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This study aims to explore the application of computer simulation method based on light image processing in the monitoring of metabolic regulation data in exercise training. The study collected light image data during exercise training, including several parameters such as skin reflexes, heart rate response and skin temperature. Then, using computer simulation technology, the data were processed and analyzed to obtain metabolic regulation data during exercise. Finally, according to the analysis results, the training effect is evaluated and the training plan is adjusted. The results of the data analysis show that by monitoring the metabolic regulation data, the effect of exercise training can be more accurately evaluated and help adjust the training plan to achieve better training results. This method has the advantages of non-invasive, real-time and accurate, and is expected to be widely used in the field of sports training monitoring and regulation, and further improve the training effect and the formulation of individual training plans. [ABSTRACT FROM AUTHOR]

In the dynamic field of satellite imagery, the significance of super-resolution (SR) techniques, grounded on advanced deep learning methods, is paramount. A thorough understanding and remediation of the distinct challenges posed by various land cover types for image resolution enhancement form the essence of this research. This work diligently employs two unique neural networks, SRCNN and SwinIR Transformer, to scrutinize their varying impacts on a range of land cover types, ensuring a detailed and comprehensive exploration. This study transcends the mere enhancement of the Sentinel-2 dataset's resolution from 20 m/pix to 10 m/pix. It ambitiously seeks to excavate the intricate trends inherent to different land cover types and their corresponding interactions with SR processes. The application of neural networks on 255 × 254 pixel patches, covering six dominant types—forests, large fields, small fields, urban, sub-urban, and mixed—highlights substantial variations in metrics, underlining the individual interactions of each land cover type with SR techniques. A comprehensive accuracy assessment is meticulously conducted, employing an array of metrics and frequency domains to shed light on the nuanced differences and provide vital insights for optimizing each land cover type's SR approaches. Notably, the PSNR metric reveals significant disparities, particularly in the “forest” and “urban” categories for both SRCNN and SwinIR. According to the PSNR metric, the “forest” class yielded the best results with 66.06 for SRCNN and 67.00 for SwinIR, while the “urban” class marked the lowest with 55.09 and 57.02, respectively, reinforcing the critical nature of this study.

Wetlands play an important role in supporting biodiversity conservation and helping sustainable development. Time series wetland classification requires a series of sample sets acquired at different times. To meet this need, we proposed a method for automatically producing global wetland samples based on 13 global and regional wetland-related datasets and millions of images from Landsat 8 Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS), Sentinel-1 Synthetic Aperture Radar Ground Range Detected, and Sentinel-2 Multispectral Instrument sensors. On this basis, we analyzed the classification capabilities of wetland features in the above four sensors and obtained a multisensor, full spectral band, multi-index wetland classification feature dataset at a global scale. The analysis results show that the shortwave infrared band and land surface water index can distinguish nonwetlands from other land cover types well, while the modified normalized difference water index, normalized difference vegetation index, normalized difference water index, and thermal infrared bands could help distinguish water bodies and wetlands. C bands in Sentinel 1 perform well in identifying water bodies, but the ability to distinguish wetlands is limited. The mapping coefficients of the corresponding spectral bands and indexes between Sentinel-2, MODIS data, and Landsat were obtained to help realize the collaborative use of multisensor image data. This article could provide guidance for large-scale wetland classification and mapping research.

The in situ measurement of thick oil slick thickness (>0.5 mm) and area in real time in order to estimate the volume of an oil spill is very important for determining the oil spill response strategy and evaluating the oil spill disposal efficiency. In this article, a method is proposed to assess the volume of oil slicks by simultaneously measuring the thick oil slick thickness and area using ultrasonic inspection and image processing methods, respectively. A remotely operated vehicle (ROV), integrating two ultrasonic immersion transducers, was implemented as a platform to receive ultrasonic reflections from an oil slick. The oil slick thickness was determined by multiplying the speed of sound by the ultrasonic traveling time within the oil slick, which was calculated using the cross-correlation method. Images of the oil slick were captured by an optical camera using an airborne drone. The oil slick area was calculated by conducting image processing on images of the oil slick using the proposed image processing algorithms. Multiple measurements were performed to verify the proposed method in the laboratory experiments. The results show that the thickness, area and volume of a thick oil slick can be accurately measured with the proposed method. The method could potentially be used as an applicable tool for measuring the volume of an oil slick during an oil spill response. [ABSTRACT FROM AUTHOR]

