighting values to the data patterns which are involved within the local area. After defining the local area and assigning the weighting value, the feature selection is carried out to reduce the dimension of the feature space. When some features are selected in terms of the reconstruction error, the local classifier which is a sort of polynomial is developed using weighted least square estimation. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods such as standard neural networks, support vector machine, linear discriminant analysis, and C4.5 trees.

Bundle Adjustment and 3D Reconstruction Method for Underwater Sonar Image (수중 영상 소나의 번들 조정과 3차원 복원을 위한 운동 추정의 모호성에 관한 연구)

  • Shin, Young-Sik;Lee, Yeong-jun;Cho, Hyun-Taek;Kim, Ayoung
    • The Journal of Korea Robotics Society
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    • v.11 no.2
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    • pp.51-59
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    • 2016
  • In this paper we present (1) analysis of imaging sonar measurement for two-view relative pose estimation of an autonomous vehicle and (2) bundle adjustment and 3D reconstruction method using imaging sonar. Sonar has been a popular sensor for underwater application due to its robustness to water turbidity and visibility in water medium. While vision based motion estimation has been applied to many ground vehicles for motion estimation and 3D reconstruction, imaging sonar addresses challenges in relative sensor frame motion. We focus on the fact that the sonar measurement inherently poses ambiguity in its measurement. This paper illustrates the source of the ambiguity in sonar measurements and summarizes assumptions for sonar based robot navigation. For validation, we synthetically generated underwater seafloor with varying complexity to analyze the error in the motion estimation.

Study of Spectral Reflectance Reconstruction Based on an Algorithm for Improved Orthogonal Matching Pursuit

  • Leihong, Zhang;Dong, Liang;Dawei, Zhang;Xiumin, Gao;Xiuhua, Ma
    • Journal of the Optical Society of Korea
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    • v.20 no.4
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    • pp.515-523
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    • 2016
  • Spectral reflectance is sparse in space, and while the traditional spectral-reconstruction algorithm does not make full use of this characteristic sparseness, the compressive sensing algorithm can make full use of it. In this paper, on the basis of analyzing compressive sensing based on the orthogonal matching pursuit algorithm, a new algorithm based on the Dice matching criterion is proposed. The Dice similarity coefficient is introduced, to calculate the correlation coefficient of the atoms and the residual error, and is used to select the atoms from a library. The accuracy of Spectral reconstruction based on the pseudo-inverse method, Wiener estimation method, OMP algorithm, and DOMP algorithm is compared by simulation on the MATLAB platform and experimental testing. The result is that spectral-reconstruction accuracy based on the DOMP algorithm is higher than for the other three methods. The root-mean-square error and color difference decreases with an increasing number of principal components. The reconstruction error decreases as the number of iterations increases. Spectral reconstruction based on the DOMP algorithm can improve the accuracy of color-information replication effectively, and high-accuracy color-information reproduction can be realized.

Range image reconstruction based on multiresolution surface parameter estimation (다해상도 면 파라미터 추정을 이용한 거리영상 복원)

  • 장인수;박래홍
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.34S no.6
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    • pp.58-66
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    • 1997
  • This paper proposes a multiresolution surface parameter estimation method for range images. Based on robust estimation of surface parameters, it approximates a patch to a planar surface in the locally adaptive window. Selection of resolution is made pixelwise by comparing a locally computed homogeneity measure with th eglobal threshold determined by te distribution of the approximation error. The proposed multiresolution surface parameter estimation method is applied to range image reconstruction. Computer simulation results with noisy rnag eimages contaminated by additive gaussian noise and impulse noise show that the proposed multiresolution reconstruction method well preserves step and roof edges compared with the conventional methods. Also the segmentation method based on the estimated surface parameters is shown to be robust to noise.

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Performance Analysis of Compressed Sensing Given Insufficient Random Measurements

  • Rateb, Ahmad M.;Syed-Yusof, Sharifah Kamilah
    • ETRI Journal
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    • v.35 no.2
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    • pp.200-206
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    • 2013
  • Most of the literature on compressed sensing has not paid enough attention to scenarios in which the number of acquired measurements is insufficient to satisfy minimal exact reconstruction requirements. In practice, encountering such scenarios is highly likely, either intentionally or unintentionally, that is, due to high sensing cost or to the lack of knowledge of signal properties. We analyze signal reconstruction performance in this setting. The main result is an expression of the reconstruction error as a function of the number of acquired measurements.

