Three-dimensional joint transform correlator

Three-dimensional joint transform correlator cryptosystem

We introduce for the first time, to the best of our knowledge, a three-dimensional experimental joint transform correlator (JTC) cryptosystem allowing the encryption of information for any 3D object, and as an additional novel feature, a second 3D object plays the role of the encoding key. While the JTC architecture is normally used to process 2D data, in this work, we envisage a technique that allows the use of this architecture to protect 3D data. The encrypted object information is contained in the joint power spectrum. We register the key object as a digital off-axis Fourier hologram. The encryption procedure is done optically, while the decryption is carried out by means of a virtual optical system, allowing for flexible implementation of the proposal. We present experimental results to demonstrate the validity and feasibility of the method.

Computational integral-imaging reconstruction-based 3-D volumetric target object recognition by using a 3-D reference object

In this paper, we propose a novel computational integral-imaging reconstruction (CIIR)-based three-dimensional (3-D) image correlator system for the recognition of 3-D volumetric objects by employing a 3-D reference object. That is, a number of plane object images (POIs) computationally reconstructed from the 3-D reference object are used for the 3-D volumetric target recognition. In other words, simultaneous 3-D image correlations between two sets of target and reference POIs, which are depth-dependently reconstructed by using the CIIR method, are performed for effective recognition of 3-D volumetric objects in the proposed system. Successful experiments with this CIIR-based 3-D image correlator confirmed the feasibility of the proposed method.

Review of three-dimensional holographic imaging by multiple-viewpoint-projection based methods

Methods of generating multiple viewpoint projection holograms of three-dimensional (3-D) realistic objects illuminated by incoherent white light are reviewed in this paper. Using these methods, it is possible to obtain holograms with a simple digital camera, operating in regular light conditions. Thus, most disadvantages characterizing conventional digital holography, namely the need for a powerful, highly coherent laser and extreme stability of the optical system, are avoided. The proposed holographic processes are composed of two stages. In the first stage, regular intensity-based images of the 3-D scene are captured from multiple points of view by a simple digital camera. In the second stage, the acquired projections are digitally processed to yield the complex digital hologram of the 3-D scene, where no interference is involved in the process. For highly reflecting 3-D objects, the resulting hologram is equivalent to an optical hologram of the objects recorded from the central point of view. We first review various methods to acquire the multiple viewpoint projections. These include the use of a microlens array and a macrolens array, as well as digitally generated projections that are not acquired optically. Next, we show how to digitally process the acquired projections to Fourier, Fresnel, and image holograms. Additionally, to obtain certain advantages over the known types of holograms, the proposed hybrid optical-digital process can yield novel types of holograms such as the modified Fresnel hologram and the protected correlation hologram. The prospective goal of these methods is to facilitate the design of a simple and portable digital holographic camera that can be useful for a variety of practical applications, including 3-D video acquisition and various types of biomedical imaging. We review several of these applications to signify the advantages of multiple viewpoint projection holography.

Three-dimensional object recognition using a quasi-correlator invariant to imaging distances

We present a new method for performing electro-optical three-dimensional (3-D) object recognition under incoherent white-light illumination. Perspective projections of the 3-D scene are acquired from multiple points of view and then processed into a single complex two-dimensional modified Fresnel hologram of the scene. This hologram is processed with a single filter which is matched to a single object, so that all identical objects in the scene yield similar correlation peaks in the 3-D space with almost no dependency on the distances of the objects from the acquisition plane. The new method is demonstrated by experiments.

Phase Fourier vector model for scale invariant three-dimensional image detection

A scale invariant 3D object detection method based on phase Fourier transform (PhFT) is addressed. Three-dimensionality is expressed in terms of range images. The PhFT of a range image gives information about the orientations of the surfaces in the 3D object. When the object is scaled, the PhFT becomes a distribution multiplied by a constant factor which is related to the scale factor. Then 3D scale invariant detection can be solved as illumination invariant detection process. Several correlation operations based on vector space representation are applied. Results show the tolerance of detection method to scale besides discrimination against false objects.

Three-dimensional distortion-tolerant object recognition using integral imaging

This paper addresses 3D distortion-tolerant object recognition using Integral Imaging (II). II is one of the techniques considered for 3D image recording and display. Multiple elemental images are captured to record different but continuous viewing zones of 3D scenes. We develop a distortion-tolerant 3D object recognition by using the II capture system. We adopt Principal Component Analysis (PCA) and Fisher Linear Discriminant (FLD) classifier followed by the nearest neighbor decision rule and the statistical distance metric. Performance is analyzed in terms of probability of correct decision, Root Mean Square Error (RMSE) between raw input vectors and reconstructed vectors from the PCA subspace and the PCA-FLD cost function. Decision strategies are compared by the varying number of training data.

