2015

• Record 181 of
  
  Title:Filtering and analysis on the random drift of FOG
  
  Author(s):Tian, Yun-Peng(1,2); Yang, Xiao-Jun(1); Guo, Yun-Zeng(1); Liu, Feng(1)
  Source: Proceedings of SPIE - The International Society for Optical Engineering  Volume: 9679  Issue:   DOI: 10.1117/12.2199345  Published: 2015  
  Abstract:Fiber optic gyro (FOG) is an optical gyroscope which is based on the Sagnac effect and uses the optical fiber coil as light propagation channel. Gyro drift consists of two components: systemic drift and random drift. Systemic drift can be compensated by testing and calibrating. Random drift changes with time, so it becomes an important indicator to measure the precision of gyroscope, which has a great impact on the inertial navigation system. It can't be compensated by the simple method. Random drift is a main error of fiber optic gyro (FOG). The static output of FOG is a random project and it has more random noise when as the inertial navigation sensor, which will affect the measurement accuracy. It is an efficient method to reduce the random drift and improve the accuracy by modeling and compensation from the output of FOG. According to the characteristic of fiber optic gyro, the random drift model is studied. Using the time series method, the constant component of the random noise original data is extracted. After stationarity and normality tests, a normal random process is acquired. Based on this, the model is established using the recursive least squares, and then the model is applied to the normal Kalman and adaptive Kalman, finally the data is process with the filter. After experimental verification, the noise variance was reduced after filtering, and the effect is obvious. © 2015 SPIE.
  Accession Number: 20155201715832
• Record 182 of
  
  Title:Biclustering learning of trading rules
  
  Author(s):Huang, Qinghua(1); Wang, Ting(1); Tao, Dacheng(2); Li, Xuelong(3)
  Source: IEEE Transactions on Cybernetics  Volume: 45  Issue: 10  DOI: 10.1109/TCYB.2014.2370063  Published: October 2015  
  Abstract:Technical analysis with numerous indicators and patterns has been regarded as important evidence for making trading decisions in financial markets. However, it is extremely difficult for investors to find useful trading rules based on numerous technical indicators. This paper innovatively proposes the use of biclustering mining to discover effective technical trading patterns that contain a combination of indicators from historical financial data series. This is the first attempt to use biclustering algorithm on trading data. The mined patterns are regarded as trading rules and can be classified as three trading actions (i.e., the buy, the sell, and no-action signals) with respect to the maximum support. A modifiedK nearest neighborhood (K -NN) method is applied to classification of trading days in the testing period. The proposed method [called biclustering algorithm and theK nearest neighbor (BIC-K-NN)] was implemented on four historical datasets and the average performance was compared with the conventional buy-and-hold strategy and three previously reported intelligent trading systems. Experimental results demonstrate that the proposed trading system outperforms its counterparts and will be useful for investment in various financial markets. © 2013 IEEE.
  Accession Number: 20145000309567
• Record 183 of
  
  Title:A novel visual codebook model based on fuzzy geometry for large-scale image classification
  
  Author(s):Li, Yanshan(1); Huang, Qinghua(2,3); Xie, Weixin(1); Li, Xuelong(4)
  Source: Pattern Recognition  Volume: 48  Issue: 10  DOI: 10.1016/j.patcog.2015.02.010  Published: October 1, 2015  
  Abstract:The codebook model has been developed as an effective means for image classification. However, the inherent operation of assigning visual words to image feature vectors in traditional codebook approaches causes serious ambiguities in image classification. In particular, the nearest word may not be the best fit to a feature, and multiple words may be equally appropriate for one specific feature. To resolve these ambiguities, we propose a novel visual codebook model based on the n-dimensional fuzzy geometry (n-D FG) theory, where all visual words and features are modeled as fuzzy points in the n-D FG space, and appropriate uncertainty is introduced to each fuzzy point to enhance the representation capacity. This n-D FG-codebook model not only inherits advantages from the fuzzy set theory, but also facilitates the analysis and determination of the relationship between visual words and features in geometric form. By explicitly taking into account the ambiguities, we propose a novel measure of similarity between the visual words and fuzzy features. Following the proposed codebook model and the novel similarity measure, we develop two useful image classification algorithms by modifying popular image coding algorithms (i.e. SPM and LLC). Finally, experimental results demonstrate that the classification accuracy of the proposed algorithms is dramatically improved for a standard large-scale image database. For example, with a codebook size of 256, the proposed algorithms achieve similar performance as traditional algorithms with a codebook size of 1024, indicating that the proposed algorithms reduce the computational cost by 75% while achieving almost identical classification accuracy to traditional algorithms. Thus, the proposed algorithms represent a more efficient and appropriate scheme for big image data. © 2015 Elsevier Ltd. All rights reserved.
  Accession Number: 20151200654309
• Record 184 of
  
