This PDF file contains the editorial “Editorial” for OE Vol. 40 Issue 09

Using a tighter kick-out condition as a faster approach to nearest codeword searches is proposed. The proposed scheme finds the nearest codeword that is identical to the one found using a full search. However, using our scheme, the search time is much shorter. Our scheme first establishes a tighter kick-out condition. Then, the temporal nearest codeword can be obtained from the codewords that survive the tighter condition. Finally, the temporal nearest codeword cooperates with the query vector to constitute a better kick-out condition. In other words, more codewords can be excluded without actually computing the distances between the bypassed codewords and the query vector. Comparisons to previous work are included to present the benefits of the proposed scheme in relation to search time.

We describe a simple, accurate, and fast approach to obtaining high-quality 2-D multiscale symmetry axis transform (SAT) skeletons based on the exact Euclidean distance. The method involves labeling the contour pixels of the shape and propagating such labels only around the neighborhood of already obtained skeleton segments. Since only small neighborhoods are processed, the algorithm efficiency becomes maximized.

We present a novel technology intended for contacting ZnSe IR optical windows. Thin polyethylene films located between plates made of ZnSe are irradiated by strong IR radiation produced by a CO2 laser. The IR radiation caused changes in the structure of the polymer, which result in the high adhesion of polymer to the surfaces of ZnSe elements to be contacted. The technology enables contacting that maintains transparency in the broad IR band (1 to 25 ?m) except for a few narrow absorption bands. Low wavefront distortion is established by a thorough interferometry study of contacted elements. The contacted ZnSe elements allow 100 W of CO2 laser radiation to pass for 1 h with no drop in transparency. Special tests prove the high thermal stability of the bonded elements. The technology is intended for a wide range of IR optics applications.

The problem of modeling electro-optical (EO) systems for the purpose of ground vehicle countermeasure development and system performance evaluation has been around for many years. This special section is devoted to recent advances in (1) computational techniques and testing procedures to predict the detectability of man-made objects in the field and in (2) methods to validate and calibrate these techniques and procedures. Most metrics that are currently used to quantify visual target distinctness and to predict the probability of detection of a target in clutter do not relate to properties of the human visual system. As a result, their predictions do not correlate with the results of human observer tests. A well-known example is the mean square error (MSE) in intensity. Although this metric has a good physical and theoretical basis, it correlates poorly with observer performance. This is due to the fact that the human visual system does not analyze an image in a simple point-by-point manner. Bottom-up grouping mechanisms appear to drive the formation of emergent perceptual units from preattentively extracted stimulus features (e.g., edges or texture elements). When searching for known targets, top-down priming signals may influence the organization of search regions. Salient areas may then be selected for further inspection.

The TNO Human Factors Search_2 image dataset consists of: a set of 44 high-resolution digital color images of different complex natural scenes, the ground truth corresponding to each of these scenes, and the results of psychophysical experiments on each of these images. The images in the Search_2 dataset are a subset of a larger set that has been used in a visual search and detection experiment. Each scene (image) contains a single military vehicle that serves as a search target. The image dataset, an Excel file with the ground truth and observer data, and an extensive report describing the dataset are available on CD-ROM (requests by email to the first author). The dataset can be used to develop and validate digital metrics that compute the visual distinctness (contrast, conspicuity, or saliency) of targets in complex scenes, and models of human visual search and detection. The dataset has already been used in more than ten different studies in the literature, ranging from studies evaluating target detectability metrics to eye movement studies and attempts to model the human visual system. In addition to this work, eight other articles in this special section address the Search_2 dataset.

Most United States ground-to-ground and air-to-ground military target acquisition is performed with thermal sights on today's battlefield. It is extremely important, therefore, to develop systems that provide the best compatibility to the users, to predict their performance, and to correctly train them in the use of these systems to prevent fratricide and increase survivability. It is not possible to perform all the needed research by means of expensive field tests. Therefore, the use of perception studies has become popular for developing training, testing system designs, and assessing effectiveness of sensors and systems. We discuss some challenges involved in perception studies conducted to gain insight into surveillance and target acquisition by military users of thermal imagery. The goal is to emulate as accurately as possible what a military observer will actually see and how he will use the sensor to detect and identify targets. The issues discussed include prior training, panning effects on eye movements, and contrast and brightness controls. The latest advances in these areas and some remaining challenges are discussed.

