Hybrid incoherent optical pattern recognition system

A pattern recognition system that uses incoherent spatial filtering to recognize images directly from a narrowband phosphor television monitor is described. Images of real objects are captured with a television camera. These images are then edge-enhanced electronically and displayed on the TV monitor. The monitor output is used directly as the input to a holographic correlator. An optical multichannel analyzer at the correlation plane is used to analyze the shape of the correlation function and to determine the position of its peak. Experimental results agree well with theory. Concepts for handling rotation, aspect angle, and scale variations of the input are discussed.     

Hybrid optical-digital signal processing applied to an optimal nonlinear phase estimator

An electrooptical realization of an optimal phase estimator for phase modulated communication signals is described in this work. The realization uses an electrooptical processor to perform a 1-D convolution in a 2-D space and a computer to complete the calculations. The processor is a realization of the time domain recursive nonlinear filter. This paper describes the nonlinear filter theory, the electrooptical realization,and the performance of the processor.     

Hybrid optical-digital method for local-displacement analysis by use of a phase-space representation

A method for evaluating the local deformation or displacement of an object in speckle metrology is described. The local displacements of the object in one direction are digitally coded in a one-dimensional specklegram. By optically performing the local spectrum of this pattern, one simultaneously achieves information about the local displacement and its spatial position. The good performance of this technique is demonstrated with computer-generated test signals.     

A fast track pattern recognition

An algorithm for fast track pattern recognition is presented. Its main features are an optimized data organization and a conformal mapping to speed up fitting procedures. The code is applied to a generic collider detector. Reconstruction time, track finding efficiencies, and momentum resolution are presented.     

Experimental systems implementation of a hybrid optical-digital correlator

A high-speed hybrid optical-digital correlator system was designed, constructed, modeled, and demonstrated experimentally. This correlator is capable of operation at approximately 3000 correlations/s. The input scene is digitized at a resolution of 512 x 512 pixels and the phase information of the two-dimensional fast Fourier transform calculated and displayed in the correlator filter plane at normal video frame rates. High-fidelity reference template images are stored in a phase-conjugating optical memory placed at the nominal input plane of the correlator and reconstructed with a high-speed acousto-optic scanner; this allows for cross correlation of the entire reference data set with the input scene within one frame period. A high-speed CCD camera is used to capture the correlation-plane image, and rapid correlation-plane processing is achieved with a parallel processing architecture.     

Realizations of focus invariance in optical-digital systems with wave-front coding

We report experimental verification of an extended depth of focus (EDF) system with near-diffraction-limited performance capabilities. Dowski and Cathey [Appl. Opt. 34, 1859-1866 (1995)] described the theory of this system in detail. We can create an EDF system by modifying a standard incoherent optical system with a special cubic phase plate placed at the aperture stop. We briefly review the theory and present the first optical experimental verification of this EDF system. The phase plate codes the wave front, producing a modified optical transfer function. Once the image is transformed into digital form, a signal-processing step decodes the image and produces the final in-focus image. We have produced a number of images from various optical systems using the phase plate, thus demonstrating the success of this EDF system.     

Artificial neural networks for pattern recognition

This tutorial article deals with the basics of artificial neural networks (ANN) and their applications in pattern recognition. ANN can be viewed as computing models inspired by the structure and function of the biological neural network. These models are expected to deal with problem solving in a manner different from conventional computing. A distinction is made between pattern and data to emphasize the need for developing pattern processing systems to address pattern recognition tasks. After introducing the basic principles of ANN, some fundamental networks are examined in detail for their ability to solve simple pattern recognition tasks. These fundamental networks together with the principles of ANN will lead to the development of architectures for complex pattern recognition tasks. A few popular architectures are described to illustrate the need to develop an architecture specific to a given pattern recognition problem. Finally several issues that still need to be addressed to solve practical problems using ANN approach are discussed. This paper is mostly a consolidation of work reported by several researchers in the literature, some of which is cited in the references. The author has borrowed several ideas and illustrations from the references quoted in this paper.     

