We present a study of light-emitting diodes (LEDs) using organic fluorescent dyes to replace the general phosphor. The blue die with a specific organic fluorescent dye gives the LED a single color appearance. Through a color-mixing cavity, multiple LEDs are used to produce a quasisolar spectrum at a certain band and white light with a color rendering index as high as 97 at around 2800 K.

We propose a multispectral index to assist the detection of human signatures in complex natural environments. Differently from previously proposed indices, it takes into account the spectral responses of human skin not only in the near infrared, but also in the visible region of the light spectrum. As a result, it can contribute to mitigate the chances of false alarms during time-critical search and rescue operations carried out in such environments. Our investigation is supported by the use of reflectance data measured for different skin specimens and natural materials such as sand, ocean water, melting snow, and forest vegetation. We believe that the observations reported in this work can be incorporated into the design of more effective procedures and devices for the differentiation of human targets from background materials commonly found in nature.

Temperature calculation is a prerequisite for infrared radiation simulation, which is widely applied in the military field. However, conventional sequential temperature computation approaches are computationally inefficient and inadequate with regard to thermal details. A method of parallel temperature calculation based on OptiX is proposed to solve this problem. First, a brief introduction to OptiX is given, and its abilities to implement parallel temperature calculation and infrared scene simulation are demonstrated. As the foundation of the temperature calculation, the one-dimensional thermal conduction equation and boundary conditions of the natural scene are then described. After that the basic equations are generalized to the case of multilayer materials. Finally, the relationship between the geometric models and relevant material properties is articulated to facilitate the temperature calculation procedure. The results show that the developed platform is feasible for parallel temperature calculation, and qualitative analysis is preliminarily presented to substantiate the rationality of the simulation result.

Linear pyroelectric array sensors have enabled useful classifications of objects such as humans and animals to be performed with relatively low-cost hardware in border and perimeter security applications. Ongoing research has sought to improve the performance of these sensors through signal processing algorithms. In the research presented here, we introduce the use of hidden Markov tree (HMT) models for object recognition in images generated by linear pyroelectric sensors. HMTs are trained to statistically model the wavelet features of individual objects through an expectation–maximization learning process. Human versus animal classification for a test object is made by evaluating its wavelet features against the trained HMTs using the maximum-likelihood criterion. The classification performance of this approach is compared to two other techniques; a texture, shape, and spectral component features (TSSF) based classifier and a speeded-up robust feature (SURF) classifier. The evaluation indicates that among the three techniques, the wavelet-based HMT model works well, is robust, and has improved classification performance compared to a SURF-based algorithm in equivalent computation time. When compared to the TSSF-based classifier, the HMT model has a slightly degraded performance but almost an order of magnitude improvement in computation time enabling real-time implementation.

Two-dimensional to three-dimensional (3-D) conversion in 3-D video applications has attracted great attention as it can alleviate the problem of stereoscopic content shortage. Depth estimation is an essential part of this conversion since the depth accuracy directly affects the quality of a stereoscopic image. In order to generate a perceptually reasonable depth map, a comprehensive depth estimation algorithm that considers the scenario type is presented. Based on the human visual system mechanism, which is sensitive to a change in the scenario, this study classifies the type of scenario into four classes according to the relationship between the movements of the camera and the object, and then leverages different strategies on the basis of the scenario type. The proposed strategies efficiently extract the depth information from different scenarios. In addition, the depth generation method for a scenario in which there is no motion, neither of the object nor the camera, is also suitable for the single image. Qualitative and quantitative evaluation results demonstrate that the proposed depth estimation algorithm is very effective for generating stereoscopic content and providing a realistic visual experience.

A technique for modulating point-target infrared (IR) signatures based on the anisotropic emission behavior of the target surface is proposed. The pixel equation is used as the physical basis for quantifying the IR transmission process and is evaluated by the ray tracing method. The maximum ability to enhance and reduce signals is represented by the positive and negative maximums of the sensitivity. It is found that the modulating effects of anisotropic emission properties upon IR signals are closely related to the geometric features as well as the hemispherical emissivity. The plane is the most sensitive, while the sphere, whose sensitivity approaches zero, is the least sensitive. Furthermore, as for the nonspherical targets, the smaller the hemisphere is, the better the modulating effects are.

