Input Object Research Articles

Photon-counting passive 3D image sensing for reconstruction and recognition of partially occluded objects

In this paper, we discuss the reconstruction and the recognition of partially occluded objects using photon counting integral imaging (II). Irradiance scenes are numerically reconstructed for the reference target in three-dimensional (3D) space. Photon counting scenes are estimated for unknown input objects using maximum likelihood estimation (MLE) of Poisson parameter. We propose nonlinear matched filtering in 3D space to recognize partially occluded targets. The recognition performance is substantially improved from the nonlinear matched filtering of elemental images without 3D reconstruction. The discrimination capability is analyzed in terms of Fisher ratio (FR) and receiver operating characteristic (ROC) curves.

Improved performance of scale-invariant pattern recognition using a combined Mellin radial harmonic function and wavelet transform

By using a combination of the Mellin radial harmonic function and the Mexican-hat wavelet transform, scale- and shift-invariant pattern recognition is reported. Without preprocessing of the input object, this filter is capable of identifying over a wide allowable scale range of 0.25 to 1. The correlation peaks are sharp, and the peak intensity is fairly uniform, with a variance below 30%. Computer simulation is adopted to investigate the performance of the filter. Experimental results implemented using a photorefractive joint-transform correlator, including those obtained under white noise, are also presented.

Random Projections Imaging With Extended Space-Bandwidth Product

We propose a novel approach to imaging that is not based on traditional optical imaging architecture. With the new approach, the image is reconstructed and visualized from random projections of the input object. The random projections are implemented within a single exposure by using a random phase mask which can be placed on a lens. For objects that have sparse representation in some known domain (e.g., Fourier or wavelet), the novel imaging systems have larger effective space - bandwidth product than conventional imaging systems. This implies, for example, that more object pixels may be reconstructed and visualized than the number of pixels of the image sensor. We present simulation results on the utility of the new approach. The proposed approach can have broad applications in efficient imaging capture, visualization, and display given ever increasing demands for larger and higher resolution images, faster image communications, and multidimensional image communications such as 3-D TV and display.

Removing degeneracy may require unbounded dimension increase

Abstract Many geometric algorithms are formulated for input objects in general position; sometimes this is for convenience and simplicity, and sometimes it is essential for the algorithm to work at all. For arbitrary inputs this requires removing degeneracies, which has usually been solved by relatively complicated and computationally demanding perturbation methods. The result of this paper can be regarded as an indication that the problem of removing degeneracies has no simple “abstract” solution. We consider LP-type problems, a successful axiomatic framework for optimization problems capturing, e.g., linear programming and the smallest enclosing ball of a point set. We prove that in order to remove degeneracies of an LP-type problem, we sometimes have to increase its combinatorial dimension by an arbitrarily large amount.

Analytical solutions for image orientation changes in prisms

Optical prisms are most commonly employed because of their ability to output an image of different orientation relative to the input object. Previous papers have presented a systematic but numeric approximation for designing a single prism, which outputs an image with a specific image orientation. Instead of a numerical solution, an exact analytical solution for the same problem is offered. Further, how to design prism systems by using off-the-shelf right-angle prisms and roof prisms as building blocks to obtain an output image with a specific image orientation is addressed. Illustrative examples are given to verify the proposed approach and demonstrate its use.

Estimating the distance to a monotone function

AbstractIn standard property testing, the task is to distinguish between objects that have a property 𝒫 and those that are ε‐far from 𝒫, for some ε > 0. In this setting, it is perfectly acceptable for the tester to provide a negative answer for every input object that does not satisfy 𝒫. This implies that property testing in and of itself cannot be expected to yield any information whatsoever about the distance from the object to the property. We address this problem in this paper, restricting our attention to monotonicity testing. A function f : {1,…,n} ↦ R is at distance εf from being monotone if it can (and must) be modified at εfn places to become monotone. For any fixed δ > 0, we compute, with probability at least 2/3, an interval [(1/2 − δ)ε,ε] that encloses εf. The running time of our algorithm is O(εf−1 log log εf− 1 log n), which is optimal within a factor of loglog εf−1 and represents a substantial improvement over previous work. We give a second algorithm with an expected running time of O(εf−1 log nlog log log n). Finally, we extend our results to multivariate functions. © 2007 Wiley Periodicals, Inc. Random Struct. Alg., 2007