Amplitude-modulated circular-harmonic filter for pattern recognition

An amplitude-modulated circular-harmonic filter is proposed for rotation-invariant pattern recognition. We investigate the filter characteristics by varying two design parameters, A(ρ) and B(ρ), and select optimum values to design an amplitude-modulated circular-harmonic filter. When compared with the phase-only circular-harmonic filter, the amplitude-modulated circular-harmonic filter is found to yield a sharper correlation peak, a better noise tolerance, and an improved correlation discrimination.     

基于人体手指指节折痕的身份识别方法

基于人体生物特征的认证是鉴别个人身份的有效方法.鉴于人体的手指折痕具有稳定性且对于不同的人具有不相同的特点,本文提出了一种基于手指折痕的身份识别的新方法.该算法系统由三部分组成:图像预处理、特征提取和特征匹配.在预处理阶段,提出了基于中心坐标轴的图像定位和归一化方法,并在手指内侧分割出了用于识别的长方形窗口形状的手指子图(ROI),ROI包含了手指的第一和第二折痕线;在第二阶段,提出了基于Radon变换和奇异值分解的特征提取方法;最后利用基于欧氏距离的最近邻分类器在一个取自61人共488幅手指图像的数据库上进行了匹配试验,结果验证了该方法是可行和有效的(等错误率为2.51%).     

Invariant pattern recognition based on centroids

A new method for pattern recognition that is invariant under changes of position, orientation, intensity, and scale is presented. The centroids of objects provide unique points that are related to the energy distribution. For obtaining more such unique points a conformal transform can be used to rearrange the energy distribution of the object. By means of the conformal transform many different centroids can be produced from the same object. A useful pattern-recognition and object-registration method that yields a position-, rotation-, intensity-, and scale-invariant feature vector based on these centroids can be created.     

Continuous-scale-invariant pattern recognition with adaptive-wavelet-matched filters

We designed and implemented a composite-wavelet-matched filter that is invariant to continuous-scale changes of the input and is useful for correlation-based pattern recognition of objects whose size is not known exactly. We optimize the adaptive wavelet to extract sparse image features and use the scale-space analysis to determine the wavelet scale for the scale invariance. Experimental results obtained by use of a programmable optical correlator with three liquid-crystal spatial light modulators are demonstrated.     

The metric tensor model of pattern recognition

Kolossváry, I., 1988. The metric tensor model of pattern recognition. Chemometrics and Intelligent Laboratory Systems, 4: 163–169.The classification criteria of pattern recognition methods used in analytical chemistry are based on some distance function in the N-dimensional Euclidean space. In this paper a concept based on alternative geometrical considerations is presented. It is shown that an efficient classification criterion can be defined by a new class of similarity measure, using the so-called scalar curvature function of Riemannian warped spaces. The theory and algorithm of a new pattern recognition method based on this idea, called the metric tensor model (MTM) is presented. A special version of MTM is programmed and shown to perform very well with respect to its recognition and prediction ability, on the IRIS data set.     

Wavelet-transform-based composite filters for invariant pattern recognition

A wavelet-transformation-based optical processor for performing invariant pattern recognition is suggested. It contains a composite filter that consists of several wavelet daughter functions derived from the reference object. The intensity of the correlation peak is determined to be invariant to various deformations of the reference object. Computer simulations show explicitly the promising capability of the new technique. Laboratory experimental results are given.     

Decision tree based control chart pattern recognition

This paper presents a new approach to classify six anomaly types of control chart patterns (CCP), of systematic pattern, cyclic pattern, upward shift, downward shift, upward trend, and downward trend. Current CCP recognition methods use either unprocessed raw data or complex transformed features (via principal component analysis or discrete wavelet transform) as the input representation for the classifier. The objective of using selected features is not only for dimension reduction of input representation, but also implies the process of data compression. In contrast, using raw data is often computationally inefficient while using transformed features is very tedious in most cases. Therefore, owing to its computational advantage, using appropriate features of CCP to achieve good classification accuracy becomes more promising in real process implementation. In this study, using three features of CCP shows quite a competitive performance in terms of classification accuracy and computational loading. More importantly, the proposed method presented here has potential to be generalized to medical, financial, and other application of temporal data.