The large-aperture static imaging spectrometer (LASIS) is an interference spectrometer with high device stability, high throughput, a wide spectral range, and a high spectral resolution. One frame image of the original data cube acquired by the LASIS shows the image superimposed with interference fringes, which is distinctly different from traditional hyperspectral images. For compression studies using this new type of data, a lossless compression scheme that combines a novel data rearrange method and the lossless multispectral and hyperspectral image compression standard CCSDS-123 is presented. In the rearrange approach, the LASIS data cube is rearranged such that the interference information overlapped on the image can be separated, and the results are then processed using the CCSDS-123 standard. Then, several experiments are conducted to investigate the performance of the rearrange method and examine the impact of different CCSDS-123 parameter settings for the LASIS. The experimental results indicate that the proposed scheme provides a 32.9% higher ratio than traditional rearrange methods. Moreover, an adequate parameter combination for this compression scheme for LASIS is presented, and it yields a 19.6% improvement over the default settings suggested by the standard. [ABSTRACT FROM AUTHOR]

• The proposed neural network-method can generate holograms significantly faster than conventional iterative algorithms, with the same or better reconstructed image quality. • Reconstruction of 3D-holograms with 16 object planes was achieved, which is the highest number of planes demonstrated using a neural network-based method to date. • A convolutional two branch neural network architecture that used one branch for generation of the phase information and one for generation of the amplitude information in the hologram reconstruction plane. • Each 2D-plane of a 3D-scene is separately propagated to the spatial light modulator (SLM) plane using such parameters as laser wavelength, SLM pixel size, and distance to the reconstruction plane, that can be independent between different planes. • Due to the integration of the propagation parameters into the training process, the network can generate holograms that can be reconstructed on random optical setups with different equipment. Computer generated holograms can create arbitrary light distributions through computation of light propagation. 3D-computer-generated-hologram generation requires significant computation time, especially so for 3D-computer-generated-holograms where it is important to calculate the interactions between different planes on top of the planes themselves. In this paper we propose a neural-network-based method for 3D-computer-generated-hologram generation in order to improve the hologram computation speed. The trained model can be used to generate holograms with arbitrary light propagation parameters. The neural network was numerically and optically tested against the GS algorithm using 3D-computer-generated-holograms with resolution up to 1024×1024 pixels. 3D-holograms with 16 object planes were generated, which is, to our knowledge, the highest number currently achieved with a neural-network-based-method. The experiments show that proposed model can create holograms significantly faster than some conventional algorithms and, overall, results better-quality images. The trained network can also be used using different propagation parameters, such as wavelength and focal distance. [ABSTRACT FROM AUTHOR]

• Optical analog computing overcomes the throughput and computational speed limitations of traditional digital computing. • Metasurface is an alternative to filters and lens to reduce size or volume. • A scheme for salient object detection in complex scenes is proposed. • The identification and segmentation for target in complex scenes are achieved in theory and simulation. Optical analog computing overcomes the throughput and computational speed limitations of traditional digital computing, facilitating salient object detection on platforms that rapidly process massive image data, such as optical remote sensing. Further device miniaturization and integration can be achieved by replacing filters in optical analog computing with metasurface. Based on the combination of optical analog computing and dielectric metasurface, we proposed an optical analog cross-correlation operation operating in the mid-infrared wavelength band to achieve the real-time and compact salient object detection. Even in extremely complex scenes, this cross-correlation operation can be used to identify and segment target patterns based on several target features accurately. The proposed optical analog cross-correlation operation may find applications in object tracking, medical image segmentation, and person re-identification. [ABSTRACT FROM AUTHOR]

The transport sector is experiencing a fast and revolutionary development, moving toward the deployment of autonomous vehicles. Thus, the accuracy of the position information and the integrity of the navigation system have become key factors. In this article, we address the problem of the enhancement of the integrity of the position provided by a global navigation satellite system receiver by exploiting sensor fusion. To this aim, we estimate the lateral offset and heading of the vehicle with respect to a georeferenced roadway centerline from the images supplied by an on-board camera. Moreover, we perform integrity monitoring based on the implementation of the solution separation in the parity space. The numerical results indicate that the use of sensor fusion and digital map allows us to attain a longitudinal protection level reduction with respect to the case in which sensor fusion is not exploited. More specifically, a decrease of about 70% is achieved when a single constellation is used, while reduction is less relevant, about 15%, when two constellations are employed. [ABSTRACT FROM AUTHOR]

A segmented primary mirror is very important for extra-large astronomical telescopes, in order to detect the phase error between segmented mirrors. Traditional iterative algorithms are hard to detect co−phasing aberrations in real time due to the long-time iterative process. Deep learning has shown large potential in wavefront sensing, and it gradually focuses on detecting piston error. However, the current methods based on deep learning are mainly applied to coarse phase sensing, and only consider the detection of piston error with no tip/tilt errors, which is inconsistent with reality. In this paper, by innovatively designing the form of pupil mask, and further updating the OTF in the frequency domain, we obtain a new decoupled independent feature image that can simultaneously detect the piston error and tilt/tilt error of all sub-mirrors, which is effectively decoupled, and eliminates the dependence of the data set on the imaging object. Then, the Bi−GRU network is used to recover phase error information with high accuracy from the feature image proposed in this paper. The network's detection accuracy ability is verified under single wavelength and broadband spectrum in simulation. This paper demonstrates that co−phasing errors can be accurately decoupled and extracted by the new feature image we proposed and will contribute to the fine phasing accuracy and practicability of the extended scenes for the segmented telescopes. [ABSTRACT FROM AUTHOR]