Cuckoo search (CS) is a new meta-heuristic optimization algorithm that is based on the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds and fruit flies. It has been found to be efficient in solving global optimization problems. An application of CS is presented to solve the visual tracking problem. The relationship between optimization and visual tracking is comparatively studied and the parameters’ sensitivity and adjustment of CS in the tracking system are experimentally studied. To demonstrate the tracking ability of a CS-based tracker, a comparative study of tracking accuracy and speed of the CS-based tracker with six “state-of-art” trackers, namely, particle filter, meanshift, PSO, ensemble tracker, fragments tracker, and compressive tracker are presented. Comparative results show that the CS-based tracker outperforms the other trackers.

A directional Bollinger bands (BB) method for the detection of defects in plain and twill fabric is presented, whereas a previous BB method was for patterned Jacquard fabric. BB are constructed using the moving average and standard deviation to characterize any irregularities (i.e., defects) in a patterned texture. Every patterned texture constitutes a primitive unit that can be used to generate the texture by a translational rule. The regularity property for a patterned texture can be implicitly regarded as the periodic signals on the rows and columns of an image. To utilize such a regularity property, an embedded shift-invariant characteristic of BB is explored. The original BB method is further developed using directional rotation iterations, which enables the detection of directional defects in plain and twill fabric. The directional BB method is an efficient, fast, and shift-invariant approach that enables defective regions to be clearly outlined. This approach is also immune to the alignment problem that often arises in the original method. The detection accuracies for 77 defective images and 100 defect-free images are 96.1% and 96%, respectively. In a pixel-to-pixel evaluation comparing the detection results of the defective images with the ground-truth images, a 93.51% detection success rate is achieved.

We propose and implement three-dimensional (3-D) ring-scanning equipment for near-infrared (NIR) diffuse optical imaging to screen breast tumors under prostrating examination. This equipment has the function of the radial, circular, and vertical motion without compression of breast tissue, thereby achieving 3-D scanning; furthermore, a flexible combination of illumination and detection can be configured for the required resolution. Especially, a rotation-sliding-and-moving mechanism was designed for the guidance of source- and detection-channel motion. Prior to machining and construction of the system, a synthesized image reconstruction was simulated to show the feasibility of this 3-D NIR ring-scanning equipment; finally, this equipment is verified by performing phantom experiments. Rather than the fixed configuration, this addressed screening/diagnosing equipment has the flexibilities of optical-channel expansion for spatial resolution and the dimensional freedom for scanning in reconstructing optical-property images.

We implement a depth-of-field (DOF) extending pickup experiment of integral imaging based on amplitude-modulated sensor arrays (SAs). By implementing the amplitude-modulating technique on the SA in the optical pickup process, we can modulate the light intensity distribution in the imaging space. Therefore, the central maximum of the Airy pattern becomes narrower and the DOF is enlarged. The experimental results obtained from the optical pickup process and the computational reconstruction process demonstrate the effectiveness of the DOF extending method. We present that the DOF extending pickup method is more suitable for enhancing the DOF of three-dimensional scenes with small depth ranges.

Line structured light techniques, especially laser scanning, are preferred for commercialized three-dimensional (3-D) shape acquisition. Typically, a captured line stripe is to be detected spatially within a single image, and the detection may fail if there are ambiguities in the image. We present a decoding strategy for line structured light patterns. Over all recorded line-patterned images, by means of Fourier analysis along the time axis for each pixel, a phase map is computed and employed for 3-D reconstruction. The phase error is theoretically analyzed. Experimental results demonstrate that, compared with typical approaches based on spatial stripe peak detection, the proposed method achieves comparable accuracy and, most importantly, successfully handles the issue of ambiguities.