An Improved Reconstruction Algorithm of Convolutional Codes Based on Channel Error Rate Estimation (채널 오류율 추정에 기반을 둔 길쌈부호의 개선된 재구성 알고리즘)

  • Seong, Jinwoo;Chung, Habong
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.42 no.5
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    • pp.951-958
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    • 2017
  • In an attack context, the adversary wants to retrieve the message from the intercepted noisy bit stream without any prior knowledge of the channel codes used. The process of finding out the code parameters such as code length, dimension, and generator, for this purpose, is called the blind recognition of channel codes or the reconstruction of channel codes. In this paper, we suggest an improved algorithm of the blind recovery of rate k/n convolutional encoders in a noisy environment. The suggested algorithm improves the existing algorithm by Marazin, et. al. by evaluating the threshold value through the estimation of the channel error probability of the BSC. By applying the soft decision method by Shaojing, et. al., we considerably enhance the success rate of the channel reconstruction.

The estimation of first order derivative phase error using iterative algorithm in SAR imaging system (SAR(Synthetic Aperture Radar)Imaging 시스템에서 제안 알고리즘의 반복수행을 통한 위상오차의 기울기 추정기법 연구)

  • 김형주;최정희
    • Proceedings of the IEEK Conference
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    • pp.505-508
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    • 2000
  • The success of target reconstruction in SAR(Synthetic Aperture Radar) imaging system is greatly dependent on the coherent detection. Primary causes of incoherent detection are uncompensated target or sensor motion, random turbulence in propagation media, wrong path in radar platform, and etc. And these appear as multiplicative phase error to the echoed signal, which consequently, causes fatal degradations such as fading or dislocation of target image. In this paper, we present iterative phase error estimation scheme which uses echoed data in all temporal frequencies. We started with analyzing wave equation for one point target and extend to overall echoed data from the target scene - The two wave equations governing the SAR signal at two temporal frequencies of the radar signal are combined to derive a method to reconstruct the complex phase error function. Eventually, this operation attains phase error correction algorithm from the total received SAR signal. We verify the success of the proposed algorithm by applying it to the simulated spotlight-mode SAR data.

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Enhanced Reconstruction of Heavy Occluded Objects Using Estimation of Variance in Volumetric Integral Imaging (VII) (Volumetric 집적영상에서 분산 추정을 이용한 심하게 은폐된 물체의 향상된 복원)

  • Hwang, Yong-Seok;Kim, Eun-Soo
    • Korean Journal of Optics and Photonics
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    • v.19 no.6
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    • pp.389-393
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    • 2008
  • Enhanced reconstruction of heavy occluded objects was represented using estimation of variance in computational integral imaging. The system is analyzed to extract information of enhanced reconstruction from an elemental images set. To obtain elemental images with enhanced resolution, low focus error, and large depth of focus, synthetic aperture integral imaging (SAII) utilizing a digital camera has been adopted. The focused areas of the reconstructed image are varied with the distance of the reconstruction plane. When an occluded object is occluded heavily, an occluded object can not be reconstructed by removing the occluding object. To obtain reconstruction of the occluded object by remedying the effect of heavy occlusion, the statistical technique has been adopted.

Sensorless Speed Control System Using a Neural Network

  • Huh Sung-Hoe;Lee Kyo-Beum;Kim Dong-Won;Choy Ick;Park Gwi-Tae
    • International Journal of Control, Automation, and Systems
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    • v.3 no.4
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    • pp.612-619
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    • 2005
  • A robust adaptive speed sensorless induction motor direct torque control (DTC) using a neural network (NN) is presented in this paper. The inherent lumped uncertainties of the induction motor DTC system such as parametric uncertainty, external load disturbance and unmodeled dynamics are approximated by the NN. An additional robust control term is introduced to compensate for the reconstruction error. A control law and adaptive laws for the weights in the NN, as well as the bounding constant of the lumped uncertainties are established so that the whole closed-loop system is stable in the sense of Lyapunov. The effect of the speed estimation error is analyzed, and the stability proof of the control system is also proved. Experimental results as well as computer simulations are presented to show the validity and efficiency of the proposed system.