Log-polar transform-based wavelet-modified maximum average correlation height filter for distortion invariance in a hybrid digital-optical correlator

We discuss and implement a log-polar transform-based distortion-invariant filter for automatic target recognition applications. The log-polar transform is a known space-invariant image representation used in several image vision systems to eliminate the effects of scale and rotation in an image. For in-plane rotation invariance and scale invariance, a log-polar transform-based filter was synthesized. In cases of in-plane rotation invariance, peaks shift horizontally, and in cases of scale invariance, peaks shift vertically. To achieve out-of-plane rotation invariance, log-polar images were used to train the wavelet-modified maximum average correlation height (WaveMACH) filter. The designed filters were implemented in the hybrid digital-optical correlation scheme. It was observed that, for a certain range of rotation and scale differences, the correlation signals merge with the strong dc. To solve this problem a shift was introduced in the log-polar image of the target. The use of a chirp function for dc removal has also been discussed. Correlation peak height and peak-to-sidelobe ratio have been calculated as metrics of goodness of the log-polar transform-based WaveMACH filter. Experimental results are presented.     

Multiple circular-harmonic-function correlation filter providing specified response to in-plane rotation

The circular-harmonic-function correlation filter originally proposed by Hsu and Arsenault [Appl. Opt. 21, 4016 (1982)] for in-plane rotation invariance uses only one harmonic, which results in poor discrimination capability of the filter. Various methods to use multiple harmonics were explored previously by different researchers. We present a new method to combine multiple circular harmonics into a single filter that can provide the desired correlation response to in-plane rotation while minimizing the correlation-plane energy. Since multiple harmonics are included, the filter can discriminate well, and since correlation-plane energy is minimized, correlation peaks tend to be sharp. Since the designer can specify the desired in-plane rotation response, a variety of filter behaviors (including complete invariance to input rotations) can be obtained. Underlying theory is discussed, and simulation results are presented.     

Detecting three-dimensional location and shape of noisy distorted three-dimensional objects with ladar trained optimum nonlinear filters

We propose a filtering technique that uses laser radar (ladar) data to detect a target's three-dimensional (3D) coordinates and shape within an input scene. A two-dimensional ladar range image is converted into 3D space, and then the 3D optimum nonlinear filtering technique is used to detect the 3D coordinates of targets (including the target's distance from the sensor). The 3D optimum nonlinear filter is designed to detect distorted targets (i.e., out-of-plane and in-plane rotations and scale changes) and to be noise robust. The nonlinear filter is derived to minimize the mean of the output energy in response to the input scene in the presence of disjoint background noise and additive noise and to maintain a fixed output peak for the members of the true-class target training set. The system is tested with real ladar imagery in the presence of background clutter. The background clutter used in the system evaluation includes false objects that are similar to the true targets. The correlation output of ladar images shows a dominant peak at the target's 3D coordinates.     

Synthetic discriminant function filter employing nonlinear space-domain preprocessing on bandpass-filtered images

Previously [Appl. Opt. 36, p. 9212 (1997)] we examined the performance of the linear and nonlinear preprocessed difference-of-Gaussians filter, and it was shown that this operation results in greater tolerance to in-class variations while maintaining excellent discrimination ability. The introduction of nonlinearity was shown to provide greater robustness to the filter's response to noise and background clutter in the input scene. We incorporate this new operation into the synthesis of a modified synthetic discriminant function filter. The filter is shown to produce sharp peaks, excellent discrimination without the need to include out-of-class objects, and good invariance to out-of-plane rotation over a distortion range of up to 90 degrees . Additionally, the introduction of nonlinearity is shown to provide greater robustness of the filter response to background clutter in the input scene.     

Scale-invariant recognition of three-dimensional objects by use of a quasi-correlator

A method of scale-invariant recognition of three-dimensional (3-D) objects is presented. Several images of the observed scene are recorded under white-light illumination from several different points of view and compressed into a single complex two-dimensional matrix. After filtering with a single scale-invariant filter, the resultant function is then coded into a computer-generated hologram (CGH). When this CGH is coherently illuminated, a correlation space is reconstructed in which light peaks indicate the existence and location of true targets in the tested 3-D scene. The light peaks are detectable for different sizes of the true objects, as long as they are within the invariance range of the filter. Experimental results in a complete electro-optical system are presented, and comparisons with other systems are investigated by use of computer simulation.     