Vector quantization (VQ) is a commonly used technique for image compression. Typically, the common codebooks (CCBs) that are designed by using multiple training images are used in VQ. The CCBs are stored in the public websites such that their storage cost can be omitted. In addition to the CCBs, the private codebooks (PCBs) that are designed by using the image to be compressed can be used in VQ. However, calculating the bit rates (BRs) of VQ includes the storage cost of the PCBs. It is observed that some codewords in the CCB are not used in VQ. The codebook refinement process is designed to generate the refined codebook (RCB) based on the CCB of each image. To cut down the BRs, the lossless index coding process and the two-stage lossless coding process are employed to encode the index table and the RCB, respectively. Experimental results reveal that the proposed scheme (PS) achieves better image qualities than VQ with the CCBs. In addition, the PS requires less BRs than VQ with the PCBs.

An adaptive one-dimensional (1-D) dimming technique for liquid crystal displays that compensates for nonuniform backlight distribution is proposed. Dimming techniques that do not consider luminance distribution may cause severe visual artifacts, such as a block artifact. However, an adaptive 1-D dimming technique that considers luminance distribution can reduce power consumption without causing any visual artifacts. Hardware implementation results verified that our method achieved lower power consumption compared to nondimming techniques and removed block artifacts from International Electrotechnical Commission 62087 standard images. The power consumption using the proposed method ranged from 85.5% to 94.7% compared to nondimming techniques. Furthermore, the contrast ratio increased by up to 231% and 165% on average compared to nondimming techniques.

A method to estimate the environmental illumination distribution of a scene with gradient-based ray and candidate shadow maps is presented. In the shadow segmentation stage, we apply a Canny edge detector to the shadowed image by using a three-dimensional (3-D) augmented reality (AR) marker of a known size and shape. Then the hierarchical tree of the connected edge components representing the topological relation is constructed, and the connected components are merged, taking their hierarchical structures into consideration. A gradient-based ray that is perpendicular to the gradient of the edge pixel in the shadow image can be used to extract the shadow regions. In the light source detection stage, shadow regions with both a 3-D AR marker and the light sources are partitioned into candidate shadow maps. A simple logic operation between each candidate shadow map and the segmented shadow is used to efficiently compute the area ratio between them. The proposed method successively extracts the main light sources according to their relative contributions on the segmented shadows. The proposed method can reduce unwanted effects due to the sampling positions in the shadow region and the threshold values in the shadow edge detection.

A dense surface reconstruction approach based on the fusion of monocular vision and three-dimensional (3-D) flash light detection and ranging (LIDAR) is proposed. The texture and geometry information can be obtained simultaneously and quickly for stationary or moving targets with the proposed method. Primarily, our 2-D/3-D fusion imaging system including cameras calibration and an intensity-range image registration algorithm is designed. Subsequently, the adaptive block intensity-range Markov random field (MRF) with optimizing weights is presented to improve the sparse range data from 3-D flash LIDAR. Then the energy function is minimized quickly by conjugate gradient algorithm for each neighborhood system instead of the whole MRF. Finally, the experiments with standard depth datasets and real 2-D/3-D images demonstrate the validity and capability of the proposed scheme.

The analysis of fluorescence molecular tomography is important for medical diagnosis and treatment. Although the quality of reconstructed results can be improved with the increasing number of measurement data, the scale of the matrices involved in the reconstruction of fluorescence molecular tomography will also become larger, which may slow down the reconstruction process. A new method is proposed where measurement data are reduced according to the rows of the Jacobian matrix and the projection residual error. To further accelerate the reconstruction process, the global inverse problem is solved with level-by-level Schur complement decomposition. Simulation results demonstrate that the speed of the reconstruction process can be improved with the proposed algorithm.

An efficient multifocus image fusion scheme in nonsubsampled contourlet transform (NSCT) domain is proposed. Based on the property of optical imaging and the theory of defocused image, we present a selection principle for lowpass frequency coefficients and also investigate the connection between a low-frequency image and the defocused image. Generally, the NSCT algorithm decomposes detail image information indwells in different scales and different directions in the bandpass subband coefficient. In order to correctly pick out the prefused bandpass directional coefficients, we introduce multiscale curvature, which not only inherits the advantages of windows with different sizes, but also correctly recognizes the focused pixels from source images, and then develop a new fusion scheme of the bandpass subband coefficients. The fused image can be obtained by inverse NSCT with the different fused coefficients. Several multifocus image fusion methods are compared with the proposed scheme. The experimental results clearly indicate the validity and superiority of the proposed scheme in terms of both the visual qualities and the quantitative evaluation.

