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Sparsity-motivated automatic target recognition

APPLIED OPTICS 2011年第10期50卷 1425-1433页

作者： Patel, Vishal M. Nasrabadi, Nasser M. Chellappa, Rama Univ Maryland Dept Elect & Comp Engn Ctr Automat Res College Pk MD 20742 USA US Army Res Lab Adelphi MD 20783 USA

We present an automatic target recognition algorithm using the recently developed theory of sparse representations and compressive sensing. We show how sparsity can be helpful for efficient utilization of data for target recognition. We verify the efficacy of the proposed algorithm in terms of the recognition rate and confusion matrices on the well known Comanche (Boeing-Sikorsky, USA) forward-looking IR data set consisting of ten different military targets at different orientations. (C) 2011 Optical Society of America

关键词： Attenuated total reflectance Forward looking infrared image quality image recognition algorithms Neural networks Synthetic aperture radar

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High-precision displacement measurement algorithm based on a depth fusion of grating projection pattern

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APPLIED OPTICS 2022年第4期61卷 1049-1056页

作者： Yu, Hai Wan, Qiuhua Lu, Xinran Du, Yingcai Liang, Lihui Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China

Displacement measurement based on an image recognition algorithm has more advantages in the field of displacement measurement. Due to the uncertainty of image location, the accuracy of the displacement measurement algorithm studied in the previous stage is limited. To improve the accuracy of image-type displacement measurement, a high-precision displacement measurement algorithm based on depth fusion of the grating projection pattern is proposed. First, a line-scan image sensor and parallel light source are placed on both sides of the calibration grating. The marking pattern on the calibration grating is projected onto the image sensor through illumination of the parallel light source to realize the projection imaging. Then, the centroid algorithm is used to calculate the centroid of each line in the collected image, and the mean value of the centroid group is determined. Finally, a linear function is used to fit multiple centroids to calculate the endpoints of the subdivision region, and the subdivision operation is carried out according to a ratio algorithm. To test the performance of the proposed algorithm, a linear displacement measurement device with a 250 mm range and an angular displacement measurement device with a 100 mm diameter grating are both used for experiments. After testing, the linear measurement accuracy of the proposed algorithm is improved from 4.63 to 1.26 mu m, and the angular displacement measurement accuracy is enhanced from 8.87 to 2.88 ''. The research in this paper provides a theoretical basis for high-performance displacement measurement. (C) 2022 Optica Publishing Group

关键词： Digital image processing image detection systems image processing algorithms image recognition algorithms image sensors Spatial resolution

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A de-illumination scheme for face recognition based on fast decomposition and detail feature fusion

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OPTICS EXPRESS 2013年第9期21卷 11294-11308页

作者： Zhou, Yi Zhou, Sheng-Tong Zhong, Zuo-Yang Li, Hong-Guang Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

Almost all the face recognition algorithms are unsatisfied due to illumination variation. Feature with high frequency represents the face intrinsic structure according to the common assumption that illumination varies slowly and the face intrinsic feature varies rapidly. In this paper, we will propose an adaptive scheme based on FBEEMD and detail feature fusion. FBEEMD is a fast version of BEEMD without time-consuming surface interpolation and iteration computation. It can decompose an image into sub-images with high frequency matching detail feature and sub-images with low frequency corresponding to contour feature. However, it is difficult to determine by quantitative analysis that which sub-images with high frequency can be used for reconstructing an illumination-invariant face. Thus, two measurements are proposed to calculate weights for quantifying the detail feature. With this fusion technique, one can reconstruct a more illumination-neutral facial image to improve face recognition rate. Verification experiments using classical recognition algorithms are tested with Yale B, PIE and FERET databases. The encouraging results show that the proposed scheme is very effective when dealing with face images under variable lighting condition. (c) 2013 Optical Society of America

关键词： Adaptive imaging Face recognition Feature extraction image processing image recognition algorithms Optical fields

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Quantitative optical diagnostics on macroscopic soot onset for ethylene diffusion flames with ethyl ester addition

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OPTICS EXPRESS 2022年第12期30卷 21410-21422页

作者： Zou, Zhiyu Sun, Haowei Chen, Chen Zhao, Xuan Huang, Qin Ying, Yaoyao Li, Tianjiao Xu, Lei Liu, Dong Nanjing Univ Sci & Technol Sch Energy & Power Engn MIIT Key Lab Thermal Control Elect Equipment Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Energy & Power Engn Adv Combust Lab Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch New Energy Jiangyin 214443 Peoples R China

