Rotationally invariant pattern recognition by use of linear and nonlinear cascaded filters

We discuss the merits of using single-layer (linear and nonlinear) and multiple-layer (nonlinear) filters for rotationally invariant and noise-tolerant pattern recognition. The capability of each approach is considered with reference to a two-class, rotation-invariant, character recognition problem. The minimum average correlation energy (MACE) filter is a linear filter that is generally accepted to be optimal for detecting signals that are free from noise. Here it is found that an optimized MACE filter cannot differentiate between the characters E and F in a rotation-invariant manner. We have found, however, that this task is possible when a single optimized linear filter is used to achieve the required response when a nonlinear threshold function is included after the filter. We show that this structure can be cascaded to form a multiple-layer, cascaded filter and that the capability of such a system is enhanced by its increased noise tolerance in the character recognition problem. Finally, we show the capability of a two-layer cascade as a means to detect different species of bacteria in images obtained from a phase-contrast microscope.     

Convolution-kernel-based optimal trade-off filters for optical pattern recognition

An architecture for the implementation of optical pattern recognition is proposed that makes use of convolution-kernel-based optimal trade-off filters to allow for an increased speed of operation and filter storage capability. The derivation of these new convolution-kernel-based optimal trade-off filters is presented, and their noise robustness and discrimination capabilities are discussed.     

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.     

Distortion-invariant pattern recognition with Fourier-plane nonlinear filters

The use of nonlinear techniques in the Fourier plane of pattern-recognition correlators can improve the correlators' performance in terms of discrimination against objects similar to the target object, correlation-peak sharpness, and correlation noise robustness. Additionally, filter designs have been proposed that provide the linear correlator with invariance properties with respect to input-signal distortions and rotations. We propose simple modifications to presently known distortion-invariant correlator filters that enable these filter designs to be used in a nonlinear correlator architecture. These Fourier-plane nonlinear filters can be implemented electronically, or they may be implemented optically with a nonlinear joint transform correlator. Extensive simulation results are presented that illustrate the performance enhancements that are gained by the unification of nonlinear techniques with these filter designs.     

Vectorial signatures for invariant recognition of position,rotation and scale pattern recognition

We investigate the development and utilization of vectorial signatures obtained from the application of properties of the scale and Fourier transform for images recognition. The signatures were applied to different input scenes. In order to find a new invariant digital system two one-dimensional vectors were calculated to achieve different mathematical transformations in the target as well as in the input scene. To recognize a target, signatures were compared, calculating the Euclidean distance between the target and the input scene. These sets are generated from two sources: alphabetical letters in Arial type and digital images of seven different copepod species. Considering the great similarity between the copepod species used in this work and between the male and female of each species the results obtained are very good.     

Optimum nonlinear composite filter for distortion-tolerant pattern recognition

We describe a nonlinear distortion-tolerant filter for pattern recognition that is optimum in terms of tolerance to input noise and discrimination capability. This filter was derived by minimization of the output energy that is due to the overlapping additive noise and the input scene, and the output of the filter meets the design constraints obtained from the training data set. The performance of this filter was tested with an input scene containing one of the training data sets, a nontraining true target, and a false object in the presence of overlapping additive noise and nonoverlapping background noise. We carried out Monte Carlo runs to measure the statistical performance of the filter and obtained receiver operating characteristics curves to show the detection capabilities of the filter.     

Nonlinear filter for pattern recognition invariant to illumination and to out-of-plane rotations

Automatic target recognition in uncontrolled conditions is a difficult task because many parametersare involved. This study deals with the recognition of targets under limited out-of-plane rotations while maintaining invariance to ambient light illumination. Contrast invariance is achieved by using the recently developed locally adaptive contrast-invariant filter, a method that yields correlation peaks whose values are invariant under any linear transformation of intensity. To reduce the sensitivity to the orientation of the object we replace the reference in the nonlinear filter by a synthetic discriminant filter. The range used for out-of-plane rotations was 40 degrees with a depression angle of 20 degrees. We present results for unsegmented targets on complex backgrounds with the presence of false targets.     

Rotation invariant color pattern recognition by use of a three-dimensional Fourier transform

Recently, the use of three-dimensional correlation for multichannel pattern recognition has been introduced. In this work we propose the use of circular harmonic components with this new technique to obtain invariance under target rotations. The differences between this method and the previous use of circular harmonic filters for multichannel images are discussed. Also the problem of determining the proper center is studied and, to our knowledge, a new and more understandable criterion to locate it is introduced. Some simulation results to verify the successful operation of the method are included.     

Well-being analysis for composite generation and transmission systems based on pattern recognition techniques

A new methodology to evaluate well-being indices for a composite generation and transmission system, based on non-sequential Monte Carlo simulation and pattern recognition techniques, is presented. To classify the success operating states into healthy and marginal, an artificial neural network based on group method data handling techniques is used to capture the patterns of these state classes, during the beginning of the simulation process. The idea is to provide the simulation process with an intelligent memory, based on polynomial parameters, to speed up the evaluation of the operating states. The proposed methodology is applied to the IEEE reliability test system (IEEE-RTS), to the IEEE-RTS-96 and to a configuration of the Brazilian South-Southeastern system.     