Quantization is widely used for compression, storage, transmission and display of computer-generated and digital holograms. It reduces number of hologram gradations to obtain better ratio of the quality of reconstructed 2D and 3D objects to the size of holographic images or videos. This paper proposes 2 methods of non-iterative quantization of holograms: adaptive non-uniform quantization based on histogram processing with peak shift (ANHS method) and additional application of logarithmic companding (ANHSL method). They were compared with 4 methods of iterative and non-iterative quantization on holograms with sizes up to 2048 × 2048 pixels. The proposed methods showed the best reconstruction quality that is higher up to 59 % in comparison with non-iterative uniform quantization. Especially the proposed methods give higher quality at little number of hologram gradations (from 2 to 16 gradations). In comparison with the most qualitative iterative methods (Lloyd-Max and k-means) the quality is increased up to 9 %, and processing time is reduced in 10–1000 times. [ABSTRACT FROM AUTHOR]

The large-aperture static imaging spectrometer (LASIS) is an interference spectrometer with high device stability, high throughput, a wide spectral range, and a high spectral resolution. One frame image of the original data cube acquired by the LASIS shows the image superimposed with interference fringes, which is distinctly different from traditional hyperspectral images. For compression studies using this new type of data, a lossless compression scheme that combines a novel data rearrange method and the lossless multispectral and hyperspectral image compression standard CCSDS-123 is presented. In the rearrange approach, the LASIS data cube is rearranged such that the interference information overlapped on the image can be separated, and the results are then processed using the CCSDS-123 standard. Then, several experiments are conducted to investigate the performance of the rearrange method and examine the impact of different CCSDS-123 parameter settings for the LASIS. The experimental results indicate that the proposed scheme provides a 32.9% higher ratio than traditional rearrange methods. Moreover, an adequate parameter combination for this compression scheme for LASIS is presented, and it yields a 19.6% improvement over the default settings suggested by the standard.

Object detection is an important task for rapidly localizing target objects using high-resolution satellite imagery (HRSI). Although deep learning has been shown an efficient means of detection, object detection in HRSI remains problematic due to variations in object scale and size. In this article, we present a novel deep neural network (DNN) that combines double-shot neural network with misplaced localization strategy that adapts to object detection tasks in satellite images. This novel architecture optimizes the localization of small and narrow rectangular objects, which frequently appear in HRSI images, without accuracy loss on other size and width/height ratio objects. This method outperforms other state-of-art methods. We evaluated our proposed method on the NWPU VHR-10 public dataset and a new benchmark dataset (seven classes of small and narrow rectangular objects, SNRO-7). The NWPU VHR-10 dataset built a dataset for multiclass object detection; however, most labels are assigned in normal size and width/height ratios. SNRO-7 focuses on multiscale and multisize object detection and includes many small-size and narrow rectangular objects. We also evaluated the accuracy difference on DNN training and testing between gray scale and RGB datasets. The results of the experiment on object detection reveal that the mean average precision (MaP) of our method is 82.6% in NWPU VHR-10 and 79.3% in SNRO-7, which exceeds the MaPs of other state-of-the-art object detection neural networks. The model trained with the RGB dataset can achieve similar accuracy (around 79.0% MIoU) testing in both RGB and gray scale datasets. When training the model by mixing RGB and gray scale datasets in different ratios, the accuracy in the RGB channel significantly decreases with increasing gray scale images, but this does not influence the accuracy in the gray scale dataset.

As one of the world's largest freshwater ecosystems, the Great Lakes Basin houses thousands of acres of wetlands that support a variety of crucial ecological and environmental functions at the local, regional, and global scales. Monitoring these wetlands is critical to conservation and restoration efforts; however, current methods that rely on field monitoring are labor-intensive, costly, and often outdated. In this article, we present a graphical user interface constructed in Google Earth Engine called the Wetland Extent Tool (WET), which allows semiautomatic wetland classification according to a user-input area of interest and date range. WET conducts multisource, moderate resolution processing utilizing Landsat 8 Operational Land Imager, Sentinel-2 MultiSpectral Instrument, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) datasets to classify wetlands in the entire Great Lakes Basin. We evaluated classification results of wetlands, uplands, and open water from May-September 2019, and tested whether SRTM elevation, slope, or the Dynamic Surface Water Extent produced the most accurate results in each Great Lake Basin in conjunction with optical indices and radar composites. We found that slope produced the most accurate classification in Lake Michigan, Huron, Superior, and Ontario, while elevation performed best in Lake Erie. Classification results averaged 86.2% overall accuracy, 70.0% wetland consumer's accuracy, and 82.7% wetland producer's accuracy across the Great Lakes Basin. WET leverages cloud-computing for multisource processing of moderate resolution remote sensing data, and employs a user interface in Google Earth Engine that wetland managers and conservationists can use to monitor wetland extent in the Great Lakes Basin in near real-time.