A novel quantitative optical diagnostics method for determining the threshold of soot onset in counterflow diffusion flames was proposed and demonstrated. The method was based on the proportional discrimination of trichromatic luminescence and the nonparametric and unsupervised automatic threshold selection algorithm. The macroscopic soot onset threshold in ethylene diffusion flame with three ethyl esters additions could be precisely determined. It was found that the undesirable soot onset phenomenon for ethylene diffusion flames was significantly suppressed with ethyl ester addition. The method proposed here will be useful as a reference for soot diagnostics in other flames. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： CCD cameras Combustion Diffusion Flames image recognition algorithms Light sources

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3-D face registration solution with speckle encoding based spatial-temporal logical correlation algorithm

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OPTICS EXPRESS 2019年第15期27卷 21004-21019页

作者： Zhou, Pei Zhu, Jiangping You, Zhisheng Sichuan Univ Sch Aeronaut & Astronaut Chengdu 610065 Sichuan Peoples R China Sichuan Univ Natl Key Lab Fundamental Sci Synthet Vis Chengdu 610065 Sichuan Peoples R China

3-D information acquisition (registration) of whole face plays a significant role in 3-D human face recognition application. In this paper, we develop a prototype of 3-D system consisting of two binocular measurement units that allows a full 3-D reconstruction by utilizing the advantages of a novel correlation algorithm. In this system, we use optical modulation to produce temporally and spatially varying high-density binary speckle patterns to encode the tested face, then propose a spatial-temporal logical correlation (STLC) stereo matching algorithm to fast determine the accurate disparity with a coarse and refined strategy. Finally the 3-D information of whole face from left- and right ear (similar to 180 degrees) can be obtainable by fusing the data from two measurement units. Comparative researches are performed to test a plastic model and a real human face by simulating real application situations. The results verify the feasibility and good performances of our computational frameworks and experimental configuration in terms of accuracy and time cost, which show a good application prospect in our future 3-D human face recognition research. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement1.

关键词： High power lasers image processing image recognition algorithms Speckle patterns Stereo matching Time division multiplexing

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Spectral optimized asymmetric segmented phase-only correlation filter

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APPLIED OPTICS 2012年第14期51卷 2638-2650页

作者： Leonard, I. Alfalou, A. Brosseau, C. Univ Brest Lab Sci & Tech Informat Commun & Connaissance Univ Europeenne Bretagne F-29238 Brest 3 France ISEN Dept Vis Lab Rech F-29228 Brest 2 France

We suggest a new type of optimized composite filter, i.e., the asymmetric segmented phase-only filter (ASPOF), for improving the effectiveness of a VanderLugt correlator (VLC) when used for face identification. Basically, it consists in merging several reference images after application of a specific spectral optimization method. After segmentation of the spectral filter plane to several areas, each area is assigned to a single winner reference according to a new optimized criterion. The point of the paper is to show that this method offers a significant performance improvement on standard composite filters for face identification. We first briefly revisit composite filters [adapted, phase-only, inverse, compromise optimal, segmented, minimum average correlation energy, optimal trade-off maximum average correlation, and amplitude-modulated phase-only (AMPOF)], which are tools of choice for face recognition based on correlation techniques, and compare their performances with those of the ASPOF. We illustrate some of the drawbacks of current filters for several binary and grayscale image identifications. Next, we describe the optimization steps and introduce the ASPOF that can overcome these technical issues to improve the quality and the reliability of the correlation-based decision. We derive performance measures, i.e., PCE values and receiver operating characteristic curves, to confirm consistency of the results. We numerically find that this filter increases the recognition rate and decreases the false alarm rate. The results show that the discrimination of the ASPOF is comparable to that of the AMPOF, but the ASPOF is more robust than the trade-off maximum average correlation height against rotation and various types of noise sources. Our method has several features that make it amenable to experimental implementation using a VLC. (C) 2012 Optical Society of America

关键词： Face recognition Fourier transforms image recognition algorithms Joint transform correlators Phase only filters Target tracking

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Observation of laser-induced elastic waves in agar skin phantoms using a high-speed camera and a laser-beam-deflection probe

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BIOMEDICAL OPTICS EXPRESS 2018年第4期9卷 1893-1905页

作者： Lalos, Jernej Gregorcic, Peter Jezersek, Matija Univ Ljubljana Fac Mech Engn Askerceva Cesta 6 Ljubljana 1000 Slovenia