Investigating Stagnation in Morphological Phylogenetics Using Consensus Data

Rieppel and Kearney (2002) have recently called attention to the lack of progress in resolving several highprofile disagreements in vertebrate phylogeny that, because of the crucial importance of fossil taxa, depend upon the interpretation of morphological data. Perhaps the best-known example concerns the phylogenetic placement of Testudines (turtles) within Amniota. Most textbooks assert that turtles lie outside a clade including all other extant reptiles because they lack temporal fenestrae in their skull (the presumed plesiomorphic, anapsid condition). However, there has long been dissent based on the possibility that, like all other extant reptiles, turtles are diapsids, and that they have secondarily lost the two fenestrae for which this group is named (e.g., Goodrich, 1916). The controversy stems from the highly divergent morphology of the group and the lack of intermediate forms. The earliest known turtle, Proganochelys quenstedti Baur, 1887, already has many of the features of modern turtles. This makes assessments of homology with potential close relatives difficult and, as evidenced by the literature, open to conflicting interpretations (homology assessments) that cannot all be correct. It might have been hoped or expected that the application of modern numerical phylogenetic techniques would resolve the phylogenetic position of turtles and their extinct relatives. Molecular data provide some support for the diapsid hypothesis (Iwabe et al., 2005, and references therein), but recent numerical phylogenetic studies of morphology have largely recapitulated rather than resolved the controversy (Fig. 1), with osteological data interpreted repeatedly as supporting either anapsid (e.g., Lee, 1997, 2001) or diapsid (e.g., Rieppel and deBraga, 1996; Rieppel and Reisz, 1999) affinities of turtles. Consensus objects (e.g., trees, sequences) are widely used in biology (Day and McMorris, 2003). Here we use data pertaining to the relationships of turtles to illustrate the use of a consensus approach for investigating conflict in morphological phylogenetics. We use this approach to identify and investigate agreements and disagreements between recent osteological data sets employed in the debate over diapsid and anapsid hypotheses and to investigate their importance in the stagnation of the debate on turtle origins. Most systematic biologists have some familiarity with the use of consensus techniques to provide representations of and/or inferences from, for example, sets of trees. Consensus objects can be viewed as representations of the input objects and/or as inferences from the input objects. In general, a consensus method is a mapping of a set of input objects (e.g., trees) onto a set of one or more output objects of the same type (e.g., trees). The mapping corresponds to the application of some consensus rule, the commonest of which (e.g., strict, majority-rule) relate to the extent of agreement among the input objects. Because many objects are complex structures that may agree in some respects while disagreeing in others, construction of consensus objects usually involves the decomposition of complex objects into component parts to which the consensus rule is applied. For example, in constructing a strict component consensus tree, the input trees are decomposed into sets of components (full splits, clades in rooted trees) and unanimous agreement upon individual components (i.e., their presence in every input tree) is the condition for inclusion of a component in the consensus tree. Where the objects are data matrices, the decomposition is into individual entries in each data matrix (i.e., the scoring of a taxon for a character) and it is the corresponding entries in different data matrices (i.e., the alternative scorings of a taxon for a character) that are subjected to a consensus rule. For example, unanimous agreement across the input data matrices is required for a datum to be included in strict consensus data. Where there is disagreement over the scoring of a character for a taxon, then at least one of the alternative scorings is incorrect and the conflict is represented by a missing

Removing Degeneracy May Require a Large Dimension Increase

Many geometric algorithms are formulated for input objects in general position; sometimes this is for convenience and simplicity, and sometimes it is essential for the algorithm to work at all. For arbitrary inputs this requires removing degeneracies, which has usually been solved by relatively complicated and computationally demanding perturbation methods. The result of this paper can be regarded as an indication that the problem of removing degeneracies has no simple “abstract” solution. We consider LP-type problems, a successful axiomatic framework for optimization problems capturing, e.g., linear programming and the smallest enclosing ball of a point set. For infinitely many integers $D$ we construct a $D$-dimensional LP-type problem such that in order to remove degeneracies from it, we have to increase the dimension to at least $(1+\epsilon)D$, where $\epsilon>0$ is an absolute constant. The proof consists of showing that certain posets cannot be covered by pairwise disjoint copies of Boolean algebras under some restrictions on their placement. To this end, we prove that certain systems of linear inequalities are unsolvable, which seems to require surprisingly precise calculations.