Abstract Monitoring of subgrade settlement in railway and highway helps to maintain traffic safety and reduce infrastructure losses. When an excess subgrade settlement is detected, maintenances of the unqualified subgrade will be processed in time, and early warning of constructions will be processed to prevent the occurrence of safety accident. In this article, a noncontact, real-time, and unattended system based on optical method is introduced to monitor the subgrade settlement remotely. Several high-power infrared LED targets are mounted on the subgrade and they will move as the subgrade subsides. The movements of the LED targets are detected by an optical detection device based on the principle of optical imaging. The displacement data is remotely transmitted to a server for analysis via GPRS. The remote monitoring system has been applied to the monitoring of subgrade settlement of Zhensheng highway in Guizhou province of China in 2014. This technology helped to detect the subgrade settlement that was caused by the construction of Beiyinpo railway tunnel. Four test points on road surface were monitored at a sampling rate every 20 minutes. The effectiveness of the system has been verified by the practical application.

Infrared sensors have been deployed in many video surveillance systems because of the insensibility of their imaging procedure to some extreme conditions (e.g. low illumination condition, dim environment). To reduce human labor in video monitoring and perform intelligent infrared video understanding, an important issue we need to consider is how to locate the object of interest in consecutive video frames accurately. Therefore, developing a robust object tracking algorithm for infrared videos is necessary. However, the infrared information may not be reliable (e.g. thermal crossover), and appearance modeling with only the infrared modality may not be able to achieve good results. To address these issues, with the wide deployment of RGB-infrared camera systems, this paper proposes an infrared tracking framework in which information from RGB-modality will be exploited to assist the infrared object tracking. Specifically, within the tracking framework, in order to deal with the contaminated features caused by large appearance variations, an online non-negative feature template learning model is designed. The non-negative constraint enables the model to capture the local part-based characteristic of the target appearance. To ensure more important modality contribute more in appearance representation, an adaptive modality importance weight learning scheme is also incorporated in the proposed feature learning model. To guarantee the model optimality, an iterative optimization algorithm is derived. The experimental results on various RGB-infrared videos show the effectiveness of the proposed method.

High-speed optical reconstruction of 3D-scenes can be achieved using digital holography with binary digital micromirror devices (DMD) or a ferroelectric spatial light modulator (fSLM). There are many algorithms for binarizing digital holograms. The most common are methods based on global and local thresholding and error diffusion techniques. In addition, hologram binarization is used in optical encryption, data compression, beam shaping, 3D-displays, nanofabrication, materials characterization, etc. This paper proposes an adaptive binarization method based on a combination of local threshold processing, hologram division into blocks, and error diffusion procedure (the LDE method). The method is applied for binarization of optically recorded and computer-generated digital holograms of flat objects and three-dimensional scenes. The quality of reconstructed images was compared with different methods of error diffusion and thresholding. Image reconstruction quality was up to 22% higher by various metrics than that one for standard binarization methods. The optical hologram reconstruction using DMD confirms the results of the numerical simulations.

Logic operation serves as the foundation and core element of computing networks; it will bring huge vitality to advanced information processing with its adaptation in the optical domain. As fundamental logic operations, AND and exclusive OR (XOR) operations serve a multitude of purposes, such as their ability to cooperate in enabling image processing and interpretation. Here, we propose and experimentally demonstrate a wavelength multiplexed AND and XOR function based on metasurfaces. By combining two cosine gratings with distinct frequencies and an initial phase difference of π/2, we extract the similarities and differences between two input images simultaneously by illuminating them with 445 and 633 nm wavelengths. Additionally, we explore its potential in information encryption, where overall security is enhanced by distributing distinct parts of initial information and encoded keys to different receivers. This design possesses the benefits of convenient mode switching and high-quality imaging, facilitating advanced applications in pattern recognition, machine vision, medical diagnosis, etc.