Comparison of passive ranging integral imaging and active imaging digital holography for three-dimensional object recognition

We present an overview of three-dimensional (3D) object recognition techniques that use active sensing by interferometric imaging (digital holography) and passive sensing by integral imaging. We describe how each technique can be used to retrieve the depth information of a 3D scene and how this information can then be used for 3D object recognition. We explore various algorithms for 3D recognition such as nonlinear correlation and target distortion tolerance. We also provide a comparison of the advantages and disadvantages of the two techniques.

Three-dimensional object feature extraction and classification with computational holographic imaging

We address three-dimensional (3D) object classification with computational holographic imaging. A 3D object can be reconstructed at different planes by use of a single hologram. We apply principal component and Fisher linear discriminant analyses based on Gabor-wavelet feature vectors to classify 3D objects measured by digital interferometry. Experimental and simulation results are presented for regional filtering concentrated at specific positions and for overall grid filtering. The proposed technique substantially reduces the dimensionality of the 3D classification problem. To the best of our knowledge, this is the first report on the use of the proposed technique for 3D object classification.

Recognition of polychromatic three-dimensional objects

We propose to use optical multichannel correlation in various chromatic systems to obtain a setup for recognition of polychromatic three-dimensional (3-D) objects based on Fourier-transform profilometry. Because red-green-blue color components are not able to split the luminance information of objects in a defined component, when the 3-D objects are brighter than the reference objects the correlation result gives false alarms. We demonstrate that it is possible to use different color spaces that can split luminance from chromatic information to yield adequate recognition of polychromatic 3-D objects. We show experimental results that prove the utility of the proposed method.

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.

Digital three-dimensional image correlation by use of computer-reconstructed integral imaging

We use integral images of a three-dimensional (3D) scene to estimate the longitudinal depth of multiple objects present in the scene. With this information, we digitally reconstruct the objects in three dimensions and compute 3D correlations of input objects. We investigate the use of nonlinear techniques for 3D correlations. We present experimental results for 3D reconstruction and correlation of 3D objects. We demonstrate that it is possible to perform 3D segmentation of 3D objects in a scene. We finally present experiments to demonstrate that the 3D correlation is more discriminant than the two-dimensional correlation.

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.

Compression of digital holograms for three-dimensional object reconstruction and recognition

We present the results of applying lossless and lossy data compression to a three-dimensional object reconstruction and recognition technique based on phase-shift digital holography. We find that the best lossless (Lempel-Ziv, Lempel-Ziv-Welch, Huffman, Burrows-Wheeler) compression rates can be expected when the digital hologram is stored in an intermediate coding of separate data streams for real and imaginary components. The lossy techniques are based on subsampling, quantization, and discrete Fourier transformation. For various degrees of speckle reduction, we quantify the number of Fourier coefficients that can be removed from the hologram domain, and the lowest level of quantization achievable, without incurring significant loss in correlation performance or significant error in the reconstructed object domain.

Fractional profilometry correlator for three-dimensional object recognition

A novel method for three-dimensional shape recognition is proposed. It combines the Fourier-transform-profilometry technique with a two-dimensional fractional correlation algorithm. A grating is projected onto the object surface, resulting in a distorted grating pattern that carries information about the height and the shape of the object. Three-dimensional objects are recognized by a fractional correlator by use of the transformed complex amplitude. An optoelectronic hybrid implementation is also suggested.

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.

Three-dimensional object recognition by use of a photorefractive volume holographic processor

We present a photorefractive volume holographic processor for recognition of three-dimensional (3D) objects. The templates are recorded by use of a volume hologram in a photorefractive LiNbO(3):Fe crystal located at the Fresnel diffraction region and correlated in real time with a 3D object illuminated by coherent light. Experimental results for recognition of 3D objects are presented and discussed. To the best of our knowledge, this is the first time a photorefractive volume holographic technique for 3D object recognition has been reported.

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.

Three-dimensional optical correlator with general complex filters

A new type of electro-optical three-dimensional (3-D) correlator is proposed and demonstrated. A 3-D object scene, observed by multiple cameras from several points of view, is correlated with a 3-D complex computer-generated function. This correlator is a hybridization of the joint transform and the VanderLugt correlators, and, as such, it allows correlations to be made between 3-D real-world objects and 3-D general complex functions. Experimental results are presented.

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.

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.