Recognition and classification of three-dimensional phase objects by digital Fresnel holography

We demonstrate the validity of wavelet-based processing for recognition and classification of three-dimensional phase objects. One Fresnel digital hologram of each of the three-dimensional (3-D) phase objects to be classified is recorded. The electronic holograms are processed digitally to permit 3-D object information to be retrieved as two-dimensional digital complex images. We use a Mexican-hat wavelet- matched filter (WMF) to enhance the correlation peak and discriminate between the objects. The WMF performs a wavelet transform (WT) to enhance the significant features of the images and the correlation of the WT coefficients thus obtained. We compare the feasibility of a WMF-based object classifier with the matched-filter-based classifier to classify our four 3-D phase objects in a 3-D scene into true or false classes with minimal error.     

Optical image recognition of three-dimensional objects

A three-dimensional (3-D) optical image-recognition technique is proposed and studied. The proposed technique is based on two-pupil optical heterodyne scanning and is capable of performing 3-D image recognition. A hologram of the 3-D reference object is first created and then is used to modulate spatially one of the pupils of the optical system; the other pupil is a point source. A 3-D target object to be recognized is then scanned in two dimensions by optical beams modulated by the two pupils. The result of the two-dimensional scan pattern effectively displays the correlation of the holographic information of the 3-D reference object and that of the 3-D target object. A strong correlation peak results if the two pieces of the holographic information are matched. We analyze the proposed technique and thereby lay a theoretical foundation for optical implementations of the idea. Finally, computer simulations are performed to verify the proposed idea.     

Object recognition using three-dimensional optical quasi-correlation

A novel method of three-dimensional (3-D) object recognition is proposed. Several projections of a 3-D target are recorded under white-light illumination and fused into a single complex two-dimensional function. After proper filtering, the resulting function is coded into a computer-generated hologram. When this hologram is coherently illuminated, a correlation space is reconstructed such that light peaks indicate the existence and locations of true targets in the observed 3-D scene. Experimental results and comparisons with results of another 3-D object recognition technique are presented.     

Two-channel self-focusing matched filter

Abstract

A two-channel self-focusing matched filter produced by a computer generated hologram is proposed in this paper. The filter function is synthesized for two different objects to be recognized. The composite filter function consists of two terms, one of which is formed by adding a negative quadratic phase factor into the conjugate Fourier spectrum of one object, and another, which is adding a positive quadratic phase factor into the Fourier spectrum of another object. The composite function is then encoded into a computer-generated hologram to fabricate a matched filter. When performing pattern recognition, the filter will produce two correlation signals of two objects in the first positive order and the first negative order of diffraction, respectively. The diffracted light can be self-focused in the plane at a designed distance behind the filter. The optical experiments have shown the capability of the filter for simultaneously recognizing two different objects.     

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.     

基于结构照明和BP神经网络的三维物体识别

提出一种具有旋转不变性的三维物体识别的新方法,该方法通过结构光照明的方法,使物体的高度分布以变形条纹的形式编码于二维强度图中,由于条纹图包含有物体的高度分布信息,因此对条纹的相关识别具有本征三维识别的特点.旋转不变性是通过BP神经网络实现的.计算机模拟结果表明,用二维强度像的基频分量做训练样本设计BP神经网络,选择训练样本和隐藏层神经元的数目,基于结构光编码的BP神经网络对三维物体具有良好的旋转不变识别效果.     

Three-dimensional pattern recognition with a single two-dimensional synthetic reference function

A novel, to our knowledge, method of distortion-invariant three-dimensional (3-D) pattern recognition is proposed. A single two-dimensional synthetic discriminant function is employed as a reference function in the 3-D correlator. Thus the proposed system is able to identify and locate any true-class object in the 3-D scene. Preliminary simulation and experimental results are presented.     

Three-dimensional object recognition by fourier transform profilometry

An automatic method for three-dimensional (3-D) shape recognition is proposed. It combines the Fourier transform profilometry technique with a real-time recognition setup such as the joint transform correlator (JTC). A grating is projected onto the object surface resulting in a distorted grating pattern. Since this pattern carries information about the depth and the shape of the object, their comparison provides a method for recognizing 3-D objects in real time. A two-cycle JTC is used for this purpose. Experimental results demonstrate the theory and show the utility of the new proposed method.     

Shift- and scale-invariant recognition of contour objects with logarithmic radial harmonic filters

The phase-only logarithmic radial harmonic (LRH) filter has been shown to be suitable for scale-invariant block object recognition. However, an important set of objects is the collection of contour functions that results from a digital edge extraction of the original block objects. These contour functions have a constant width that is independent of the scale of the original object. Therefore, since the energy of the contour objects decreases more slowly with the scale factor than does the energy of the block objects, the phase-only LRH filter has difficulties in the recognition tasks when these contour objects are used. We propose a modified LRH filter that permits the realization of a shift- and scale-invariant optical recognition of contour objects. The modified LRH filter is a complex filter that compensates the energy variation resulting from the scaling of contour objects. Optical results validate the theory and show the utility of the newly proposed method.     

Performance assessment of the modified-hybrid optical neural network filter

We present in detail the recorded results of the modified-hybrid optical neural network (M-HONN) filter during a full series of tests to examine its robustness and overall performance for object recognition tasks. We test the M-HONN filter for its detectability and peak sharpness with within-class distortion of the input object, its discrimination ability between an in-class and out-of-class object, and its performance with cluttered images of the true-class object. The M-HONN filter is found to exhibit good detectability, an ability to maintain its correlation-peak sharpness throughout the recorded tests, good discrimination ability, and an ability to detect the true-class object within cluttered input images. Additionally we observe the M-HONN filter's performance within the tests in comparison with the constrained-hybrid optical neural network filter for the first three series of tests and the synthetic discriminant function-maximum average correlation height filter for the fourth set of tests.     

