2017
2017
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Record 145 of
Title:Real-time image haze removal using an aperture-division polarimetric camera
Author(s):Zhang, Wenfei(1,2,3); Liang, Jian(1,3); Ren, Liyong(1); Ju, Haijuan(1,3); Qu, Enshi(1); Bai, Zhaofeng(1); Tang, Yao(4); Wu, Zhaoxin(2)Source: Applied Optics Volume: 56 Issue: 4 DOI: 10.1364/AO.56.000942 Published: February 1, 2017Abstract:Polarimetric dehazing methods have been proven to be effective in enhancing the quality of images acquired in turbid media. We report a new full-Stokes polarimetric camera, which is based on the division of aperture structure. We design a kind of automatic polarimetric dehazing algorithm and load it into the field programmable gate array (FPGA) modules of our designed polarimetric camera, achieving a real-time image haze removal with an output rate of 25 fps. We demonstrate that the image quality can be significantly improved together with a good color restoration. This technique might be attractive in a range of real-time outdoor imaging applications, such as navigation, monitoring, and remote sensing. ? 2017 Optical Society of America.Accession Number: 20170603336337 -
Record 146 of
Title:Regularized class-specific subspace classifier
Author(s):Zhang, Rui(1); Nie, Feiping(1); Li, Xuelong(2)Source: IEEE Transactions on Neural Networks and Learning Systems Volume: 28 Issue: 11 DOI: 10.1109/TNNLS.2016.2598744 Published: November 2017Abstract:In this paper, we mainly focus on how to achieve the translated subspace representation for each class, which could simultaneously indicate the distribution of the associated class and the differences from its complementary classes. By virtue of the reconstruction problem, the class-specific subspace classifier (CSSC) problem could be represented as a series of biobjective optimization problems, which minimize and maximize the reconstruction errors of the related class and its complementary classes, respectively. Besides, the regularization term is specifically introduced to ensure the whole system's stability. Accordingly, a regularized class-specific subspace classifier (RCSSC) method can be further proposed based on solving a general quadratic ratio problem. The proposed RCSSC method consistently converges to the global optimal subspace and translation under the variations of the regularization parameter. Furthermore, the proposed RCSSC method could be extended to the unregularized case, which is known as unregularized CSSC (UCSSC) method via orthogonal decomposition technique. As a result, the effectiveness and the superiority of both proposed RCSSC and UCSSC methods can be verified analytically and experimentally. ? 2016 IEEE.Accession Number: 20174904513944 -
Record 147 of
Title:Automatic seamless image mosaic method based on SIFT features
Author(s):Liu, Meiying(1,2); Wen, Desheng(1)Source: Proceedings of SPIE - The International Society for Optical Engineering Volume: 10256 Issue: DOI: 10.1117/12.2257792 Published: 2017Abstract:An automatic seamless image mosaic method based on SIFT features is proposed. First a scale-invariant feature extracting algorithm SIFT is used for feature extraction and matching, which gains sub-pixel precision for features extraction. Then, the transforming matrix H is computed with improved PROSAC algorithm, compared with RANSAC algorithm,the calculate efficiency is advanced, and the number of the inliers are more. Then the transforming matrix H is purify with LM algorithm. And finally image mosaic is completed with smoothing algorithm. The method implements automatically and avoids the disadvantages of traditional image mosaic method under different scale and illumination conditions. Experimental results show the image mosaic effect is wonderful and the algorithm is stable very much. It is high valuable in practice. ? 2017 SPIE.Accession Number: 20171703607535 -
Record 148 of
Title:A novel design of subminiature star sensor's imaging system based on TMS320DM3730
Author(s):Liu, Meiying(1,2); Wang, Hu(1); Wen, Desheng(1); Yang, Shaodong(1)Source: Proceedings of SPIE - The International Society for Optical Engineering Volume: 10256 Issue: DOI: 10.1117/12.2257791 Published: 2017Abstract:Development of the next generation star sensor is tending to miniaturization, low cost and low power consumption, so the imaging system based on FPGA in the past could not meet its developing requirements. A novel design of digital imaging system is discussed in this paper. Combined with the MT9P031 CMOS image sensor's timing sequence and working mode, the sensor driving circuit and image data memory circuit were implemented with the main control unit TMS320DM3730. In order to make the hardware system has the advantage of small size and light weight, the hardware adopted miniaturization design. The software simulation and experimental results demonstrated that the designed imaging system was reasonable, the function of tunable integration time and selectable window readout modes were realized. The communication with computer was exact. The system has the advantage of the powerful image processing,small-size, compact, stable, reliable and low power consumption. The whole system volume is 40 mm ?40 mm ?40mm,the system weight is 105g, the system power consumption is lower than 1w. This design provided a feasible solution for the realization of the subminiature star sensor's imaging system. ? 2017 SPIE.Accession Number: 20171703607534 -