Wavelet-transform-based method of analysis for Lamb-wave ultrasonicNDE signals

A new acoustic nondestructive method using Lamb waves as a probe is presented. These waves are generated and received by an ElectroMagnetic Acoustic Transducer (EMAT). The position of flaws in the structure under test is computed from the time of arrival of the main peak of the reflected signal. Due to the noisy nature of the received signal, we use a wavelet transform algorithm to extract the required time information. The main advantage of such a multi-scale method of signal analysis is to be suitable for peak detection problems especially in highly noisy environments. We explain how we proceed to do the feature extraction, and we propose two methods for reconstructing the image of the inspected structure. Results of real-world ultrasonic Lamb waves signal analysis are presented. In addition, to test the noise robustness of the method, the case of synthetic noisy signals is also treated     

Distance-classifier correlation filters for multiclass target recognition

We describe a correlation-based distance-classifier scheme for the recognition and the classification of multiple classes. The underlying theory uses shift-invariant filters to compute distances between the input image and ideal references under an optimum transformation. The original distance-classifier correlation filter was developed for a two-class problem. We introduce a distance-classifier correlation filter that simultaneously considers multiple classes, and we show that the earlier two-class formulation is a special case of the classifier presented. Initial results are presented to demonstrate the discrimination- and distortion-tolerance capabilities of the proposed filter.     

Application of correlation filters for texture recognition

We propose a new statistical method to design spatial filters to recognize and to discriminate between various textures. Unlike existing correlation filters, the proposed filters are not meant to recognize specific shapes or objects. Rather, they discriminate between textures such as terrains, background surfaces, and random image fields. The filters do not require any on-line statistical computations for extracting texture information. Therefore optical (or digital) correlators can be used for fast real-time texture recognition without segmentation. The procedure is based on the assumption that textures can be modeled as stationary random processes over limited regions of an image. The optimum filter coefficients are determined by use of eigenvector analysis. Several examples are given to illustrate the proposed scheme.     

Noise robustness of nonlinear filters for image recognition

We analyze the performance of the Fourier plane nonlinear filters in terms of signal-to-noise ratio (SNR). We obtain a range of nonlinearities for which SNR is robust to the variations in input-noise bandwidth. This is shown both by analytical estimates of the SNR for nonlinear filters and by experimental simulations. Specifically, we analyze the SNR when Fourier plane nonlinearity is applied to the input signal. Using the Karhunen-Loève series expansion of the noise process, we obtain precise analytic expressions of the SNR for Fourier plane nonlinear filters in the presence of various types of additive-noise processes. We find a range of nonlinearities that need to be applied that keep the output SNR of the filter stable relative to changes in the noise bandwidth.     

Nonlinear cascaded correlation processes to improve the performances of automatic spatial-frequency-selective filters in pattern recognition

A recognition process consisting of two cascaded correlation stages with a sigmoid nonlinearity applied in the first correlation plane is investigated. The filters are computed to give prespecified central correlation amplitudes in the second correlation plane when inputs are reference images. It is also desired that the second correlation amplitudes with the training images should minimize the cost function of the automatic spatial-frequency selection algorithm to reduce distortion sensitivity and to improve the performance of the filters. Filter computation methods are given, and it is shown why two such correlation processes may improve the correlation performance. Numerical simulations are described and compared with the one-stage correlation system that works with the automatic spatial-frequency selection filter.     

Spherical nonlinear correlations for global invariant three-dimensional object recognition

We define a nonlinear filtering based on correlations on unit spheres to obtain both rotation- and scale-invariant three-dimensional (3D) object detection. Tridimensionality is expressed in terms of range images. The phase Fourier transform (PhFT) of a range image provides information about the orientations of the 3D object surfaces. When the object is sequentially rotated, the amplitudes of the different PhFTs form a unit radius sphere. On the other hand, a scale change is equivalent to a multiplication of the amplitude of the PhFT by a constant factor. The effect of both rotation and scale changes for 3D objects means a change in the intensity of the unit radius sphere. We define a 3D filtering based on nonlinear operations between spherical correlations to achieve both scale- and rotation-invariant 3D object recognition.     

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.     

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.     

Circular harmonic filters for the recognition of marine microorganisms

We present an application of circular-harmonic filters (CHF's) for the recognition of planktonic microorganisms. CHF's discriminated both genera Acartia and Calanus. The symmetry of genus Acartia permitted discrimination to the species and sex levels, whereas the asymmetry of the genus Calanus permitted discrimination only to the generic level. The differences among organisms of different sex of the genus Calanus could not be detected by these particular CHF's. More research needs to be carried out with more complex CHF's to enhance their performance and to permit the implementation of an automated optodigital system to identify and count marine microorganisms.     

基于不变特征的标志识别技术研究

标志识别是近距离空间交会对接中的一项重要的关键技术.在空间交会对接过程中,太阳和月球等各种天体以及飞行器上零部件的辐射与反射都会影响CCD的成像,形成大量杂散光干扰.为此,本文首先设计了具有不变特征的标志系统,然后根据标志系统固有的不变特征,详细设计了左右像点匹配、远场像平面不变特征识别、近场像平面预测识别以及像空间模型匹配全等识别等一套完整算法,可以保证彻底消除杂散光的干扰,正确可靠地完成各个标志灯像点的辨识.模拟图像仿真实验和真实图像仿真实验结果验证了算法的正确性和可靠性.