Output behavior based on writing occupancy in a photorefractive converter

In this paper, a one-dimensional image distribution is stored as modulation of birefringence in a BSO photorefractive crystal in an incoherent to coherent converter arrangement. The features of the read-out image under different write-in image magnifications of an input object are analyzed. In particular, characteristic parameters are introduced to assess the feasibility of an adequate recognition of the converted image. Experimental results under extinction configuration obtained by using intensity-normalized images are described.

Object segmentation and recovery via neural oscillators implementing the similarity and prior knowledge gestalt rules

Object recognition requires the solution of the binding and segmentation problems, i.e., grouping different features to achieve a coherent representation. Synchronization of neural activity in the γ-band, associated with gestalt perception, has often been proposed as a putative mechanism to solve these problems, not only as to low-level processing, but also in higher cortical functions. In the present work, a network of Wilson–Cowan oscillators is used to segment simultaneous objects, and recover an object from partial or corrupted information, by implementing two gestalt rules: similarity and prior knowledge. The network consists of H different areas, each devoted to representation of a particular feature of the object, according to a topological organization. The similarity law is realized via lateral intra-area connections, arranged as a “Mexican-hat”. Prior knowledge is realized via inter-area connections, which link properties belonging to a previously memorized object. A global inhibitor allows segmentation of several objects avoiding interference. Simulation results, performed using three simultaneous input objects, show that the network is able to detect an object even in difficult conditions (i.e., when some features are absent or shifted with respect to the original one). Moreover, the trade-off between sensitivity (capacity to detect true positives) and specificity (capacity to reject false positives) can be controlled acting on the extension of lateral synapses (i.e., on the level of accepted similarity). Finally, the network can also deal with correlated objects, i.e., objects which have some common features. Simulations performed using a different number of objects (2, 3, 4 or 5) suggest that the network is able to segment and recall up to four objects, but the oscillation frequency must increase, the lower the number of objects simultaneously present. The model, although quite simpler compared with neurophysiology, may represent a theoretical framework for the analysis of the relationships between object representation, memory, learning, and γ-band activity. In particular, it extends previous studies on autoassociative memory since it exploits not only oscillatory dynamics, but also a topological organization of features.

Evolutionary Testing Using an Extended Chaining Approach

Fitness functions derived from certain types of white-box test goals can be inadequate for evolutionary software test data generation (Evolutionary Testing), due to a lack of search guidance to the required test data. Often this is because the fitness function does not take into account data dependencies within the program under test, and the fact that certain program statements may need to have been executed prior to the target structure in order for it to be feasible. This paper proposes a solution to this problem by hybridizing Evolutionary Testing with an extended Chaining Approach. The Chaining Approach is a method which identifies statements on which the target structure is data dependent, and incrementally develops chains of dependencies in an event sequence. By incorporating this facility into Evolutionary Testing, and by performing a test data search for each generated event sequence, the search can be directed into potentially promising, unexplored areas of the test object's input domain. Results presented in the paper show that test data can be found for a number of test goals with this hybrid approach that could not be found by using the original Evolutionary Testing approach alone. One such test goal is drawn from code found in the publicly available libpng library.

Evolutionary Testing Using an Extended Chaining Approach

Fitness functions derived from certain types of white-box test goals can be inadequate for evolutionary software test data generation (Evolutionary Testing), due to a lack of search guidance to the required test data. Often this is because the fitness function does not take into account data dependencies within the program under test, and the fact that certain program statements may need to have been executed prior to the target structure in order for it to be feasible. This paper proposes a solution to this problem by hybridizing Evolutionary Testing with an extended Chaining Approach. The Chaining Approach is a method which identifies statements on which the target structure is data dependent, and incrementally develops chains of dependencies in an event sequence. By incorporating this facility into Evolutionary Testing, and by performing a test data search for each generated event sequence, the search can be directed into potentially promising, unexplored areas of the test object's input domain. Results presented in the paper show that test data can be found for a number of test goals with this hybrid approach that could not be found by using the original Evolutionary Testing approach alone. One such test goal is drawn from code found in the publicly available libpng library.