Some shortcomings of past and current approaches for modeling human visual search and target acquisition (STA) are discussed. The effects of complex pattern perception, visual attention, learning, and cognition on STA performance are particularly emphasized. The importance of these processes is explained and approaches are suggested for modeling them. Guidelines are also provided for testing and validating models of visual STA. These guidelines take into account the roles of pattern perception, visual attention, learning, and cognition in STA performance. We also present and compare alternative approaches to field testing for the purpose of model validation.

Rather than attempting to fully interpret visual scenes in a parallel fashion, biological systems appear to employ a serial strategy by which an attentional spotlight rapidly selects circumscribed regions in the scene for further analysis. The spatiotemporal deployment of attention has been shown to be controlled by both bottom-up (image-based) and top-down (volitional) cues. We describe a detailed neuromimetic computer implementation of a bottom-up scheme for the control of visual attention, focusing on the problem of combining information across modalities (orientation, intensity, and color information) in a purely stimulus- driven manner. We have applied this model to a wide range of target detection tasks, using synthetic and natural stimuli. Performance has, however, remained difficult to objectively evaluate on natural scenes, because no objective reference was available for comparison. We present predicted search times for our model on the Search_2 database of rural scenes containing a military vehicle. Overall, we found a poor correlation between human and model search times. Further analysis, however, revealed that in 75% of the images, the model appeared to detect the target faster than humans (for comparison, we calibrated the model's arbitrary internal time frame such that 2 to 4 image locations were visited per second). It seems that this model, which had originally been designed not to find small, hidden military vehicles, but rather to find the few most obviously conspicuous objects in an image, performed as an efficient target detector on the Search_2 dataset. Further developments of the model are finally explored, in particular through a more formal treatment of the difficult problem of extracting suitable low-level features to be fed into the saliency map.

We present a new method for characterizing information about a target relative to its background. The resultant computational measures are then applied to quantify the visual distinctness of targets in complex natural backgrounds from digital imagery. A generalization of the Kullback-Leibler joint information gain over the optimal interest points of the target image is shown to correlate strongly with visual target distinctness as estimated by human observers. Optimal interest points are defined as spatial locations of partially invariant features, which minimize the error probability between the target and the nontarget scenes; their significance is a function of the corresponding degree of congruence across scales and orientations.

Human factors experiments can be used to test whether a sensor can improve operator performance for detecting or recognizing a target.1 Although human factors experiments are of tremendous value, these tests are time consuming and resource intensive. To reduce costs associated with collecting behavioral data, a two-dimensional matched filter is proposed. The objective is to compare and contrast behavioral and matched filter receiver operating characteristic (ROC) plots to determine whether the matched filter technique is a good predictor of human performance. Five different background images (three infrared band images, a chromatic-fused image, and monochromatic-fused image) were used with, and without, a target (airplane) present. False alarm and target detection probabilities were computed and results were plotted on an ROC curve. The matched filter ROC curves were then compared to behavioral ROC curves. Results showed that the matched filter ROC curves were similar to behavioral ROC curves with color fusion, longwave infrared showing the highest sensitivity, and mid-wave and shortwave infrared scenes were significantly less sensitive (near chance). These results indicate that the matched filter analysis may be used to model human behavior.

The psychophysical method of limits was used to measure the distance at which observers could distinguish military vehicles photographed in natural landscapes. Obtained from the TNO-TM Search_2 dataset, these pictures either were rear-projected 35-mm slides or were presented on a computer monitor. Based on the rationale that more difficult vehicle targets would require more visual pathways for recognition, difficulty of acquisition was defined in terms of the relative retinal area required for recognition. Relative retinal area was derived from the inverse square of the recognition distance of a particular vehicle relative to the distance of the vehicle that could be seen furthest away. Results are compared with data on the time required to find the vehicles in these pictures. These comparisons indicate recognition distance thresholds can be a suitable means of defining standards for the effectiveness of vital graphic information; and the two methods are complementary with respect to distinguishing different degrees of acquisition difficulty, and together may provide a means to measure the total information processing required for recognition.