Pose estimation for autonomous grasping with a robotic arm system

A robust and accurate method for estimating the 3-D pose of a planar rigid object is presented. This article demonstrates that 3-D pose estimation becomes feasible by using the 2-D tracking points on an object of scale-invariant feature transform (SIFT) and 3-D point cloud detected by stereo vision on an object, assuming that a 3-D geometric model of an object is known a priori. The roll and pitch angles of an object are estimated by the normal vector of approximate plane of 3-D point cloud on an object and the yaw angle is estimated by 2-D tracking point on an object of SIFT. Accurate object detection and localization in the camera coordinate system is crucial for grasping. In the motion planning, the bidirectional rapidly exploring random tree algorithm is used to search for a valid path for efficient grasping. Our robot arm can robustly and autonomously grasp a randomly rotative rigid object detected by SIFT in 3-D space. We have realized a grasping scenario with a dexterous arm (ADAM) such that an object in front of ADAM can be grasped. This demonstration shows how the proposed components build a dexterous and robust system integrating object detection, pose estimation, and motion planning.     

基于调制度位相展开的三维面形测量及其可视化

在三维面形测量中，对复杂面形的处理位相展开仍然是十分困难的，所以极大地限制着测量精度和测量系统的自动化程度。通过多次实践，提出一种在数字加权滤波和调制度分析基础上形成可靠性控制模板，并按调制度进行位相展开的方法。实现了频谱面的数字加权滤波，进一步对条纹质量进行分类排序。在不影响位相精度的情况下，保证了位相展开的可靠性，避开了复杂的极点连接工作，节省了处理时间。最后笔者给出了一个实验，实验结果证明了方法的可行性和有效性。     

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.     

A unit cell approach of finite element calculation of ballistic impact damage of 3-D orthogonal woven composite

This paper presents ballistic impact damages of 3-D orthogonal woven composite in finite element analysis (FEA) and experimental. A unit-cell model of the 3-D woven composite was developed to define the material behavior and failure evolution. A user-defined subroutine VUAMT was compiled and connected with commercial available FEA code ABAQUS/Explicit to calculate the ballistic penetration. Ballistic impact tests were conducted to investigate impact damage of 3-D kevlar/glass hybrid woven composite. Residual velocities of conically-cylindrical steel projectiles (Type 56 in China Military Standard) and impact damage of the composite targets after ballistic perforation were compared both in theoretical and experimental. The reasonable agreements between FEA results and experimental results prove the validity of the unit-cell model in ballistic limit prediction of the 3-D woven composite. We believe such an effort could be extended to bulletproof armor design with the 3-D woven composite.     

Computer-generated holograms of three-dimensional realistic objects recorded without wave interference

We propose a method of synthesizing computer-generated holograms of real-life three-dimensional (3-D) objects. An ordinary digital camera illuminated by incoherent white light records several projections of the 3-D object from different points of view. The recorded data are numerically processed to yield a two-dimensional complex function, which is then encoded as a computer-generated hologram. When this hologram is illuminated by a plane wave, a 3-D real image of the object is reconstructed.     

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.     

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.     

Large-scale three-dimensional object measurement: a practical coordinate mapping and image data-patching method

In a practical three-dimensional (3-D) sensing system, the measurement of a large-scale object cannot be completed in only one operation. A relieflike object is generally divided into several subregions, an optical sensor positioned at each of these locations, and the shape of the whole object obtained by patching together all the 3-D data of the subregions. It is important to have accurate 3-D coordinates (x, y, z) for each subregion. We propose a new phase-to-height mapping algorithm and an accurate lateral coordinate calibration method with which to obtain the 3-D coordinates. After all the subregions are measured, it is necessary to transform the local coordinates into global world coordinates; here we present a new image data-patching method based on a flood algorithm. This method provides the optimal path along which to patch all the subregions into the shape of the entire object. We have measured and successfully patched a large sandy pool (9 m x 5 m), and the reliability and feasibility of our method have been demonstrated by experiment.     

In-plane Compressive Behaviors of 3-D Textile Composites at Various Strain Rates

The in-plane compressive behaviors of 3-D textile composites, which including 3-D woven composite, multi-axial multi-layer warp knitted (MMWK) composite and 3-D braided composite, were studied at quasi-static and high strain rate compression loading. The compression behaviors at high strain rates (600∼2,500/s) were tested with split Hopkinson pressure bar (SHPB). The quasi-static compressive tests were conducted on a MTS 810.23 tester and compared with the results at high strain rates. The comparisons indicate that the compression stiffness, failure stress and failure strain for the three kinds of 3-D textile composites are sensitive to strain rate. The MMWK composite has higher failure stress than the 3-D woven composite and 3-D braided composite at the same strain rate; however, the failure strain of the 3-D braided composites is higher than that of the 3-D woven composite and 3-D knitted composite at quasi-static compression because of the quasi-isotropic structure feature in the 3-D braided composite. The compressive failure modes of the 3-D woven composite, MMWK composite and 3-D braided composite are totally different because of the different preform structure.     

Spectral optimized asymmetric segmented phase-only correlation filter

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.