  Title:Maximum projection and velocity estimation algorithm for small moving target detection in space surveillance
  
  Author(s):Yao, Dalei(1,2,3); Wen, Desheng(2); Xue, Jianru(1); Chen, Zhi(2); Wen, Yan(2); Jiang, Baotan(2); Ma, Junyong(2)
  Source: Proceedings of SPIE - The International Society for Optical Engineering  Volume: 9675  Issue:   DOI: 10.1117/12.2202360  Published: 2015  
  Abstract:The article presents a new method to detect small moving targets in space surveillance. Image sequences are processed to detect and track targets under the assumption that the data samples are spatially registered. Maximum value projection and normalization are performed to reduce the data samples and eliminate the background clutter. Targets are then detected through connected component analysis. The velocities of the targets are estimated by centroid localization and least squares regression. The estimated velocities are utilized to track the targets. A sliding neighborhood operation is performed prior to target detection to significantly reduce the computation while preserving as much target information as possible. Actual data samples are acquired to test the proposed method. Experimental results show that the method can efficiently detect small moving targets and track their traces accurately. The centroid locating precision and tracking accuracy of the method are within a pixel. © 2015 SPIE.
  Accession Number: 20161602266803
• Record 185 of
  
  Title:Study on the technology of mutual alignment based on the fourquadrant photo electric detector
  
  Author(s):Hu, Ya-Bin(1,2); Wang, Miao(1)
  Source: Proceedings of SPIE - The International Society for Optical Engineering  Volume: 9795  Issue:   DOI: 10.1117/12.2207844  Published: 2015  
  Abstract:Panoramic stereo cameras and laser radars have their own coordinate system in the dynamic spatial sensing area and they have to determine the position relationship between each other through joint calibration. As using the traditional technology of mutual alignment based on the telescope cross wire is tedious and requires high operating skills, a new method of mutual alignment using lasers and four-quadrant photo electric detectors is provided after analyzing the working principle of four-quadrant photo electric detectors. Firstly make the laser beam irradiate the active area of the four-quadrant photo electric detector through coarse aiming. Then the center of a light spot offset relative to the center of the active area can be obtained according to the output voltage of four quadrants. The pose of two instruments can be adjusted properly to realize mutual alignment. The experimental results indicate that the alignment accuracy of four-quadrant detectors can meet the requirements of mutual alignment, which provides a new idea for joint calibration. © 2015 SPIE.
  Accession Number: 20161702279089
• Record 186 of
  
  Title:Carbon-fiber-reinforced polymer variable-curvature mirror used for optical zoom imaging: Prototype design and experimental demonstration
  
  Author(s):Zhao, Hui(1); Fan, Xuewu(1); Pang, Zhihai(1); Ren, Guorui(1); Wang, Wei(1); Xie, Yongjie(1); Ma, Zhen(1); Du, Yunfei(1); Su, Yu(1); Wei, Jingxuan(2)
  Source: Optical Engineering  Volume: 54  Issue: 2  DOI: 10.1117/1.OE.54.2.025103  Published: February 1, 2015  
  Abstract:In recent years, optical zoom imaging without moving elements has received much attention. The key to realizing this technique lies in the design of the variable-curvature mirror (VCM). To obtain enough optical magnification, the VCM should be able to change its radius of curvature over a wide range. In other words, the VCM must be able to provide a large sagittal variation, which requires the mirror material to be robust during curvature variation, require little force to deform, and have high ultimate strength. Carbon-fiber-reinforced polymer (CFRP) satisfies all these requirements and is suitable for fabricating such a VCM. Therefore, in this research, a CFRP prototype VCM has been designed, fabricated, and tested. With a diameter of 100 mm, a thickness of 2 mm, and an initial radius of curvature of 1740 mm, this VCM can provide a maximum 23-m sagittal variation and a minimum and maximum radius of curvature of 1705 and 1760 mm. © Society of Photo-Optical Instrumentation Engineers. © 2015 Society of Photo-Optical Instrumentation Engineers.
  Accession Number: 20150700522654
• Record 187 of
  