Partial rotation-invariant pattern matching and face recognition with a joint transform correlator

We describe the circular-harmonic (CH) image CH(mr), which is based on CH components for rotationally invariant pattern recognition. CH components of the order m, derived from an image in polar coordinates, are used to form a two-dimensional space together with the radial variable r. Filtering the CH(mr) image leads to a reference image with some rotational invariance. For a narrow-pass filter we have a single CH component with full rotation invariance; for an all-pass filter we have the original image with no rotational invariance; for a low-pass filter we form a reference image containing multiple circular harmonics with partial rotation invariance. Results of numerical simulations and optical experiments with a joint transform correlator are given that illustrate partial-rotation-invariant recognition for human face images.     

Optimum rotation-invariant filter for disjoint-noise scenes

We introduce an optimum matched filter for rotation-invariant pattern recognition in scenes with disjoint noise. The optimum filter maximizes the ratio between the correlation-peak intensity and the correlation-output energy; in order to ensure rotation invariance, we assume the filter to take the form of a circular-harmonic function. The performance of this new filter is tested by computer simulations with a variety of natural and artificial backgrounds. The results obtained from these tests show an excellent performance much better than the classical circular-harmonic function (the filter that maximizes the signal-to-noise ratio while maintaining rotation invariance).     

Wavelet-modified maximum average correlation height filter for rotation invariance that uses chirp encoding in a hybrid digital-optical correlator

We discuss and implement a wavelet-modified maximum average correlation height (MACH) filter for 0 degrees -360 degrees in-plane rotations in a hybrid digital-optical correlator. Use of a wavelet transform improves the performance of the MACH filter by reducing the number of filters that are required to identify a target rotated at any angle between 0 degrees and 360 degrees in-plane rotations and enhances the autocorrelation peak intensity significantly. The output of a hybrid digital-optical correlator contains two autocorrelation peaks and a strong dc. Using a chirp function with the wavelet-modified MACH filter, the correlation signals are focused in three different planes. Thus placing a peak-capturing CCD camera at a particular plane, only one autocorrelation peak is recorded, discarding the strong dc and other autocorrelation peaks. A signal-to-noise ratio has been calculated as a metric of goodness of the proposed wavelet-modified MACH filter.     

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.     

基于边界不变特征的扩展目标识别方法

提出一种新的以边界不变矩作为识别特征,运用BP网络识别扩展目标的方法。首次通过详细的理论证明和实验分析,揭示了离散边界不变矩不再具有严格的比例不变性,而位移和旋转不变性保持相对稳定,并对该不变矩作为识别特征的误差进行了深入分析,给出了正确计算边界不变矩的途径。在此基础上,以该边界不变矩作为识别特征,输入BP网络,采用合理的网络结构,实现对发生位移、旋转和尺度变化的扩展目标的识别。边界不变特征的引入,减少了数据运算量,实验结果表明,识别率达到95.9%。     

Distortion-tolerant three-dimensional object recognition with digital holography

We present a technique to implement three-dimensional (3-D) object recognition based on phase-shift digital holography. We use a nonlinear composite correlation filter to achieve distortion tolerance. We take advantage of the properties of holograms to make the composite filter by using one single hologram. Experiments are presented to illustrate the recognition of a 3-D object in the presence of out-of-plane rotation and longitudinal shift along the z axis.     

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.     

Real filter based on Mellin radial harmonics for scale-invariant pattern recognition

Several theoretical and experimental studies are developed in order to simplify the construction of filters based on Mellin radial harmonics (MRH) for scale-invariant pattern recognition. A real filter based on MRH is designed. The impulse response of the filter is a hermitic function, obtained by a suitable modification of a MRH component. This real filter has the same scale invariance as the conventional complex MRH filters, with the main advantage of its simplicity. Both computer simulations and optical experiments are presented.     