Monitoring of subgrade settlement in railway and highway helps to maintain traffic safety and reduce infrastructure losses. When an excess subgrade settlement is detected, maintenances of the unqualified subgrade will be processed in time, and early warning of constructions will be processed to prevent the occurrence of safety accident. In this article, a noncontact, real-time, and unattended system based on optical method is introduced to monitor the subgrade settlement remotely. Several high-power infrared LED targets are mounted on the subgrade and they will move as the subgrade subsides. The movements of the LED targets are detected by an optical detection device based on the principle of optical imaging. The displacement data is remotely transmitted to a server for analysis via GPRS. The remote monitoring system has been applied to the monitoring of subgrade settlement of Zhensheng highway in Guizhou province of China in 2014. This technology helped to detect the subgrade settlement that was caused by the construction of Beiyinpo railway tunnel. Four test points on road surface were monitored at a sampling rate every 20 minutes. The effectiveness of the system has been verified by the practical application. [ABSTRACT FROM AUTHOR]

We present simulation results of hypothetical satellite systems with fifth-order optical aberrations and various fractal apertures. On the image sensor side, we utilized novel metamaterial based image sensors (metaxels). Real physical systems always have some amount of optical aberrations even though electro-optical systems operating in space are generally idealized as diffraction limited in many simulations. Not only Seidel aberrations but also fifth-order aberrations are important in satellite image analyses and here we illustrate the effects of six aberration groups: spherical, linear coma, elliptical coma, oblique spherical, astigmatism-field curvature, and distortion. Our analyses yield optical diffraction and aberration effects all at once. Any combined form of aberrations could be investigated, which constitute an ample dimension for imaging chain and image quality studies. For a λ/2 aberration coefficient, quantitative comparison was provided, and for various fractal aperture cases image chain simulations are performed. Correlation coefficient values for the listed groups of aberration enabled us to quantify significance order of these fifth-order aberrations. Fifth-order distortion was the most dominant aberration affecting the image quality. In addition, we increased the fractal dimension of apertures and obtained smaller degradation in the images. The study ends with Wiener filter restoration results. Peak signal-to-noise ratio and correlation coefficient values for the restored images were higher than corresponding aperture cases as expected. [ABSTRACT FROM AUTHOR]

In an electronic imaging system, image is retrieved using a Charged Coupled Device (CCD). Its finite pixel size integrates the optical information and limits the spatial resolution, whereas pixel-pitch provides the sampling interval. Spatial resolution can be improved if pixels of smaller sizes are used or equivalently if Geometric Super-Resolution technique is achieved. In this paper, we are reporting use of the Negative rect mask (NRM) at image plane to achieve Geometric Super-resolution through sub-pixeling. The NRM is scanned over CCD in sub-pixel steps and image data is gathered. The target is assumed to be static during the scanning process. The data collected through CCD with NRM coding and the position information of the scanning mask provides a mean to render the geometrically super resolved image. The work is demonstrated in one and two dimensions with computer simulation. The results are also applicable on CMOS based imaging. [ABSTRACT FROM AUTHOR]

Nowadays coastline extraction and tracking of its changes become of high importance because of the climate change, global warming and rapid growth of human population. Coastal areas play a significant role for the economy of the entire region. In this paper we propose a new methodology for automatic extraction of the coastline using aerial images. A combination of a four step algorithm is used to extract the coastline in a robust and generalizable way. First, noise distortion is reduced in order to ameliorate the input data for the next processing steps. Then, the image is segmented into two regions, land and sea, through the application of a local threshold to create the binary image. The result is further processed by morphological operators with the aim that small objects are being eliminated and only the objects of interest are preserved. Finally, we perform edge detection and active contours fitting in order to extract and model the coastline. These algorithmic steps are illustrated through examples, which demonstrate the efficacy of the proposed methodology. [ABSTRACT FROM AUTHOR]

The idea of using a single-pixel photodetector to sense the world may sound a little bit ambitious. However, a new type of computational imaging technology termed computational ghost imaging (CGI), is indeed making it a reality. No longer satisfied with using a non-spatially resolved photodetector to "see" the target, the computationally convolutional ghost imaging (CCGI) proposed in this paper can directly "see" the target's features of interest without imaging first anymore. Rather than a conventional 4-f optical system, the CCGI scheme completes the convolution operations by optical methods with a single-pixel photodetector and an engineered structured illumination. Meanwhile, our CCGI scheme can adaptively work under sub-Nyquist sampling conditions, and it can facilitate real-time non-imaging edge detection of the real scene. With some multiplexing schemes, the prototype of CCGI has the potential as a new type of single-pixel computer vision that might be used as an optical frontend of a lightweight convolutional neural network to recognize objects intelligently. Our scheme brings new insights into both the convolution operation and the CGI technology, greatly broadening the application scenarios of CGI. [ABSTRACT FROM AUTHOR]