  Title:Semi-supervised multitask learning for scene recognition
  
  Author(s):Lu, Xiaoqiang(1); Li, Xuelong(1); Mou, Lichao(1)
  Source: IEEE Transactions on Cybernetics  Volume: 45  Issue: 9  DOI: 10.1109/TCYB.2014.2362959  Published: September 1, 2015  
  Abstract:Scene recognition has been widely studied to understand visual information from the level of objects and their relationships. Toward scene recognition, many methods have been proposed. They, however, encounter difficulty to improve the accuracy, mainly due to two limitations: 1) lack of analysis of intrinsic relationships across different scales, say, the initial input and its down-sampled versions and 2) existence of redundant features. This paper develops a semi-supervised learning mechanism to reduce the above two limitations. To address the first limitation, we propose a multitask model to integrate scene images of different resolutions. For the second limitation, we build a model of sparse feature selection-based manifold regularization (SFSMR) to select the optimal information and preserve the underlying manifold structure of data. SFSMR coordinates the advantages of sparse feature selection and manifold regulation. Finally, we link the multitask model and SFSMR, and propose the semi-supervised learning method to reduce the two limitations. Experimental results report the improvements of the accuracy in scene recognition. © 2013 IEEE.
  Accession Number: 20153401206377
• Record 188 of
  
  Title:MTF online compensation in space optical remote sensing camera
  
  Author(s):Qu, Youshan(1); Zhai, Bo(1); Han, Yameng(1); Zhou, Jiang(1,2)
  Source: Proceedings of SPIE - The International Society for Optical Engineering  Volume: 9449  Issue:   DOI: 10.1117/12.2076000  Published: 2015  
  Abstract:An ordinary space optical remote sensing camera is an optical diffraction-limited system and a low-pass filter from the theory of Fourier Optics, and all the digital imaging sensors, whether the CCD or CMOS, are low-pass filters as well. Therefore, when the optical image with abundant high-frequency components passes through an optical imaging system, the profuse middle-frequency information is attenuated and the rich high-frequency information is lost, which will blur the remote sensing image. In order to overcome this shortcoming of the space optical remote sensing camera, an online compensating approach of the Modulation Transfer Function in the space cameras is designed. The designed method was realized by a hardware analog circuit placed before the A/D converter, which was composed of adjustable low-pass filters with a calculated value of quality factor Q. Through the adjustment of the quality factor Q of the filters, the MTF of the processed image is compensated. The experiment results display that the realized compensating circuit in a space optical camera is capable of improving the MTF of an optical remote sensing imaging system 30% higher than that of no compensation. This quantized principle can efficiently instruct the MTF compensating circuit design in practice. © 2015 SPIE.
  Accession Number: 20151100635528
• Record 189 of
  
  Title:Camera calibration method of binocular stereo vision based on OpenCV
  
  Author(s):Zhong, Wanzhen(1,2); Dong, Xiaona(1)
  Source: Proceedings of SPIE - The International Society for Optical Engineering  Volume: 9675  Issue:   DOI: 10.1117/12.2202186  Published: 2015  
  Abstract:Camera calibration, an important part of the binocular stereo vision research, is the essential foundation of 3D reconstruction of the spatial object. In this paper, the camera calibration method based on OpenCV (open source computer vision library) is submitted to make the process better as a result of obtaining higher precision and efficiency. First, the camera model in OpenCV and an algorithm of camera calibration are presented, especially considering the influence of camera lens radial distortion and decentering distortion. Then, camera calibration procedure is designed to compute those parameters of camera and calculate calibration errors. High-accurate profile extraction algorithm and a checkboard with 48 corners have also been used in this part. Finally, results of calibration program are presented, demonstrating the high efficiency and accuracy of the proposed approach. The results can reach the requirement of robot binocular stereo vision. © 2015 SPIE.
  Accession Number: 20161602266797
• Record 190 of
  
  Title:Combined analysis of tunable phase mask within spatial and frequency domain
  