We present a methodology for computational evaluation of camouflage effectiveness. The methodology is implemented in software at the Danish Defence Research Establishment (DDRE) under the acronym for camouflage evaluation (CAMEVA). Basic input is a single image comprising a highly resolved static target as well as a proper amount of representative background. Separate target and background images can also be handled. Target and background regions are manually selected using the computer’s standard pointing device, the mouse. From the input data, CAMEVA predicts the target detectability as a function of the target distance. The detectability estimate is based on statistical distributions of features extracted from the imagery, establishing a multidimensional feature space. In the feature space, the Bhattacharyaa distance measure is applied as an estimator of the separability between the target and background. The intention is that the extracted features should resemble those applied during the human perception process. Typically, contrast and various measures of edge strength are applied. The Bhattacharyaa distance establishes a relative separability, while the absolute detection range is obtained by deriving a relation between the Bhattacharyaa distance and the estimated target resolution, at range. Thus by introducing parameters of the sensor, typically the human unaided eye, detectability as a function of the range is obtained. The methodology does not reflect individual observer performance but is aimed at providing an estimate of the optimal detection performance, given the selected set of features. During the choice of features and of sensor parameters, perception mechanisms other than the human observer performance can be modeled with this methodology. We discuss theoretical and practical aspects of CAMEVA. Validation and application examples, including results on the Search–1 and Search–2 datasets, are presented together with other data.

The mean search time of observers searching for targets in visual scenes with clutter is computed using the fuzzy logic approach (FLA). The FLA is presented as a robust method for the computation of search times and/or probabilities of detection for treated vehicles. The Mamdani/Assilian and Sugeno models have been investigated and are compared. The Search_2 dataset from TNO is used to build and validate the fuzzy logic approach for target detection modeling. The input parameters are: local luminance, range, aspect, width, and wavelet edge points, and the single output is search time. The Mamdani/Assilian model gave predicted mean search times for data not used in the training set that had a 0.957 correlation to the field search times. The data set is reduced using a clustering method, then modeled using the FLA, and results are compared to experiment.

Using image-based metrics, a contrast-to-clutter model is applied to a visible imagery set and perception experiment data. To calculate the contrast metric, a new image is generated from a gray scale version of the original image by replacing the target with an expected background using the local background surrounding the target. The contrast metric is obtained from the difference of this new image and the original image. Via a simple mathematical formula, the ratio of the contrast measure to a clutter metric is used to predict performance.

Ground vehicles in natural lighting tend to have significant and systematic variation in luminance through the presented area. This arises, in large part, from the vehicle surfaces having different orientations and shadowing relative to the source of illumination and the position of the observer. These systematic differences create the appearance of a structured 3-D object. The 3-D appearance is an important factor in search, figure-ground segregation, and object recognition. We present a contrast metric to predict search and detection performance that accounts for the 3-D structure. The approach first computes the contrast of the front (or rear), side, and top surfaces. The vehicle contrast metric is the area-weighted sum of the absolute values of the contrasts of the component surfaces. The 3-D structure contrast metric, together with target height, account for more than 80% of the variance in probability of detection and 75% of the variance in search time. When false alarm effects are discounted, they account for 89% of the variance in probability of detection and 95% of the variance in search time. The predictive power of the signature metric, when calibrated to half the data and evaluated against the other half, is 90% of the explanatory power.

Developments in the area of signature suppression make it progressively more difficult to recognize targets. The emphasis has been on the reduction of distinct features, like hot spots in the infrared band. Thus, to obtain a low false alarm rate, threat sensors have to utilize more information, i.e., spatial and spectral properties. The purpose of our work is to develop a general tool for camouflage assessment. The approach proposed applies texture descriptors to quantify the similarity between different parts of an image. In addition, other descriptors are used to distinguish man-made object characteristics. The selection of an appropriate set of features is discussed. The assumption is that an area containing observable targets has different statistics than other areas. Statistical properties along with detected target specific features have to be combined with methods used in data fusion. An experiment with a dataset of 44 reference images has been carried out, using a recently developed computer program called Terrtex. High correlation with perception experiments is achieved using only one or two texture features. The importance of a careful selection of background area size is finally discussed.

The effect of imagery wavelength on the agreement level between various image metrics and human detection performance of targets embedded in natural images is addressed. Three specially designed psychophysical experiments were conducted to record human detection performance of targets embedded in natural infrared images (3 to 5 and 8 to 12 ?m) and in natural images taken in the visual region of the spectrum. The image metrics used were designed to imitate some of the most important human perceptual cues, such as global image complexity (clutter), local target to background distinctness (contrast), and texture. The agreement level between the experimental results and the metrics products was evaluated by correlating the metrics values with target detection results obtained from each of the three psychophysical experiments. In general, it was found that the longer the imagery wavelength, the greater the dominance of global image complexity on human detection performance, and that local target to background distinctness correlates with performance for short wavelength imagery. It was also found that image texture correlates with human detection performance with very little dependence on imagery wavelength.

