The depth of field of optical systems can be extended with a phase mask placed in the pupil. We propose to design this
phase mask through a heuristic numerical optimization. We describe two different criteria to illustrate the technique. We
present the obtained phase profiles and we show that they have good properties regarding the extension of the depth of
Proc. SPIE. 6736, Unmanned/Unattended Sensors and Sensor Networks IV
KEYWORDS: Optical transfer functions, Point spread functions, Imaging systems, Spatial frequencies, Sensors, Deconvolution, Modulation transfer functions, Imaging arrays, Integral imaging, 3D image processing
Integral imaging systems permit to capture and reproduce three-dimensional scenes. However they usually suffer from a
limited depth of field and/or limited depth of focus that severely reduces the depth range that can be used in practice. In
order for such a system to be able to capture and reproduce large-depth scenes without any adjustment, we propose to
include in the integral imaging system an array of phase masks in order to increase the depth of focus of the final three-dimensional
images. We consider both the pickup and the reconstruction stages.
In this paper we concern ourselves with the subject of superresolution in Digital Holography (DH), i.e. increasing the resolution of DH system beyond its limit. The limiting factor regarding resolution in a DH system is the pixel size, which is equal to the smallest resolvable unit. By careful superposition of different digital holograms captured of the same 3-D object, we attempt to increase the resolution of the reconstructed image and equivalently to increase the range of angles of reconstruction. This is accomplished by rotating the input object wavefield either by rotation of the object (it is 2-D) or by rotation of a mirror that is placed between the object and the CCD. Rotating the input wavefield shifts the wavefield in the hologram plane in space and spatial frequency. Therefore, those parts of the hologram field that contained energy at too great an angle for recording and were therefore arranged to be adjacent to and not on the CCD will be shifted in space onto the CCD face and will also be shifted to a recordable angle. We outline a sub-pixel correlation technique to stitch the consecutive holograms together in both the space and spatial frequency domains. Multiple captures enable us to record a DH of large resolution and angle of reconstruction. Storage and reconstruction of the stitched hologram is also discussed and experimental results are given. The method may be applied with any existing form of DH. We use the Wigner Distribution Function to qualify and quantify the method.
We review two different techniques for visualization and processing of three-dimensional (3D) objects based on passive and active optical sensing. First, we describe the basis of a passive-sensing technique based on integral imaging. Also, we show that it is possible to improve the depth of field of this method by using amplitude-modulated microlens arrays. Second, we describe an active-sensing technique based on digital holography. Finally, we apply both techniques to de-velop 3D image processing applications. In particular, we design two different 3D pattern recognition techniques. Both of them are based in storing the 3D data in two-dimensional (2D) form. In this way, it is possible to recognize 3D objects by performing 2D correlations or applying neural network techniques. Experimental results are presented.
This paper analyzes the security of amplitude encoding for double random phase encryption. We describe several types of attack. The system is found to be resistant to brute-force attacks but vulnerable to chosen and known plaintext attacks.
We describe several methods for optoelectronic processing of 3D images based in digital holography. In all cases, phase-shift digital holography is used to record the complex amplitude distribution associated to the diffraction field generated by 3D objects illuminated with coherent light. First, we review a technique to encrypt a 3D image by using digital holography. Encryption is performed by using random phase codes as key functions. In this way, it is possible to send secure 3D information through conventional digital communication lines. In our approach, decryption is carried out digitally. Next we describe both, digital and optical reconstruction of 3D images starting from digital holograms. Finally, we show how to perform 3D pattern recognition with high discrimination based in the above techniques. Experimental results are presented.
A few fundamental symbols are common to many mesoamerican cultures. However the shape of each symbol greatly varies depending on the considered culture and period. In this paper, we present a technique to recognize one of these symbols in spite of its shape variability. We base the recognition on a small set of symmetry and morphology rules. We describe the rules and their computational implementation, as well as practical recognition results.
Two techniques for recognizing three-dimensional(3D) objects based on passive and active optical sensing followed by numerical correlation are presented. One technique uses passive sensing of 3D object based on integral imaging and the other technique uses active sensing based on digital holography. In both techniques, the 3D data is stored in two-dimensional(2D) form as digital format and then the detected 3D information is used for recognizing 3D object based on 2D correlation techniques or neural network. Experimental results in both techniques are presented.
We use an array of microlenses to create multiple perspectives of a 3D scene (integral image). These perspectives are captured by a camera and digitally processed to extract the distance information from the differences of parallax. We present a technique to estimate the distance of objects and to digitally reconstruct the 3D scene in a computer. We show how this digital reconstruction can be used to visualize or recognize objects in a 3D scene.
Integral images contain three-dimensional (3D) information about a scene. We extract this information using a stereo-matching algorithm and we digitally reconstruct the 3D scene in a computer. We then use the reconstructed scene to perform 3D recognition by means of a nonlinear 3D correlation. We demonstrate the recognition and localization of objects in a 3D scene. We also compare discrimination of 2D and 3D correlations.