Quantum theory of super-resolution for optical systems with circular apertures

We present the two-dimensional quantum theory of super-resolution, applicable for a large variety of optical systems with circular pupils. Our theory is formulated in terms of circular prolate spheroidal functions which form the eigen basis of two-dimensional imaging system with circular pupils. We provide, in particular, analytical and numerical results for the point-spread function characterizing reconstruction of optical objects with super-resolution from diffraction-limited images. We evaluate the super-resolution factor as a function of the signal-to-noise ratio in the input object for coherent light and multimode squeezed light.

Superresolved imaging in digital holography by superposition of tilted wavefronts

A technique based on superresolution by digital holographic microscopic imaging is presented. We used a two dimensional (2-D) vertical-cavity self-emitting laser (VCSEL) array as spherical-wave illumination sources. The method is defined in terms of an incoherent superposition of tilted wavefronts. The tilted spherical wave originating from the 2-D VCSEL elements illuminates the target in transmission mode to obtain a hologram in a Mach-Zehnder interferometer configuration. Superresolved images of the input object above the common lens diffraction limit are generated by sequential recording of the individual holograms and numerical reconstruction of the image with the extended spatial frequency range. We have experimentally tested the approach for a microscope objective with an exact 2-D reconstruction image of the input object. The proposed approach has implementation advantages for applications in biological imaging or the microelectronic industry in which structured targets are being inspected.

From Design Specification to SystemC

In this paper, we present a framework for transforming the design model into SystemC code. Such a framework uses as input UML state machine and object diagrams, which are more and more used as design models in embedded systems. To do this, we have firstly used Poseidon tool for editing the design model and generating the XMI representation, and secondly integrated a transformation module leading to SystemC code. The mapping to SystemC code offers not only a system-level executable specification, but also a means to facilitating the system partitioning in hardware and software parts.

Binary phase only filters for rotation and scale invariant pattern recognition with the joint transform correlator

The joint transform correlator (JTC) is one of the two main optical image processing architecture which provides a highly effective way of comparing images in a wide range of applications. Traditionally, an optical correlator is used to compare an unknown input scene with a pre-captured reference image library, to detect if the reference occurs within the input. Strength of the correlation signal decreases rapidly as the input object rotates or varies in scale relative to the reference object. The aim of this paper is to overcome the intolerance of the JTC to rotation and scale changes in the target image. Many JTC systems are constructed with the use of ferroelectric liquid crystal (FLC) spatial light modulators (SLMs) as they provide fast two-dimensional binary modulation of coherent light. Due to the binary nature of the FLC SLMs used in the JTC systems, any image addressed to the device need to have some form of thresholding. Carefully thresholding the grey scale input plane and the joint power spectrum (JPS) has significant effect on the quality of correlation peaks and zero order (DC) noise. A new thresholding technique to binarise the JPS has been developed and implemented optically. This algorithm selectively enhances the desirable fringes in the JPS which provide correlation peaks of higher intensity. Zero order noise is further reduced when compared to existing thresholding techniques. Keeping in mind the architecture of the JTC and limitations of FLC SLMs, a new technique to design rotation and scale invariant binary phase only filters for the JTC architecture is presented. Filers design with this technique have limited dynamic range, higher discriminability among target and non-target objects, and convenience for implementation on FLC SLMs. Simulation and experiments shows excellent results of various rotation and scale invariant filters designed with this technique. A rotation invariant filter is needed for various machine vision applications of the JTC. By fixing the distance between camera and input object, the scale sensitivity of the correlator can be avoided. In contrast to the industrial machine vision applications, scale factor is very important factor for the applications of the JTC systems in defence and security. A security system using a scale invariant JTC will be able to detect a target object well in advance and will provide more time to take a decision.