We present an optical study of elastic wave propagation inside skin phantoms consisting of agar gel as induced by an Er: YAG (wavelength of 2.94 mu m) laser pulse. A laserbeam- deflection probe is used to measure ultrasonic propagation and a high-speed camera is used to record displacements in ablation-induced elastic transients. These measurements are further analyzed with a custom developed image recognition algorithm utilizing the methods of particle image velocimetry and spline interpolation to determine point trajectories, material displacement and strain during the passing of the transients. The results indicate that the ablation-induced elastic waves propagate with a velocity of 1 m/s and amplitudes of 0.1 mm. Compared to them, the measured velocities of ultrasonic waves are much higher, within the range of 1.42-1.51 km/s, while their amplitudes are three orders of magnitude smaller. This proves that the agar gel may be used as a rudimental skin and soft tissue substitute in biomedical research, since its polymeric structure reproduces adequate soft-solid properties and its transparency for visible light makes it convenient to study with optical instruments. The results presented provide an insight into the distribution of laser-induced elastic transients in soft tissue phantoms, while the experimental approach serves as a foundation for further research of laser-induced mechanical effects deeper in the tissue. (c) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： image recognition algorithms Laser systems Laser velocimetry Optical devices Visible light Wave propagation

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Microwave photonics interrogation for multiplexing fiber Fabry-Perot sensors

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OPTICS EXPRESS 2021年第11期29卷 16652-16664页

作者： Wu, Nishan Xia, Min Wu, Ying Li, Shiyu Qi, Ruiling Huang, Yuhao Xia, Li Huazhong Univ Sci & Technol Sch Opt & Elect Informat Wuhan 430074 Peoples R China

A microwave photonics interrogation system for multiplexing fiber Fabry-Perot (FP) sensors is demonstrated in this paper. Different from previous FP demodulation schemes, this system aims at quasi-distributed sensing networks composed of FP sensors with a short effective cavity length less than 1 mm. With the help of a dispersion element, the superimposed reflected spectrum from FP sensors based on a hollow core fiber (HCF) can be converted into separate response passbands in the frequency domain simultaneously, whose center frequency will shift linearly with the variations of environment. The experimental results exhibit high linearity and interrogation ability for both the all-FP multiplexing system and hybrid multiplexing system. A strain interrogation sensitivity of 0.938 kHz/mu epsilon and temperature sensitivity of -0.699 MHz/degrees C have been realized, corresponding to a FP cavity length demodulation sensitivity of 1.563 MHz/mu m. Furthermore, numerical studies about the impacts of the HCF-FP spectrum envelope on the RF response passband, as well as the theoretical minimum detectable cavity length and multiplexing capacity of the system, are also carried out. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Fiber optic sensors Hollow core fibers image recognition algorithms Microwave photonic filters Microwave photonics Optical frequency domain reflectometry

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Face recognition performance with superresolution

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APPLIED OPTICS 2012年第18期51卷 4250-4259页

作者： Hu, Shuowen Maschal, Robert Young, S. Susan Hong, Tsai Hong Phillips, P. Jonathon USA Res Lab Adelphi MD 20783 USA NIST Gaithersburg MD 20899 USA

With the prevalence of surveillance systems, face recognition is crucial to aiding the law enforcement community and homeland security in identifying suspects and suspicious individuals on watch lists. However, face recognition performance is severely affected by the low face resolution of individuals in typical surveillance footage, oftentimes due to the distance of individuals from the cameras as well as the small pixel count of low-cost surveillance systems. Superresolution image reconstruction has the potential to improve face recognition performance by using a sequence of low-resolution images of an individual's face in the same pose to reconstruct a more detailed high-resolution facial image. This work conducts an extensive performance evaluation of superresolution for a face recognition algorithm using a methodology and experimental setup consistent with real world settings at multiple subject-to-camera distances. Results show that superresolution image reconstruction improves face recognition performance considerably at the examined midrange and close range.

关键词： Face recognition Fourier transforms image enhancement image recognition algorithms image reconstruction Superresolution

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Illumination invariant recognition and 3D reconstruction of faces using desktop optics

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OPTICS EXPRESS 2011年第8期19卷 7491-7506页

作者： Mian, Ajmal Univ Western Australia Crawley WA 6009 Australia

We propose illumination invariant face recognition and 3D face reconstruction using desktop optics. The computer screen is used as a programmable extended light source to illuminate the face from different directions and acquire images. Features are extracted from these images and projected to multiple linear subspaces in an effort to preserve unique features rather than the most varying ones. Experiments were performed using our database of 4347 images (106 subjects), the extended Yale B and CMU-PIE databases and better results were achieved compared to the existing state-of-the-art. We also propose an efficient algorithm for reconstructing the 3D face models from three images under arbitrary illumination. The subspace coefficients of training faces are used as input patterns to train multiple Support Vector Machines (SVM) where the output labels are the subspace parameters of ground truth 3D face models. Support Vector Regression is used to learn multiple functions that map the input coefficients to the parameters of the 3D face. During testing, three images of an unknown/novel face under arbitrary illumination are used to estimate its 3D model. Quantitative results are presented using our database of 106 subjects and qualitative results are presented on the Yale B database. (C) 2011 Optical Society of America

关键词： Face recognition image recognition algorithms Laser scanning Light sources Reconstruction algorithms Three dimensional reconstruction

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