Phase correlation (PC) is a well-established image matching algorithm. It is robust to the variation of image contrast and brightness, but whether it is invariant to local illumination change, particularly sun angle change, is yet to be investigated. This paper presents our study to prove and demonstrate the robustness of illumination invariance of the PC algorithm via mathematical analysis and image matching experiments. First, a 3-D space named slope-aspect-intensity (SAI) is proposed to characterize the 3-D relationship between image intensity and terrain slope/aspect angles for a given illumination geometry. Based on the SAI space, the mathematical relationship between PC cross-power spectra and illumination direction (e.g., solar azimuth and zenith angles) is analyzed. The impact of illumination angle variation between two images for matching is then rigorously investigated via experiments using simulated terrain shading images from a digital elevation model and real optical images taken under different sun illumination conditions. Our study confirms that PC is robustly invariant to illumination and therefore can achieve reliable matching between images taken under different solar illumination conditions for various remote sensing applications. [ABSTRACT FROM PUBLISHER]

A large proportion of individuals who live with type-2 diabetes suffer from plantar sensory neuropathy. Regular testing and assessment for the condition is required to avoid ulceration or other damage to patient's feet. Currently accepted practice involves a trained clinician testing a patient's feet manually with a hand-held nylon monofilament probe. The procedure is time consuming, labor intensive, requires special training, is prone to error, and repeatability is difficult. With the vast increase in type-2 diabetes, the number of plantar sensory neuropathy sufferers has already grown to such an extent as to make a traditional manual test problematic. This paper presents the first investigation of a novel approach to automatically identify the pressure points on a given patient's foot for the examination of sensory neuropathy via optical image processing incorporating plantar anthropometry. The method automatically selects suitable test points on the plantar surface that correspond to those repeatedly chosen by a trained podiatrist. The proposed system automatically identifies the specific pressure points at different locations, namely the toe (hallux), metatarsal heads and heel (Calcaneum) areas. The approach is generic and has shown 100% reliability on the available database used. The database consists of Chinese, Asian, African, and Caucasian foot images. [ABSTRACT FROM AUTHOR]

In this paper, we present a new method for detecting in-plane displacements in microelectromechanical systems (MEMS) with an unprecedented sub-ångström accuracy. We use a curve-fitting method that is commonly employed in spectroscopy to find peak positions in a spectrum. We fit a function to the intensity profile of the image of a silicon beam that was captured with a CCD camera on an optical microscope. The position resolution depends on the amount of pixel noise and on how the moving feature is spread across the detector pixels. The resolution is usually limited by photon shot noise, which can be controlled and lowered in several ways. To demonstrate the technique we measure the adhesion snap-off of two silicon surfaces. We assess the accuracy of the technique using two different silicon MEMS devices and an experimental ultrananocrystalline diamond device. The lowest position noise that we report is obtained by summing 1 577 image lines and is as low as 60 pm root mean square. [2014-0040] [ABSTRACT FROM AUTHOR]

adjacent effect caused by a nonideal system point spread function (PSF) is an important source generating additional errors in radiometric measurements. Traditional recovery methods, i.e., inverse and Wiener filters, are attempted to restore the scene without the influence of RSF, which is inexistent in real observations and usually leads to the ringing artifact as well as uncertainties in noise amplification (NA). In this paper, a novel spatial-response matched filter (SRMF) and its processing method are established, where the aimed scene is supposed to be observed by instruments with higher PSF performance. Also, NA effects triggered by SRMF are precisely modeled and used for the further evaluation of radiometric accuracy (RA) variation after SRMF processing. Simulation results indicate that the SRMF method is more suitable for images with lower noise level and greater variation in the targets' radiation, where RA performance could be improved in a certain extent. Meanwhile, when the imager in a Multifunctional Transport Satellite is selected as the reference one with high-performance PSF, the SRMF for Fengyun-2 satellite is set up and applied for observations in thermal infrared band during April andMay 2013. After SRMF processing, the recovered images show significant improvements in both visual effects and RA, where the increase of RA is expected to be 2-6 K at 190 K in statistics. Such a progress is believed to be beneficial to tropical cyclone intensity estimation as well as for other relevant products, i.e., cloud classification generation. [ABSTRACT FROM AUTHOR]

This paper presents a method for predicting positions of color cubes inside a square transparent solid object from images taken at the orthogonal boundary surfaces. The work is developed for use in mapping flow of non-aqueous phase liquids (NAPL) in transparent soils. Transparent soil models have been developed to study the flow of contaminants through porous media, in bench scale tests. Yellow transparent cubes are used to represent NAPL plumes and clear transparent cubes are used as representations of transparent soil in order to definitively validate the algorithm. Color space information is used to relate concentration and image intensity. The new algorithm employs a so-called 3D carving method to iteratively reconstruct a 3D model using images taken at three orthogonal boundaries. The methodology presented in this paper is a fast, relatively accurate, non-intrusive and inexpensive method for quantifying NAPL zones in transparent soil models. [ABSTRACT FROM AUTHOR]