SimEye: computer-based simulation of visual perception under various eye defects using Zernike polynomials

We describe a computer eye model that allows for aspheric surfaces and a three-dimensional computer-based ray-tracing technique to simulate optical properties of the human eye and visual perception under various eye defects. Eye surfaces, such as the cornea, eye lens, and retina, are modeled or approximated by a set of Zernike polynomials that are fitted to input data for the respective surfaces. A ray-tracing procedure propagates light rays using Snell's law of refraction from an input object (e.g., digital image) through the eye under investigation (i.e., eye with defects to be modeled) to form a retinal image that is upside down and left-right inverted. To obtain a first-order realistic visual perception without having to model or simulate the retina and the visual cortex, this retinal image is then back-propagated through an emmetropic eye (e.g., Gullstrand exact schematic eye model with no additional eye defects) to an output screen of the same dimensions and at the same distance from the eye as the input object. Visual perception under instances of emmetropia, regular astigmatism, irregular astigmatism, and (central symmetric) keratoconus is simulated and depicted. In addition to still images, the computer ray-tracing tool presented here (simEye) permits the production of animated movies. These developments may have scientific and educational value. This tool may facilitate the education and training of both the public, for example, patients before undergoing eye surgery, and those in the medical field, such as students and professionals. Moreover, simEye may be used as a scientific research tool to investigate optical lens systems in general and the visual perception under a variety of eye conditions and surgical procedures such as cataract surgery and laser assisted in situ keratomileusis (LASIK) in particular.

Spatial information transmission using orthogonal mutual coherence coding

We use the coherence of a light beam to encode spatial information. We apply this principle to obtain spatial superresolution in a limited aperture system. The method is based on shaping the mutual intensity function of the illumination beam in a set of orthogonal distributions, each one carrying the information for a different frequency bandpass or spatial region of the input object. The coherence coding is analogous to time multiplexing but with multiplexing time slots that are given by the coherence time of the illumination beam. Most images are static during times much longer than this coherence time, and thus the increase of resolution in our system is obtained without any noticeable cost.

Spatial targeting using queries in a 3-D GIS environment with application to mineral exploration

A query framework for spatial targeting within a 3-D geographic information system (GIS) software environment is introduced. Input to a query consists of parameters relevant to the query type together with a set of Common Earth Modelling objects represented as point sets, polygonal lines, surfaces, and grids or a region set (subset) thereof. The result of a 3-D GIS query is a region within each of the input objects that consists of nodes or grid cells where the query criteria was satisfied. We provide example scenarios, drawn from mineral exploration, where 3-D queries are used to guide spatial targeting within a near-mine or regional map scale setting. Query types supported are: proximity query (to a “probe” object), property query (numeric attribute), shell query (containment within a closed surface), meta-data query, feature query (dome, depression, curvature), trend query (dip plane, vector) and intersection query (with a “probe” object). Queries can be specific for a given object type but in general transcend object types. Standard set theoretical operations for a query results in newly defined regions and are supported within the Gocad © development environment. This development focuses on queries relevant in the 3-D data integration and interpretation stages of mature geological model development as well as early analysis, typically undertaken before a fully partitioned and attributed 3-D topological model is available.

Analytical derivation of distortion constraints and their verification in a learning vector quantization-based target recognition system

We obtain a novel analytical derivation for distortion-related constraints in a neural network- (NN)-based automatic target recognition (ATR) system. We obtain two types of constraints for a realistic ATR system implementation involving 4-f correlator architecture. The first constraint determines the relative size between the input objects and input correlation filters. The second constraint dictates the limits on amount of rotation, translation, and scale of input objects for system implementation. We exploit these constraints in recognition of targets varying in rotation, translation, scale, occlusion, and the combination of all of these distortions using a learning vector quantization (LVQ) NN. We present the simulation verification of the constraints using both the gray-scale images and Defense Advanced Research Projects Agency's (DARPA's) Moving and Stationary Target Recognition (MSTAR) synthetic aperture radar (SAR) images with different depression and pose angles.