We present an experimental framework for evaluating metrics for the search and discrimination of a natural texture pattern from its background. Such metrics could help identify preattentive cues and underlying models of search and discrimination, and evaluate and design camouflage patterns and automatic target recognition systems. Human observers were asked to view image stimuli consisting of various target patterns embedded within various background patterns. These psychophysical experiments provided a quantitative basis for comparison of human judgments to the computed values of target distinctness metrics. Two different experimental methodologies were utilized. The first methodology consisted of paired comparisons of a set of stimuli containing targets in a fixed location known to the observers. The observers were asked to judge the relative target distinctness for each pair of stimuli. The second methodology involved stimuli in which the targets were placed in random locations unknown to the observer. The observers were asked to search each image scene and identify suspected target locations. Using a prototype eye tracking testbed, the integrated testbed for eye movement studies, the observers' fixation points during the experiment were recorded and analyzed. For both experiments, the level of correlation with the psychophysical data was used as the basis for evaluating target distinctness metrics. Overall, of the set of target distinctness metrics considered, a metric based on a model of image texture was the most strongly correlated with the psychophysical data.

Assessment of camouflage, concealment, and deception (CCD) methodologies is not a trivial problem; conventionally the only method has been to carry out field trials, which are both expensive and subject to the vagaries of the weather. In recent years computing power has increased, such that there are now many research programs using synthetic environments for CCD assessments. Such an approach is attractive; the user has complete control over the environment parameters and many more scenarios can be investigated. The UK Ministry of Defence is currently developing a synthetic scene generation tool for assessing the effectiveness of air vehicle camouflage schemes. The software is sufficiently flexible to allow it to be used in a broader range of applications, including full CCD assessment. The synthetic scene simulation system (CAMEO-SIM) has been developed, as an extensible system, to provide imagery within the 0.4 to 14 ?m spectral band with as high a physical fidelity as possible. It consists of a scene design tool, an image generator, that incorporates both radiosity and ray-tracing processes, and an experimental trials tool. The scene design tool allows the user to develop a three-dimensional representation of the scenario of interest from a fixed viewpoint. Target(s) of interest can be placed anywhere within this 3-D representation and may be either static or moving. Different illumination conditions and effects of the atmosphere can be modeled together with directional reflectance effects. The user has complete control over the level of fidelity of the final image. The output from the rendering tool is a sequence of radiance maps, which may be used by sensor models or for experimental trials in which observers carry out target acquisition tasks. The software also maintains an audit trail of all data selected to generate a particular image, both in terms of material properties used and the rendering options chosen. A range of verification tests has shown that the software computes the correct values for analytically tractable scenarios. Validation tests using simple scenes have also been undertaken. More complex validation tests using observer trials are planned. The current version of CAMEO-SIM and how its images are used for camouflage assessment is described. The verification and validation tests undertaken are discussed. In addition, example images will be used to demonstrate the significance of different effects, such as spectral rendering and shadows. Planned developments of CAMEO-SIM are also outlined.

A new direction for the US Army Night Vision and Electronic Sensors Directorate is the development of ultra-narrow field of view (UNFOV) infrared target acquisition (TA) systems. Frequently, the performance of these systems is limited by atmospheric turbulence in the imaging path. It is desirable to include the effects of atmospheric turbulence blur in infrared TA models. The current TA models are currently linear shift invariant (LSI) systems with component modulation transfer functions (MTFs). The use of additional MTFs, to account for atmospheric turbulence, requires that the turbulence blur have LSI properties. The primary unresolved issue with the treatment of turbulence blur as an MTF is the LSI characteristics of the blur. Significant variation in spatial blur and temporal blur prohibit the use of a single MTF in an LSI target acquisition model. Researchers at Ben-Gurion University (BGU) use a TA model that includes an LSI blur, which is a temporal average of the turbulence blur. The research described here evaluates the BGU-type treatment of atmospheric MTF and determines it reasonable for inclusion in the US Army's TA model. In addition to the spatial characteristics, the temporal variation of the turbulence blur is also described.