Finding a correlation between image pixel information and non-aqueous phase liquid (NAPL) saturation is an important issue in bench-scale geo-environmental model studies that employ optical imaging techniques. Another concern is determining the best dye color and its optimum concentration as a tracer for use in mapping NAPL zones. Most bench scale flow studies employ monochromatic gray-scale imaging to analyze the concentration of mostly red dyed NAPL tracers in porous media. However, the use of grayscale utilizes a third of the available information in color images, which typically contain three color-space components. In this study, eight color spaces consisting of 24 color-space components were calibrated against dye concentration for three color-dyes. Additionally, multiple color space components were combined to increase the correlation between color-space data and dyed NAPL concentration. This work is performed to support imaging of NAPL migration in transparent synthetic soils representing the macroscopic behavior of natural soils. The transparent soil used in this study consists of fused quartz and a matched refractive index mineral-oil solution that represents the natural aquifer. The objective is to determine the best color dye concentration and ideal color space components for rendering dyed sucrose-saturated fused quartz that represents contamination of the natural aquifer by a dense NAPL (DNAPL). Calibration was achieved for six NAPL zone lengths using 3456 images (24 color space components×3 dyes×48 NAPL combinations) of contaminants within a defined criteria expressed as peak signal to noise ratio. The effect of data filtering was also considered and a convolution average filter is recommended for image conditioning. The technology presented in this paper is fast, accurate, non-intrusive and inexpensive method for quantifying contamination zones using transparent soil models. [ABSTRACT FROM AUTHOR]

Abstract: A novel optical encryption method is proposed involving double image encryption in which one image is introduced as the pseudo image while the other is the original object image. The Double Random Phase Encoding technique is used to encrypt both the pseudo and object images into complex images. A unique binary image is then employed to first generate the random phase key for the object image encryption and then to embed the encrypted object image into the encrypted pseudo image, which acts as host image. Both the second random phase mask used for encoding the pseudo image and the binary image act as encryption keys. If an attacker attempts to crack the random phase key and decrypt the original object image, the pseudo image will be obtained instead. Simulation results and robustness tests are performed which demonstrate the feasibility of the algorithm. [Copyright &y& Elsevier]

Abstract: In this paper we review a number of optical image encryption techniques proposed in the literature inspired by the architecture of the classic optical Double Random Phase Encoding (DRPE) system. The optical DRPE method and its numerical simulation algorithm are first investigated in relation to the sampling considerations at various stages of the system according to the spreading of the input signal in both the space and spatial frequency domains. Then the several well-known optically inspired encryption techniques are examined and categorized into all optical techniques and image scrambling techniques. Each method is numerically implemented and compared with the optical DRPE scheme, in which random phase diffusers (masks) are applied after different transformations. The optical system used for each method is first illustrated and the corresponding unitary numerical algorithm implementation is then investigated in order to retain the properties of the optical counterpart. The simulation results for the sensitivities of the various encryption keys are presented and the robustness of each method is examined. This overview allows the numerical simulations of the corresponding optical encryption systems, and the extra degree of freedom (keys) provided by different techniques that enhance the optical encryption security, to be generally appreciated and briefly compared and contrasted. [Copyright &y& Elsevier]

On the verge of the 50th anniversary of Vander Lugt's formulation for pattern matching based on matched filtering and optical correlation, we acknowledge the very intense research activity developed in the field of correlation-based pattern recognition during this period of time. The paper reviews some domains that appeared as emerging fields in the last years of the 20th century and have been developed later on in the 21st century. Such is the case of three-dimensional (3D) object recognition, biometric pattern matching, optical security and hybrid optical-digital processors. 3D object recognition is a challenging case of multidimensional image recognition because of its implications in the recognition of real-world objects independent of their perspective. Biometric recognition is essentially pattern recognition for which the personal identification is based on the authentication of a specific physiological characteristic possessed by the subject (e.g. fingerprint, face, iris, retina, and multifactor combinations). Biometric recognition often appears combined with encryption-decryption processes to secure information. The optical implementations of correlation-based pattern recognition processes still rely on the 4f-correlator, the joint transform correlator, or some of their variants. But the many applications developed in the field have been pushing the systems for a continuous improvement of their architectures and algorithms, thus leading towards merged optical-digital solutions. [ABSTRACT FROM AUTHOR]