Remote sensing Fourier transform spectrometers are required to be robust and portable. Introducing moving flat mirrors instead of cube corner refractors simplifies the spectrometer design and makes it more compact. Flat mirrors, however, introduce additional angular errors. We analyze the effect of tilted beams on spectral line shape and derive a mathematic equation for this effect based on the diffraction theory. A rough estimate equation for the intensity of two interfering beams based on the amount of tilt between them is presented. A case of a varying amount of tilt during moving mirror scanning is presented and various forms of mirror misalignment are analyzed.

The influence of a fiber dispersion calibration interferometer on the measurement results for a large-scale high-resolution broadband frequency-modulated continuous wave (FMCW) measurement system was studied. A model was constructed to simulate the influences of fiber dispersion on the measurements when using a frequency sampling method that corrects the tuning nonlinearity. The results indicated that a broadband external cavity tunable laser, in comparison with a semiconductor laser, causes linear variations in the measurement results because of the effect of the fiber dispersion in the calibration interference path for large-scale high-resolution measurements, and these variations decreased the resolution of the measurements. A method that combines chirp slope calibration and phase compensation to reduce the effects of the fiber dispersion was proposed. A gauge block with a height difference of 200  μm at a distance of 2.43 m was measured during the experiments. Before calibrating the fiber dispersion, the frequency spectrum showed false peaks, and it was difficult to distinguish the peaks of the targets. After compensating for the dispersion, the peaks of the targets could be clearly distinguished, and a height difference of 199.6  μm was measured. Using this model and the method to compensate for the dispersion will provide a reference for large-scale high-resolution broadband FMCW laser measurements.

Laser rangefinders are used in various electro-optical (EO) fire control systems. They often operate at eye-safe wavelengths around 1.55  μm, which extends their utility. The paper investigates the use of a modified eye-safe laser rangefinder at 1.55  μm to obtain information on atmospheric attenuation and couple that information to the performance of active and passive EO sensors with an emphasis of lower visibility conditions. Such information can be of great value both for estimating own sensor capabilities at a given moment as well as estimating the threat capability. One obvious example is ship defense where it is difficult to obtain visibility along variable and slant atmospheric paths, especially in darkness. The experimental equipment and the results from measurements of atmospheric backscatter along various atmospheric paths are presented. The backscatter curve is used to evaluate the extinction. These extinction values are compared with those deduced from a point visibility meter and from echo measurements against two similar nets positioned at two ranges from the sensor. TV and IR images of test targets along a 1.8 km path close to sea surface in the Baltic Sea were collected in parallel with the lidar. A weather station and a scintillometer collected weather and turbulence parameters. Results correlating the lidar attenuation with the imaging performance will be given.

Numerous methods are available to measure the spatial frequency response (SFR) of an optical system. A recent change to the ISO 12233 photography resolution standard includes a sinusoidal Siemens star test target. We take the sinusoidal Siemens star proposed by the ISO 12233 standard, measure system SFR, and perform an analysis of errors induced by incorrectly identifying the center of a test target. We show a closed-form solution for the radial profile intensity measurement given an incorrectly determined center and describe how this error reduces the measured SFR of the system. Using the closed-form solution, we propose a two-step process by which test target centers are corrected and the measured SFR is restored to the nominal, correctly centered values.

As is well-known, optical testing has begun to emerge as a limiting factor in the application of freeform surfaces. In all kinds of published freeform optical metrology, the tilted-wave-interferometer (TWI) is the precise and flexible method for testing a freeform surface as it can compensate the local surface’s deviation from its best fit sphere by using a set of tilted waves. In the process of measurement with TWI, accurate assessment of the test surface error from the fringes plays a key role. We present a method for evaluation and characterization of surface aberrations in TWI by combining computer-generated wave technology and a retrace errors elimination algorithm. The feasibility of the method is proved by the simulation and experimental results.