This study investigates observer performance in visual search and target detection tasks for two different conditions of image viewing: binocular viewing of single line of sight images versus stereoscopic display of wide baseline stereo images. A multiple baseline imagery database was obtained during a field test. The targets were personnel wearing forest camouflage uniforms. They were arrayed at two rural terrain sites. The weather condition at the first site was clear to partly cloudy and at the second site overcast with light rain. The preliminary analysis of the first site database indicated that wide baseline stereo can improve search and target acquisition, but that the baseline used was too wide for many of the closer targets at that site. The database from the second site was then analyzed through observer testing, with single line of sight and wide baseline stereo displays. The results indicate that with training, stereo vision effectively reduces false alarm detection by a factor of 2. Additionally, guidelines are obtained for optimum stereo display that can be used to improve positive target detection by 20%.

A 1-D drift-diffusion model is developed for reach-through avalanche photodiodes. Based on this model an equivalent circuit is suggested for these devices. The equivalent circuit is suitable for simulating the device in circuit simulation packages such as PSpice. The model is validated by simulating an actual reach-through silicon avalanche photodiode in the steady state and the transient conditions. Some of the simulation results are compared to the manufacturer data by estimating the device parameters. This model is also used to investigate the effect of the device microstructure on its performance. For a certain operating bias voltage and wavelength, it is found that the multiplication width has a critical value for defining the device stability. The absorption width defines the wavelength for peak responsivity, which suggests a structure enhancement for IR detection. Also the effect of varying the doping concentration is studied.

We describe a dwell time algorithm for the polishing of small axis-symmetrical aspherical surfaces. The dwell time distribution of the scanning polishing tool on the rotating workpiece is calculated to reduce the residual surface error. The dwell time at each discrete grid is calculated as an integer multiple of the workpiece rotation period, which is also useful for the spatially varying case in the local polishing area. A spherical polyurethane tool with abrasives is adopted for a computer- controlled polishing process. A linear algebraic equation of removal depth, removal matrix, and dwell time is derived by convolution of the removal depth at the dwell positions. The nonnegative least-squares method gives a solution to minimize residual error. Parametric effects such as the dwell grid interval are simulated. Finally, an experiment for tool mark removal is performed and the dwell time algorithm is evaluated to be valid.

A differential absorption lidar (DIAL) system consisting of two identical tunable laser systems and a single optical receiver is applied to measurement of O3 concentration profiles in the lower troposphere. Each laser is capable of emitting two wavelengths on alternate pulses, so the system is capable of simultaneous measurement of two species in the same wavelength region. We set the two lasers to emit at identical wavelength pairs consisting of on wavelength 285.0 nm and off wavelength 290.1 nm for simultaneous measurement of two null profiles, one at each wavelength, and two DIAL profiles, or O3 concentration profiles. Null profiles are useful in estimating instrumental error and checking the vertical range interval in which the DIAL profiles are accurate. Null and DIAL profiles are obtained for vertical range 1000 to 4000 m using neutral density filters of different transmissions to prevent the strong return signals from close range from saturating the photodetector. The obtained O3 concentration profiles agree with simultaneous O3 sonde measurements. An evaluation of the measurement error shows that the average O3 measurement error for vertical range 1000 to 4000 m was 3.4 ppb, or 8% relative to the average O3 concentration of 42.3 ppb, most of which is due to statistical error. The error due to differential Mie attenuation and differential backscatter gradient was found to be 0.5 ppb.

With the coming of the digital era, digital imagery has become an important medium of information processing. However, there is a major problem: digital images can be forged easily. To increase the degree of credibility for digital imagery, some image tamper proofing and recovering techniques should be explored. We propose a new secure recoverable tamper proofing method. The proposed method exploits the vector quantization (VQ) technique to embed some information into the middle-frequency range of the image transformed by the discrete cosine transform (DCT), where the embedded information can be used for tamper detection and recovery. The experimental results show that the proposed method can securely and effectively detect the tamper and recover the image. Even though the image is tampered with beyond recognition, the technique can still recover the original content. Our proposed method is therefore an effective and secure tamper processing technique.

Relations for Gaussian beam propagation are expressed in full diameters and full angles. They are also expressed in terms of area and solid angle. The resulting formulas are more elegant than traditional expressions with half diameters and half angles. The space-angle product of a Gaussian beam is derived from the Heisenberg uncertainty principle. The resulting 2-D space-angle product contains only parameters of the beam without any scalar constants. The longitudinal uncertainty is employed to derive the coherence length.