Abstract: We report some electric field controlled photorefractive higher-order diffraction phenomena of a paraelectric phase potassium lithium tantalate niobate crystal doped with iron. In experiments, a p-polarized semiconductor laser (532nm) was used to record grating at a small incident angle. Higher-order diffraction images were observed when the signal beam was focused behind and in front of the crystal. Then the higher-order diffraction images were reconstructed by a p-polarized He–Ne laser (632.8nm) in the direction perpendicular to the surface. The higher-order diffraction images could be controlled by the external electric field. A theory about the higher-order diffraction images of the K and 2 K grating is developed. The results show that the even order diffraction images of the K grating and the odd order diffraction of the 2 K grating overlap each other. The odd order diffraction images of the K grating are diffracted in unattached direction. The electric field controlled higher-order diffraction image provides a useful method for optical information processing. [Copyright &y& Elsevier]

Shadows in remote sensing images often cause problems, especially in land-cover classification and change detection. Hence, it is very useful if the radiance of shadow areas is corrected to the same level as that of shadow-free areas. In this study, a radiance measurement was carried out to investigate the spectral characteristics of sunlight. Then a method is proposed for shadow detection and correction of optical imagery. First, building shadow areas are detected using an object-based classification method that employs brightness values and their relationship with the neighboring area. Next, the detected shadow areas are corrected using a linear function to produce a shadow-free image. The shadow pixels with different darkness levels are corrected by using different ratios to obtain a smoothly restored image. The proposed semi-automated method was applied to a QuickBird and a WorldView-2 images of Tokyo, Japan, to demonstrate the effectiveness of the method. [ABSTRACT FROM PUBLISHER]

This paper presents an experimental study using laser mouse sensors for the contactless revolutions per minute (RPM) measurement of a rotating shaft. The sensor performance characterization experiment is firstly conducted under different parameter setups. After the optimal parameter value has been found, the rotor RPM experiment is then conducted with a speed sweep from 500 to 3800 rpm, and data are gathered at 30 different speeds and processed using two different methods to convert the sensor readings into the RPM of the rotating shaft; the results are then displayed. The performance differences between the two methods are compared, and the observation is that both the linearity and the signal-to-noise ratio of the frequency correlation method are several times better than those for the amplitude correlation method. The conclusion summarizes the experimental results and the advantage associated with this new RPM sensing method and provides the motivation for its potential applications and its future works. [ABSTRACT FROM PUBLISHER]

Ever-expanding networks of surf cameras offer a unique opportunity to monitor the coastline over large expanses at very little cost compared to traditional in situ survey methods. Here, we describe and test a new coastal monitoring system maintained by CoastalCOMS Pty Ltd. at their test site at Gold Coast, Australia. The two-camera system monitors two highly sensitive 4-km stretches of sandy coastline adjacent to high-value assets. The traditional static multi-camera setup has been replaced by a single rotational camera. A 14-month data set, encompassing one major storm, a recovery period, and a seasonal cycle, was analyzed. Positive shoreline detections using the new camera system were available 64% of the time (roughly 145 days of the available 226, where daily offshore significant wave heights Hs ≤ 1 m). Comparison of the CoastalCOMS-derived shorelines and in situ survey data showed a mean shoreward bias of 25.5 m. Daily shoreline estimates were used to calculate weekly and five-week running mean beach widths at both sites. Analysis showed that both sites eroded between 15-22 m during the May 2009 storm and then recovered during the proceeding five-month calm period. Distinct intersite variability was observed between the more exposed Northern Beaches that displayed an annual shoreline cycle and very little intrasite variability and the more sheltered southern Palm Beach site that displayed large intrasite spatial variability and sensitivity to changes to both wave direction and wave height. [ABSTRACT FROM AUTHOR]

Abstract: We present an optical processor capable to perform partial second derivatives of an image working with partially coherent illumination. The proposed system utilizes a liquid crystal display, onto which images to be derived are displayed. The method is based on the capacity of these devices to generate simultaneously a contrast reverted replica of the image displayed on it. A positive replica of original image is obtained when the LCD is between crossed polarizers, while the negative one is obtained with the LCD is between parallel polarizers. Since the LCD is a diffraction element, both polarizer arrangements may be simultaneously implemented, for example, in a 4f optical processor using polarization filtering of the different diffraction orders. When three images (two replicas of original image displaced in opposite direction and the other one with contrast reverted and centered at the origin.) are imagined across a slightly defocused plane, one obtains an image with enhanced second derivatives in a given direction. No other incoherent (or partially coherent) optical system is presently known which can perform partial second-order derivatives of an image. Experimental results obtained are presented. [Copyright &y& Elsevier]

Abstract: An optical setup is proposed for the implementation of compressive sensing with coherent images. This setup specifically exploits the natural multiplicative action of speckle noise occurring with coherent light, in order to optically realize the essential step in compressive sensing which is the multiplication with known random patterns of the image to be acquired. In the test of the implementation, we specifically examine the impact of several departures, that exist in practice, from the ideal conditions of a pure multiplicative action of the speckle. In such practical realistic conditions, we assess the feasibility, performance and robustness of the optical scheme of compressive sensing. [Copyright &y& Elsevier]