Record 149 of
Title:Fast polarimetric dehazing method for visibility enhancement in HSI colour space
Author(s):Zhang, Wenfei(1,2,3); Liang, Jian(1,3); Ren, Liyong(1); Ju, Haijuan(1,3); Bai, Zhaofeng(1); Wu, Zhaoxin(2)Source: Journal of Optics (United Kingdom) Volume: 19 Issue: 9 DOI: 10.1088/2040-8986/aa7f39 Published: August 22, 2017Abstract:Image haze removal has attracted much attention in optics and computer vision fields in recent years due to its wide applications. In particular, the fast and real-time dehazing methods are of significance. In this paper, we propose a fast dehazing method in hue, saturation and intensity colour space based on the polarimetric imaging technique. We implement the polarimetric dehazing method in the intensity channel, and the colour distortion of the image is corrected using the white patch retinex method. This method not only reserves the detailed information restoration capacity, but also improves the efficiency of the polarimetric dehazing method. Comparison studies with state of the art methods demonstrate that the proposed method obtains equal or better quality results and moreover the implementation is much faster. The proposed method is promising in real-time image haze removal and video haze removal applications. ? 2017 IOP Publishing Ltd.Accession Number: 20173604133858 -
Record 150 of
Title:Automatic identification of side branch and main vascular measurements in intravascular optical coherence tomography images
Author(s):Cao, Yihui(1,2,3); Jin, Qinhua(4); Chen, Yundai(4); Yin, Qinye(2); Qin, Xianjing(5); Li, Jianan(1); Zhu, Rui(1); Zhao, Wei(1)Source: Proceedings - International Symposium on Biomedical Imaging Volume: 0 Issue: DOI: 10.1109/ISBI.2017.7950594 Published: June 15, 2017Abstract:Automatic identification of side branch and main vascular measurements in IVOCT images take critical roles in pre-interventional decision making for coronary artery disease treatment. Very little works have been presented on these tasks. In this paper, we proposed a novel side branch identification algorithm which utilizes a newly defined global curvature feature to identify the ostium of side branch. Based on identification results, the main vascular can be segmented automatically for measurements. In the measurement of main vascular, the diameter of maximum inscribed circle of main vascular is proposed for the first time, which could be helpful in stent size decision. The qualitative and quantitative validation results demonstrated that the proposed algorithm is effective and accurate. ? 2017 IEEE.Accession Number: 20172903945431 -
Record 151 of
Title:Study on the optical properties of the off-axis parabolic collimator with eccentric pupil
Author(s):Gang, Li(1,2); Xin, Gao(3); Jing, Duan(1); Henjin, Zhang(1)Source: Proceedings of SPIE - The International Society for Optical Engineering Volume: 10256 Issue: DOI: 10.1117/12.2258223 Published: 2017Abstract:The off-axis parabolic collimator with eccentric pupil has the advantages of wide spectrum, simple structure, easy assembly and adjustment, high performance price ratio.So, it is widely used for parameters testing and image quality calibration of ground-based and space-based cameras. In addition to the Strehl ratio, resolution, wavefront aberration, modulation transfer function, the general evaluation criteria on the imaging quality of the optical system, the beam parallelism characterize the collimator angle resolving capability and collimation condition of the collimator with the target board, can be measured easily, quickly and operation process is simple, but the study mainly focus on how to measure it so far.In order to solve Quantitative calculation of this problem, firstly, the discussion of aberration condition of the off-axis parabolic is carried out based on the the primary aberration theory. Secondly, analysis on the influencing factor on collimator optical properties is given, including the geometrical aberrations of spherical aberration, coma, astigmatism, the relation between the position of the eccentric pupil and the aberration and optical element surface wavefront aberration, after that, according to the basis of diffraction and wavefront aberration theory, the paper deduced calculation method of the beam parallelism, at last, an example of a 400mm diameter off-axis parabolic collimator with eccentric pupil is given to caculate, the practical results shows that calculation data is well in accordance with actual measurement data and results can meet the demand and has a guiding significance to the actual project manufacture and the theory analysis. ? 2017 SPIE.Accession Number: 20171703607572 -
Record 152 of
Title:Dispersion management of a compact all fiber Yb doped NPE passive mode-locked oscillator by a tapered fiber
Author(s):Yang, Peilong(1,2); Hu, Zhongqi(1,2); Teng, Hao(2); Lv, Zhiguo(3); Wei, Zhiyi(2)Source: Optics InfoBase Conference Papers Volume: Part F41-CLEO_SI 2017 Issue: DOI: 10.1364/CLEO_SI.2017.SM4L.8 Published: 2017Abstract:We explored dispersion management of a NPE mode-locked Yb-doped all fiber oscillator by a tapered fiber, the compressed pulse duration of 116fs, power of 36mW , spectrum is widened to near 20nm. ? 2017 OSA.Accession Number: 20172403755708 -
Record 153 of
Title:High-efficiency supercontinuum generation in solid thin plates at 0.1 TW level