Most of the current lossless compression algorithms, including the new international baseline JPEG-LS algorithm, do not exploit the interspectral correlations that exist between the color planes in an input color (RGB) picture. To improve the compression performance (i.e., lower the bit rate) it is necessary to exploit these correlations. A major concern is to find efficient methods for exploiting the correlations that, at the same time, are compatible with and can be incorporated into the JPEG-LS algorithm. One such algorithm is the method of intercolor error prediction (IEP), which when used with the JPEG-LS algorithm, results on average in a reduction of 8% in the overall bit rate. We show how the IEP algorithm can be simply modified and that it nearly doubles the size of the reduction in bit rate to 15%.

The applicability of digital image-processing techniques is studied to enhance the displacement resolution of far-IR Fizeau interferometry (FIFI). Two image-processing techniques are considered-the optical/digital fringe multiplication (O/DFM) method and the phasestepping method. The effect of multiple reflections on the fractional fringe order is analyzed mathematically and the results are verified by experimental data. The results indicate that the O/DFM method is fully compatible with the complex asymmetric intensity distribution produced by FIFI, while the phase-stepping method yields significant errors in fractional fringe orders when the surface reflectivity is high. The errors are quantified to provide an application guideline for the phase-stepping method.

Moire interferometry is a high-sensitivity method for whole- field, in-plane displacement measuring. This technique requires the surface under test to be mirror-like and prepared with a fine diffraction grating (typically ~1200 lines/mm). Gratings are commonly made of photoresist. However, if we want to analyze engineering structures, to have a surface without preparation under polishing, the surface roughness is an important parameter to be characterized. We analyze the fringe visibility of moire during the fabrication of specimen gratings in function of the surface object roughness. The surface object target is impregnated with photoresist Shipley S1822. Two beams from a He-Cd laser illuminating the surface target are used to record the specimen grating. With a similar optical system we use a He-Ne laser to obtain a virtual grating and consequently the interferometric moire fringes. Through the correlation length and basic statistical the surface roughness is characterized.

When mechanical vibrations are measured by means of a dynamic phase-shifting speckle pattern interferometer, the deformation can be tracked with an error that depends on the amplitude and frequency of the vibration. We use a numerical simulation based on a detailed mathematical model of the system to predict the expected frequency response of the root mean square (rms) measurement error in the time-varying phase difference maps. The performance of four phase- shifting algorithms (three-frame, four-frame, Carre, and Schwider- Hariharan five-frame) and a temporal phase unwrapping method is studied over a range of vibrational amplitudes and frequencies. The numerical results indicate that the Carre algorithm is the preferred phase-shifting method to measure vibrations with a dynamic electronic speckle pattern interferometry (ESPI) system. Vibration frequencies up to 30% of the carrier frequency can be measured with an rms phase change error less than l0% of the vibration amplitude. The numerical results are finally compared with experimental data, acquired using a 1000 frame/s phase-shifting speckle interferometer together with a laser vibrometer, which provides a reference phase measure.

We present the results of experimental examinations of the interaction of laser light with the surfaces of cast test bars. From the scientific literature, it is known that the unique properties of laser radiation result in the grained structure (called speckles) of the angular distribution for the intensity of light reflected from rough surfaces. The features of these speckles depend on the characteristics of the rough surfaces. The measuring circuit of the experimental set-up consisted of a CCD camera and a microcomputer. Computer analysis of the speckle images (or angular distributions) recorded with the camera reveal the existence of the dependencies between the parameters of surface roughness (i.e., the mean square deviation of the profile o) and some features of these images (the mean area S under the curve of light intensity for one line of the image or its contrast C). The examined samples of the castings have the values of the profile roughness height Rz from the range 10 to 160 ?m.