  Author(s):Zhou, Liang(1,2); Liu, Zhao-Hui(1); She, Wen-Ji(1)
  Source: Wuli Xuebao/Acta Physica Sinica  Volume: 64  Issue: 22  DOI: 10.7498/aps.64.224207  Published: November 20, 2015  
  Abstract:Wavefront coding technique is a powerful technique which overcomes the defects of traditional way to extend depth of field. By inserting a phase mask into the traditional incoherent imaging system, wavefront coding technique does not reduce the resolution and the light gathering power of the optical system but enlarges the depth of field of incoherent imaging system. Although several kinds of phase masks have been reported, cubic phase mask is still of a classical type which has been investigated widely both in spatial and frequency domain. Since the phase profiles of phase masks adopted in classical wavefront coding systems are predefined with specific optical systems, the extension of depth of field is not tunable. Tunable wavefront coding systems are introduced by using a pair of detachable phase masks, which is possible to control the depth of field and bandwidth of system by changing the position of each component with respect to the pupil center. Ojeda-Castañeda [Ojeda-Castañeda J, Rodríguez M, Naranjo R 2010 Proceedings of Progress in Electronics Research Symposium, Cambridge, July 5-8, 2010 p531] proposed to use a pair of cosine phase masks to make defocus sensitivity tunable. Zhao [Zhao H, Wei J X 2014 Opt. Commun. 326 35] investigated an improved version of Ojeda- Castaneda's design in frequency domain and found that the proposed system realized tunable bandwidth. The present study, based on the work of Zhao, analyzes the tunable characteristics of a pair of simple modified detachable cubic phase masks in spatial domain and frequency domain. Firstly, the ray aberration theory is adopted to give mathematical analyses and ray aberration maps of the proposed tunable phase mask. Based on the mathematical derivations, the size of point spread function (PSF) of system can be changed not only by profile of each cubic mask but also by the each mask displacement relative to pupil center. Secondly, a mathematical PSF based on the stationary phase method is derived in spatial domain. Simulations indicate that the positions of PSF translate in the image plane with the displacements of phase mask profile and the position of each component with respect to the pupil center. By analyzing the oscillations of PSF, the effective bandwidth is obtained. Through the expression, we can conclude that the effective bandwidth can be changed by the position, mask profile of each component and defocus. Only when the addition of two mask profiles is large enough, can the effective bandwidth be simplified without adding the influence of defocus. In addition, though the approximate expression of magnitude transfer of function (MTF) has been given by adopting stationary phase method in the appendix of previous work, it cannot give an intuitive grasp of the effective bandwidth in MTF map. Unlike the MTF expression derived before, the exact optical transfer function (OTF) expression is derived by adopting Fresnel integral in frequency domain. Exact MTF and phase transfer function (PTF) can be derived from OTF. Based on the exact MTF expression, simulations give an intuitive effective bandwidth in MTF map. Simulations also show the nonlinear property of PTF. The effective bandwidth and MTF can be changed by different phase mask profiles and positions, which indicate that the effective bandwidth and defocus sensitivity can be tuned. Analyses are conducted both in spatial domain and in frequency domain to verify the tunable property of the proposed phase mask, which provides theoretical foundation for tunable wavefront coding system design. © 2015 Chinese Physical Society.
  Accession Number: 20155001667359
• Record 191 of
  
  Title:Detection ability analysis of ground based imaging polarization detector
  
  Author(s):Yao, Dalei(1,2,3); Wen, Desheng(1); Xue, Jianru(3); Qiu, Yuehong(1); Xi, Jiangbo(1,2); Wen, Yan(1); Chen, Zhi(1)
  Source: Optik  Volume: 126  Issue: 23  DOI: 10.1016/j.ijleo.2015.08.188  Published: 2015  
  Abstract:A ground based imaging polarization detector for astronomical observation is designed, the working principle is analyzed and the mathematical models of degree of polarization and detection ability of the detector are derived. The SNR (signal-to-noise ratio) and number of phoelectrons needed by the detector are analyzed by simulation with the detection demand of 1% or 2% degree of polarization. Then, the integration time of the detector is calculated when detecting stars with different brightness according to the parameters of the detector we designed. The theoretical basis is given through these results. © 2015 Elsevier GmbH. All rights reserved.
  Accession Number: 20160101753540
• Record 192 of
  
  Title:Spectral-Spatial Kernel Regularized for Hyperspectral Image Denoising
  
  Author(s):Yuan, Yuan(1); Zheng, Xiangtao(1); Lu, Xiaoqiang(1)
  Source: IEEE Transactions on Geoscience and Remote Sensing  Volume: 53  Issue: 7  DOI: 10.1109/TGRS.2014.2385082  Published: July 2015  
  Abstract:Noise contamination is a ubiquitous problem in hyperspectral images (HSIs), which is a challenging and promising theme in many remote sensing applications. A large number of methods have been proposed to remove noise. Unfortunately, most denoising methods fail to take full advantages of the high spectral correlation and to simultaneously consider the specific noise distributions in HSIs. Recently, a spectral-spatial adaptive hyperspectral total variation (SSAHTV) was proposed and obtained promising results. However, the SSAHTV model is insensitive to the image details, which makes the edges blur. To overcome all of these drawbacks, a spectral-spatial kernel method for HSI denoising is proposed in this paper. The proposed method is inspired by the observation that the spectral-spatial information is highly redundant in HSIs, which is sufficient to estimate the clear images. In this paper, a spectral-spatial kernel regularization is proposed to maintain the spectral correlations in spectral dimension and to match the original structure between two spatial dimensions. Moreover, an adaptive mechanism is developed to balance the fidelity term according to different noise distributions in each band. Therefore, it cannot only suppress noise in the high-noise band but also preserve information in the low-noise band. The reliability of the proposed method in removing noise is experimentally proved on both simulated data and real data. © 2015 IEEE.
  Accession Number: 20151200657787