We propose to use integral images to reconstruct and recognize three-dimensional (3D) scenes in a computer. A stereo-matching algorithm is applied to integral images in order to extract the depth information. This information is used to digitally reconstruct the 3D scenes. A numerical 3D correlation is then computed between various reconstructed scenes. We demonstrate the reconstruction and correlation results from experimental integral images. We propose to use a nonlinear correlation for better discrimination and we present the successful recognition and 3D localization of an object in a 3D scene. We finally compare the discrimination of two- and three-dimensional correlations.
Integral images contain multiple views of a scene obtained from slightly different points of view. They therefore include three-dimensional (3D) information - including depth - about the scenes they represent. In this paper, we propose to use this depth information contained in integral images in order to recognize 3D objects. The integral images are first used to estimate the longitudinal distances of the objects composing the 3D scene. Using this information, a 3D model of the scene is reconstructed in the computer. These models are then used to compute digital 3D correlations between various scenes and objects. For a better discrimination we use a nonlinear 3D correlation. We present experimental results for digital 3D reconstruction of real 3D scenes containing several objects at various distances. With these experimental data, we demonstrate the recognition and 3D localization of objects through nonlinear correlation. We investigate the effect of the nonlinearity strength in the correlation. We finally present experiments to show that the three-dimensional correlation is more discriminant than the two-dimensional correlation.
Cameras provide only bi-dimensional views of three-dimensional objects. These views are projections that change depending on the spatial orientation or pose of the object. In this paper we propose a technique to estimate the pose of a 3D object knowing only a 2D picture of it. The proposed technique explores both the linear and the nonlinear composite correlation filters in a combination with a neural network. We present results in estimating two orientations: in-plane and out-of-plane rotations within an 8 degree square range.
We create digital holograms of real-world objects using a process called phase-shift digital holography. This system has been used as the basis for a three-dimensional object reconstruction and recognition technique. We present the results of applying lossless and lossy data compression to individual holographic frames. The lossy techniques are based on quantization and amplitude equalization. We also present a novel technique that uses only phase information of the digital hologram for the real-time optical reconstruction of three-dimensional objects.
In this paper, we propose a method based on the Maximum Likelihood theory for removing the speckle pattern that plagues coherent images. The hypothesis on the speckle effect imply that the maximum likelihood criterion uses only intensity information. The model of the image is based on a lattice of knots corresponding to the vertices of triangles where the gray level of each pixel inside a cell is provided by linear interpolation.
The two-dimensional view, obtained with a camera, of a three-dimensional (3-D) object varies with the 3-D orientation of this object, complicating the recognition task. In this work we address the problem of estimating the pose of a 3-D object knowing only a 2-D projection. The proposed technique is based on a combination of synthetic-discriminant-function filters and neural networks. We succeed in estimating two orientations: in-plane and out-of-plane rotations within a 8 degree square range.
We present the initial results of a novel technique that uses only phase information from a digital hologram for the reconstruction of three-dimensional (3D) objects. Our holograms are created using phase-shift digital holography. Perspectives of the 3D object are usually reconstructed numerically on a computer. For large holograms this can be a computationally intensive task. We believe that the proposed reconstruction technique is promising for 3D display because the phase-encoded digital holograms admit optical, and therefore realtime, reconstructions that use commercially available display devices such as liquid crystal spatial light modulators. Numerical evaluation of the reconstructed 3D object and an experimental demonstration are presented.
We present a method to recognize three-dimensional objects from phase-shift digital holograms. The holograms are used to reconstruct various views of the objects. These views are combined to create nonlinear composite filters in order to achieve distortion invariance. We present experiments to illustrate the recognition of a 3D object in the presence of out-of-plane rotation and longitudinal shift along the z-axis.
We describe a number of new optoelectronic approaches to three-dimensional (3D) image recognition. In all the cases, digital holography is used to record the complex amplitude distribution of Fresnel diffraction patterns generated by 3D scenes illuminated with coherent light. This complex information is compared with that from a similar digital hologram of a 3D reference object using correlation methods. Pattern recognition techniques that are shift-variant or shift-invariant along the optical axis are described. In the latter case it is possible to detect the 3D position of the reference in the input scene with high accuracy. We use also a nonlinear composite correlation filter to achieve distortion tolerance. Experiments are presented to illustrate the recognition of a 3D object in the presence of out-of-plane rotation.
In this paper we present the results of applying data compression to a three-dimensional object recognition technique based on phase-shift digital holography. Industry-standard lossless data compression algorithms were first applied. Next, lossy techniques based on subsampling, discrete cosine transformation, and discrete Fourier transformation were examined. We used normalized cross-correlation in the object plane as our performance metric. For each hologram tested, we found that as many as 90% of the cosine and Fourier components could be removed, without significant loss in correlation performance.
In this paper we describe the realization and the operation of a high capacity optoelectronic neural network implementing a classification of vectors through a Kohonen topological map. The setup uses volume holographic interconnects inside a photorefractive crystal to implement the neurons. We show that the system work and is able to classify several tens of vectors.