Matrix-matrix multiplication has an important and essential role in digital data processing. In optical parallel computation, many proposals using various matrix-matrix multiplication techniques were seen in the last few decades. We propose a new concept for a digital matrix multiplication scheme using fiber optic spatial maps and at the same time implementation with nonlinear optical material.

image registration algorithms based on gradient methods provide quantitative motion measurements from sequences of video images. Although such measurements can be degraded by image noise, larger degradations typically result from systematic bias in the algorithms that is present even if the images are noise-free. To improve the accuracy of motion measurements, we develop a new class of multi-image algorithms based on multidimensional digital filters. The new algorithms provide better estimates of spatial and temporal gradients and also compensate for motion blur caused by the nonzero acquisition time of the imager. We optimize filters to measure arbitrary motions, and we illustrate the results when those filters are used to estimate constant velocity movements. We also show results for filters that are optimized for harmonic analysis of periodic motions. Using these algorithms, systematic bias in the amplitude of sinusoidal motion is less than 0.001 pixels for motions smaller than 1 pixel in amplitude. This represents a hundredfold decrease in bias compared to existing methods.

Binary and continuous phase-only surface relief diffractive optical elements can be fabricated on hybrid sol-gel glass based on a single-step microfabrication process. We report on the fabrication and characterization of an inorganic-organic hybrid SiO2/TiO2 sol-gel glass by UV light exposure. Preliminary results of binary and continuous surface relief structures fabricated on sol-gel glass with UV exposure using holographic technique and a high-energy-beam-sensitive (HEBS) glass mask are demonstrated. The effects of the fabrication parameters such as doping concentration, bake temperature, and UV light exposure time on the film characteristics (thickness and refractive index) are studied.

A new method is developed to fabricate a refractive microlens array by etching ammonium dichromate gelatin (ADG) with enzyme solution. The light exposes ADG through a high contrast binary mask, then the exposed parts of ADG generate a cross-linking reaction. Usually, the relief achieved by water developing is very shallow (<1 ?m), so we compound a 0.2% concentration protein enzyme solution as the developer to develop ADG and deepen its relief. At 37°C, the biochemical reaction occurs between ADG and the enzyme solution, and because of surface tension, ADG turns to spherical structure after developing. The optimum technique parameters of this process are presented. The principle of etching ADG with an enzyme solution to achieve deep relief and the swelling of ADG are discussed in detail. The process of fabricating microlens array does not require the use of expensive equipment, and it is not sophisticated and time consuming. Results are presented for experiments and evaluated by a profile meter and an interference microscope.

A new application of glass surface treatment is presented. Fine polishing of optical fiber end (silica type), core/cladding surface is performed. A low-power monomode longitudinal 10-W continuous wave CO2 laser is used. Laser power density is 140 W/cm2. The beam is spatially filtered to increase the beam uniformity. A decrease of the roughness from 2.5 ?m average to less than 100 nm is achieved. The microstructure of the surfaces is studied using atomic force microscopy. An optimum irradiation time of exposure of 6 s is found. A loss decrease of 1.5 dB after laser irradiation is measured. Thus, a simple method to further decrease the optical fiber losses after mechanical polishing is put into evidence. This method is easy to apply to any type of optical fiber. Moreover, it is considered that for such polished fibers, when used in conjunction with a connector (FC/PC, ST, SC, APC, and DIN type connectors), the efficiency in any application increases.

The passive Q-switching performance of the Cr:YSO Q-switched ruby laser for different polarization orientations is numerically studied in detail with the Runge-Kutta method. The simulation results show that the Cr:YSO Q-switched ruby laser system has a better passive Q-switching performance when the polarization orientation is along the n1 axis of the Cr:YSO, when compared to the n2 and n3 axes. With a typical laser configuration, a Q-switched laser pulse of 19.8 mJ in 22.3 ns can be obtained.

A method of recognizing and classifying 3-D shapes with continuous surfaces by integrating shadow moire technique and neural network is presented. Unlike existing methods of 3-D shape recognition that use range images of polyhedral objects or objects of different geometries such as cones, rods, spheres, etc., the proposed method classifies continuous surfaces that are geometrically similar. The objects selected to test the classification method are eggs of four different grades. The shadow moire technique, which has greater sensitivity compared to structured lighting or laser scanning, is used to obtain moire patterns on the surface of the eggs. From the moire pattern images 14 parameters are extracted and used as input to a multilayer feedforward neural network. The results of the classification using the neural network show that the prediction accuracy attainable is 60% when classification is performed on all four grades. The accuracy increased to 95% when three of the grades are classified.

Laser triangulation is used to examine the vertical (longitudinal) movements of the vocal folds during phonation. A method for evaluating the blurry, elongated image of the laser spot by vibration amplitude is presented. The method is used in situ on humans and is calibrated with known vibrations of bovine tissue.