Recognition of unsegmented targets invariant under transformations of intensity

Images taken in noncooperative environments do not always have targets under the same illumination conditions. There is a need for methods to detect targets independently of the illumination. We propose a technique that yields correlation peaks that are invariant under a linear intensity transformation of object intensity. The new locally adaptive contrast-invariant filter accomplishes this by combining three correlations in a nonlinear way. This method is not only intensity invariant but also has good discrimination and resistance to noise. We present simulation results for various intensity transformations with and without random and correlated noise. When the noise is high enough to threaten errors, the method trades off intensity invariance in order to achieve the optimum signal to noise ratio, and the peak to sidelobe ratio in the presence of clutter is always greater than one. In the presence of random disjoint noise, the signal to noise ratio is independent of the target contrast and of the level of the noise.     

Rotation-invariant optical recognition of three-dimensional objects

An automatic method for rotation-invariant three-dimensional (3-D) object recognition is proposed. The method is based on the use of 3-D information contained in the deformed fringe pattern obtained when a grating is projected onto an object's surface. The proposed method was optically implemented by means of a two-cycle joint transform correlator. The rotation invariance is achieved by means of encoding with the fringe pattern a single component of the circular-harmonic expansion derived from the target. Thus the method is invariant for rotations around the line of sight. The whole experimental setup can be constructed with simple equipment. Experimental results show the utility of the proposed method.     

Optical angular mapping for pattern recognition using self-pumped phase conjugation

An optical angular feature mapping of an object for pattern recognition is presented that uses self-pumped phase conjugation followed by a vertical projection. In this method the vertical contour features of an object are most enhanced and vertically projected at each angle as the object is rotated. The vertical enhancement of the contour and the projection are performed optically with self-pumped phase conjugation and a cylindrical lens, respectively. The peaks in the resulting projection at the angle are located and integrated to produce an angular signature function. The signature function is then energy normalized for scale invariance, and pattern recognition is performed by calculation of a cross correlation between two signature functions. The method exhibits invariance to shift, rotation, and variations in scale. Effects of a spatial filter and incident-beam conditions used for phase conjugation are also discussed along with experimental results.     

基于瞬时频率的双曲调频信号距离估计误差分析

双曲调频信号的多普勒不变性可使运动目标匹配滤波输出的能量损失达到最小,从而可对运动目标有很高的检测能力,在运动目标检测与参数估计中具有独特优势,但同时也给目标距离的估计带来一定误差。在双曲调频信号模糊函数、多普勒不变性和匹配滤波特性的基础上,对距离分辨力和测距误差进行了分析,提出了基于瞬时频率的距离误差计算公式,可直接应用于目标距离的补偿以及目标跟踪中量测方程的校正,提高声呐的工作性能。     

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.     

Invariant optical-pattern recognition based on a contour bank

We propose a new optical pattern-recognition method that is invariant to shift, rotation, and scale changes. By means of a zero-order circular-harmonic filter the input object can be transformed into a correlation peak whose outline retains a shape similar to the outline of the input object. After multilevel thresholding and binarization, a bank composed of many contours with different sizes is formed. The invariant recognition can be obtained by a small contour line being taking as a reference and its second-order circular-harmonic component being taken as a filter. In addition to the scale invariance the correlation peak is superior to the classical circular-harmonic filter and the phase-only circular-harmonic filter in terms of peak intensity, half-maximum width, and peak-to-sidelobe ratio.     

基于形状相似度的水下目标识别算法

在水下声纳图像目标检测与识别中,水下目标的识别问题是关键技术之一。基于形状相似度的概念,提出一种新的水下目标形状分类算法。首先对检测到的水下目标阴影区域进行规格化处理,采用改进的形状上下文方法对常见水下目标形状进行分类识别。与传统形状上下文方法相比,改进后的算法具有旋转不变性和模板自适应更新能力。通过对球形、圆柱形和圆台形三类目标的阴影区域进行仿真分类计算可知,该方法具有仿射不变性,分类准确率较高。