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Laser-diode phase-shifting joint-transform correlator with multiple-object recognition

OPTICAL REVIEW 2006年第2期13卷 53-63页

作者： Takahashi, Takeshi Ishii, Yukihiro Univ Ind Technol Dept Elect Syst Engn Sagamihara Kanagawa 2291196 Japan Tokyo Univ Sci Dept Appl Phys Shinjuku Ku Tokyo 1628601 Japan

A joint-transform phase correlation is made by an extracted phase from the joint power spectrum using a phase-shifting Mach-Zehnder interferometer with a wave length-shifted laser diode (LD), and then b numerically Fourier-transforming a measured phase. The algorithm in six steps is insensitive to the power changes in an LD. The phase correlator gives the best discrimination performance. Discriminative multiple-object recognition is performed with no intermodulation noise and artifact noise-free correlation by arranging the multiple objects in a regularly equal-spaced array. The experimental and numerical results are shown. (c) 2006 The Optical Society of Japan.

关键词： laser diode wavelength tunability phase-shifting interferometer joint-transform phase correlator multiple-object recognition

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Optical morphological SDF filter for multiple-object recognition 9

Optical morphological SDF filter for multiple-object recogni...

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Conference on Optical Pattern recognition IX

作者： Kim, JC Kim, JW Lee, HW Doh, YH Kim, SJ Kyungpook Jr Coll Dept Elect Engn Kyungpook 750050 South Korea

ISBN: (纸本)0819428353

A new nonlinear morphological detection algorithm is proposed for input scenes with a number of objects present in a clutter. It uses a synthetic discriminant function(SDF) to form the matched spatial filter(MSF) of the structuring element(SE) used in the hit-miss transform(HMT) detection. The SDF synthetic technique is used to adapt intraclass distortions and interclass similarity, where an HMT is used to adapt noisy and cluttered scenes. Simulation results show the proposed algorithm is an attractive nonlinear optical image processing technique that can be applied to an HMT detection to improve both the false alarm rate and its ability to detect multiple-object with distortions and clutter.

关键词： hit-miss transform multiple-object recognition synthetic discriminant function filter

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NEURAL NETWORKS FOR COLLECTIVE TRANSLATIONAL INVARIANT object recognition

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International Journal of Pattern recognition and Artificial Intelligence 1992年第1期6卷 143-156页

作者： LAI-WAN CHAN Computer Science Department The Chinese University of Hong Kong Shatin N. T. Hong Kong China

A novel method using neural networks for translational invariant object recognition is described in this paper. The objective is to enable the recognition of objects in any shifted position when the objects are presented to the network in only one standard location during the training procedure. With the presence of multiple or overlapped objects in the scene, translational invariant object recognition is a very difficult task. Noise corruption of the image creates another difficulty. In this paper, a novel approach is proposed to tackle this problem, using neural networks with the consideration of multiple objects and the presence of noise. This method utilizes the secondary responses activated by the backpropagation network. A confirmative network is used to obtain the object identification and location, based on these secondary responses. Experimental results were used to demonstrate the ability of this approach.

关键词： Neural networks backpropagation network pattern recognition invariant object recognition multiple-object recognition

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Phase-shifting joint-transform correlator with a wavelength-shifted laser diode

Phase-shifting joint-transform correlator with a wavelength-...

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Conference on Optical Information Processing Technology

作者： Takahashi, T Ishii, Y Univ Ind Technol Sagamihara Dept Elect Sagamihara Kanagawa 2291196 Japan

ISBN: (纸本)0819447188

A joint-transform phase correlator is presented for processing multiple-pattern recognition that uses the six-step phase-shifting interferometer with a wavelength-shifted laser diode. The correlation peaks are obtained by the numerical Fourier transformation of a measured phase in an on-axis joint power spectrum. multiple-pattern phase correlation can be performed to demonstrate high discrimination without the intermodulation owing to zero optical path differences between multiple targets in the interferometer. The experimental results are shown.

关键词： laser diode wavelength tunability phase-shifting interferometer joint-transform phase correlator multiple-object recognition

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Laser-diode joint-transform correlator with a digital interferometric technique

Laser-diode joint-transform correlator with a digital interf...

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International Conference on Optical Engineering for Sensing and Nanotechnology (ICOSN 2001)

作者： Ishii, Y Takahashi, T Univ Ind Teachnol Dept Elect Sagamihara Kanagawa 2291196 Japan

ISBN: (纸本)081944121X

A new method for joint-transform correlator (JTC) is presented in which complex amplitudes of joint power spectrum (JPS) of two inputs are measured by the six-step phase-shifting interferometry with a wavelength-shifted laser diode (LD). A phase calculation is immune to the changes in LD power. A joint-transform phase correlator (JTPC) is demonstrated with a single centered correlation by the numerical Fourier transformation of a measured phase in an on-axis JPS. multiple-object recognition by a JTPC is performed to eliminate the correlation between input targets. The experimental results with a JTPC are shown, exhibiting sharp correlation peak.

关键词： laser diode wavelength tunability phase-shifting interferometer joint-transform phase correlator multiple-object recognition

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Adaptive learning method for multiple-object detection in manufacturing

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ADVANCES IN MECHANICAL ENGINEERING 2015年第12期7卷 1687814015618906-1687814015618906页

作者： Lin, Tsun-Kuo Shih Chien Univ Dept Informat Technol & Commun Kaohsiung 84550 Taiwan

In manufacturing, vision-based inspections are effective nondestructive methods for implementing object discrimination. To minimize inspection times, multiple-object discrimination must be implemented for one inspection image. However, multiple-object inspections are difficult to perform in manufacturing because of the lack of distinct objects in inspection images. In addition, inspectors might fail to use multiple sensing devices when concurrently detecting objects. This article proposes a novel multiple-object sensing system that incorporates a local-adaptive-region-growing-based learning method for adaptively segmenting multiple-camera images for multiple-object discrimination. The proposed local-adaptive-region-growing method and support vector machine-based discrete wavelet transform can effectively classify multiple objects in the local subregions of inspection images. The proposed system bridges the gap between sensing devices and inspectors for solving problems encountered when concurrently using multiple sensing devices. The learning method yielded more favorable results than existing inspection methods have. Furthermore, a standard test series was developed for quantitatively comparing inspection methods.

关键词： Automated inspection learning method local-adaptive-region-growing multiple-object recognition support vector machine

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DESIGNING A BINARY FOURIER-PHASE-ONLY CORRELATION FILTER USING A SIMULATED ANNEALING ALGORITHM

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JAPANESE JOURNAL OF APPLIED PHYSICS PART 2-LETTERS & EXPRESS LETTERS 1990年第11期29卷 L2074-L2076页

作者： NOMURA, T NAGASE, T ITOH, K ICHIOKA, Y Osaka University Department of Applied Physics

A method of designing a multiple-object recognition filter using a simulated annealing algorithm is presented. This filter has only binary phase information and is correlated with a binary Fourier-phase component of a test object. The filter is used in an incoherent-optical/digital-electric hybrid system. Computer simulations confirm the superior performance of this filter.

关键词： Binary phase-only filter Hybrid system multiple-object recognition Optical pattern recognition Simulated annealing

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