Projection-invariant pattern recognition with logarithmic harmonic function and wavelet transform

A logarithmic harmonic filter can detect objects at different projection angles. The Mexican-hat wavelet function can extract edges of equal width for objects, regardless of their sizes. Hence incorporating wavelet filtering in the logarithmic harmonic filter can improve its performance. The theory is presented together with computer simulation. Finally, an experiment using a joint transform correlator is presented to verify the capability of the proposed filter.     

Multidistortion-invariant image recognition with radial harmonic Fourier moments

We propose radial harmonic Fourier moments, which are shifting, scaling, rotation, and intensity invariant. Compared with Chebyshev-Fourier moments, the new moments have superior performance near the origin and better ability to describe small images in terms of image-reconstruction errors and noise sensitivity. A multidistortion-invariant pattern-recognition experiment was performed with radial harmonic Fourier moments.     

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.     

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.     

Extended scale-invariant pattern recognition with white-light illumination

A previous method of obtaining scale-invariance detection with white-light illumination has been improved on. We were able to detect different scaled versions of the target up to a magnification factor equal to 2. We simultaneously detected several versions in the same scene, because each scale factor is codified in a different wavelength. Experimental results demonstrate the proposed technique and show the utility of the method.     

Coordinate-transformed filter for shift-invariant and scale-invariant pattern recognition

A variable radial coordinate transformation of the phase-only filter (POF) that is dependent on the energy's angular distribution of the target spectrum is used to perform shift- and scale-invariant pattern recognition. The POF of a basic size target and the cumulative energy of its angular distribution are calculated. The filter function is then transformed by means of stretching along the radial coordinate so that the same energy contribution to the correlation peak is provided for any size target. The maximum ratio for recognizing scaled objects is 1:1.5. Computer simulations and optical experiments showing the performances of the filter are presented.     

Design of correlation filters for recognition of linearly distorted objects in linearly degraded scenes

Generalized correlation filters are proposed to improve recognition of a linearly distorted object embedded in a nonoverlapping background when the input scene is degraded with a linear system and additive noise. Several performance criteria defined for the nonoverlapping signal model are used for the design of filters. The derived filters take into account information about an object to be recognized, disjoint background, noise, and linear degradations of the target and the input scene. Computer simulation results obtained with the proposed filters are discussed and compared with those of various correlation filters in terms of discrimination capability, location errors, and tolerance to input noise.     

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.     

Continuous radial superresolution filters with large focal depth

A new set of continuous superresolution filters is proposed which exhibits a radial superresolution performance with an extended depth of focus in an optical system by properly choosing the design parameters. Numerical simulation results of the performance parameters of the superresolution gain, the radial central core size, the Strehl ratio, the side-lobe factor and the depth of focus with different design parameters for the optimized patterns are displayed. We also give a design example for this kind of filter characterized by a birefringent element inserted between two parallel polarizers. This kind of filter would be useful in fields such as optical data storage systems.     

Position-invariant, rotation-invariant, and scale-invariant process for binary image recognition

A novel recognition process is presented that is invariant under position, rotation, and scale changes. The recognition process is based on the Fang-H?usler transform [Appl. Opt. 29, 704 (1990)] and is applied to the autoconvolved image, rather than to the image itself. This makes the recognition process sensitive not only to the image histogram but also to its detailed pattern, resulting in a more reliable process that is also applicable to binary images. The proposed recognition process is demonstrated, by use of a fast algorithm, on several types of binary images with a real transform kernel, which contains amplitude, as well as phase, information. Good recognition is achieved for both synthetic and scanned images. In addition, it is shown that the Fang-Hausler transform is also invariant under a general affine transformation of the spatial coordinates.     

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.     

Polynomial expansion for shift- and one- or two-dimensional scale-invariant pattern recognition

A polynomial expansion is suggested for achieving optical invariant pattern recognition. The expansion results in a real function and thus is theoretically able to be implemented under both coherent and spatially incoherent illumination. One obtains the expansion after applying the Gram-Schmidt algorithm on the Laurent's series in order to achieve orthonormality. The initial Laurent term with which we apply the Gram-Schmidt procedure is chosen according to the desired expansion order. The use of the polynomial expansion is demonstrated for shift- and one-dimensional scale-invariant pattern recognition as well as for shift-and two-dimensional scale-invariant recognition.     

Volume-holographic filters for rotational sensing of three-dimensional objects

A highly precise rotational filter based on a volume-holographic optical element is proposed and demonstrated. We present a clear theoretical calculation of the rotation sensitivity of the volume-holographic filter used to sense the rotation of a spherical ground glass object. By introducing the longitudinal displacement of the scattering point across the sphere, the sensitivity of the filter is greatly improved to 350 times that of a general case for a planar ground glass.     

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 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.     

Fourier-Bessel harmonic expansions for tomography of partially opaque objects

Tomographic reconstruction from a limited amount of projection data of fields with embedded opaque objects can result in streaks and other artifacts in the reconstructed image. These artifacts result from the use of local-basis-function expansions to represent the image. I demonstrate that reconstructions by circular-harmonic expansions are largely free of these artifacts. A Fourier-Bessel expansion on a circular domain is used as the reconstruction basis; this expansion is used to compare circular-harmonic reconstructions with square-pixel reconstructions to determine qualitative differences between the local bases and the circular harmonics. Computational issues are also discussed.     

Lossless parameterisation of image contour for shape recognition

A parameterised contour that is invariant against affine transformations is a convenient substitution of the object image for shape recognition. Generally, parameterisation needs several representative signals of the image contour to fit the affine transformation model. When the representative signals fail to carry the contour information thoroughly, information loss occurs in the resulting parameterised contour, and so the accuracy of shape recognition may deteriorate. Synthesised feature signals are shown, which represent that an image contour without information loss can be extracted with partial Fourier synthesis or partial cosine synthesis. Lossless parameterisation of the image contour is obtained by substituting the synthesised feature signals into the affine invariant function. Experimental results verify its representative, affine invariance and recognition rate in shape recognition. The results are compared with those by partial wavelet synthesis, which has insignificant information loss.     

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.     

Robotic tactile recognition of pseudorandom encoded objects

This paper discusses an original model-based method for blind robotic tactile recognition of three-dimensional objects. Conveniently shaped geometric symbols representing terms of a pseudorandom array (PRA) are embossed on object surfaces. Symbols recovered by tactile probing are recognized using a neural network and then clustered in a PRA window that contains enough information to fully identify the absolute coordinates of the recovered window within the encoding PRA. By knowing how different object models were mapped to the PRA, it is possible to unambiguously identify the object face and the exact position of the recovered symbols on the face.