Author(s):He, Peng(1,2); Liu, Yangyang(2); Zhao, Kun(2); Teng, Hao(2); He, Xinkui(2); Huang, Pei(3); Huang, Hangdong(1); Zhong, Shiyang(2); Jiang, Yujiao(1); Fang, Shaobo(2); Hou, Xun(3); Wei, Zhiyi(2)Source: Optics Letters Volume: 42 Issue: 3 DOI: 10.1364/OL.42.000474 Published: February 1, 2017Abstract:Supercontinuum generation in a solid-state medium was investigated experimentally. A continuum covering 460 to 950 nm was obtained when 0.8 mJ/30 fs Ti:sapphire laser pulses were applied to seven thin fused silica plates at a 1 kHz repetition rate. The primary processes responsible for spectral broadening were self-phase modulation (SPM) and self-steepening, while SPM and self-focusing were balanced to optimize the spectral broadening and suppress the multiphoton process. The output was compressed to a 5.4 fs and a 0.68 mJ pulse, corresponding to two optical cycles and 0.13 TW of peak power. ? 2017 Optical Society of America.Accession Number: 20170603336018 -
Record 154 of
Title:Sparse Learning with Stochastic Composite Optimization
Author(s):Zhang, Weizhong(1); Zhang, Lijun(2); Jin, Zhongming(1); Jin, Rong(3); Cai, Deng(1); Li, Xuelong(4); Liang, Ronghua(5); He, Xiaofei(1)Source: IEEE Transactions on Pattern Analysis and Machine Intelligence Volume: 39 Issue: 6 DOI: 10.1109/TPAMI.2016.2578323 Published: June 1, 2017Abstract:In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate O(1/λT), but they often fail to deliver sparse solutions at the end either due to the limited sparsity regularization during stochastic optimization (SO) or due to the limitation in online-to-batch conversion. Even when the objective function is strongly convex, their high probability bounds can only attain O(log(1/δ)/T) with δ is the failure probability, which is much worse than the expected convergence rate. To address these limitations, we propose a simple yet effective two-phase Stochastic Composite Optimization scheme by adding a novel powerful sparse online-to-batch conversion to the general Stochastic Optimization algorithms. We further develop three concrete algorithms, OptimalSL, LastSL and AverageSL, directly under our scheme to prove the effectiveness of the proposed scheme. Both the theoretical analysis and the experiment results show that our methods can really outperform the existing methods at the ability of sparse learning and at the meantime we can improve the high probability bound to approximately O(log(log(T)/δ)/λT). ? 2016 IEEE.Accession Number: 20172003677493 -
Record 155 of
Title:Unsupervised large graph embedding
Author(s):Nie, Feiping(1); Zhu, Wei(1); Li, Xuelong(2)Source: 31st AAAI Conference on Artificial Intelligence, AAAI 2017 Volume: Issue: DOI: Published: 2017Abstract:There are many successful spectral based unsupervised dimensionality reduction methods, including Laplacian Eigenmap (LE), Locality Preserving Projection (LPP), Spectral Regression (SR), etc. LPP and SR are two different linear spectral based methods, however, we discover that LPP and SR are equivalent, if the symmetric similarity matrix is doubly stochastic, Positive Semi-Definite (PSD) and with rank p, where p is the reduced dimension. The discovery promotes us to seek low-rank and doubly stochastic similarity matrix, we then propose an unsupervised linear dimensionality reduction method, called Unsupervised Large Graph Embedding (ULGE). ULGE starts with similar idea as LPP, it adopts an efficient approach to construct similarity matrix and then performs spectral analysis efficiently, the computational complexity can reduce to O(ndm), which is a significant improvement compared to conventional spectral based methods which need O(n2d) at least, where n, d and m are the number of samples, dimensions and anchors, respectively. Extensive experiments on several public available data sets demonstrate the efficiency and effectiveness of the proposed method. Copyright ? 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.Accession Number: 20174104242975 -
Record 156 of
Title:Quantifying and detecting collective motion by manifold learning
Author(s):Wang, Qi(1); Chen, Mulin(1); Li, Xuelong(2)Source: 31st AAAI Conference on Artificial Intelligence, AAAI 2017 Volume: Issue: DOI: Published: 2017Abstract:The analysis of collective motion has attracted many researchers in artificial intelligence. Though plenty of works have been done on this topic, the achieved performance is still unsatisfying due to the complex nature of collective motions. By investigating the similarity of individuals, this paper proposes a novel framework for both quantifying and detecting collective motions. Our main contributions are threefold: (1) the time-varying dynamics of individuals are deeply investigated to better characterize the individual motion; (2) a structure-based collectiveness measurement is designed to precisely quantify both individual-level and scene-level properties of collective motions; (3) a multi-stage clustering strategy is presented to discover a more comprehensive understanding of the crowd scenes, containing both local and global collective motions. Extensive experimental results on real world data sets show that our method is capable of handling crowd scenes with complicated structures and various dynamics, and demonstrate its superior performance against state-of-the-art competitors. Copyright ? 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.Accession Number: 20174104243114