Close-range Imaging Research Articles

Endoscopy and endoscopic ultrasound for embedded or migratory digestive foreign body.

Embedded or migratory ingested foreign bodies (FBs) may be a disaster when not found intraluminally and can be seriously life-threatening. Endoscopic ultrasound (EUS) has advantages of transmural, close range and real-time imaging. Under its guidance, minimally invasive endoscopic removal of FBs and abscess drainage become available and safe to avoid unnecessary surgery. However, relatively few diagnostic applications of EUS have been reported. Endoscopic removal and abscess drainage with or without EUS-guidance were even scarce. We found 21 cases of migratory FBs reported in which EUS was used to diagnose and/or treat including our case. We further summarized clinical characteristics and EUS manifestations of embedded or migratory FBs for future differentiation, and treatment strategies by endoscopy and EUS. EUS is a valuable tool in recognizing and differentiating embedded or migratory FBs. EUS-guided endoscopic removal is a minimally invasive, safe and innovative solution. EUS with novel device also assists in sufficient and secure drainage for FBs related abscess collections. A multidisciplinary team consult is sometimes mandatory for complicated cases. This would break a conceptual barrier in therapeutic endoscopy.

Close-range imaging for green roofs: Feature detection, band matching, and image registration for mixed plant communities

Green roofs offer ecological benefits but harsh rooftop environment stressors like heat, water scarcity, and wind can limit optimal plant growth. Monitoring plant health is crucial for optimizing green roof performance and remote sensing provides a non-destructive approach, yet challenges persist in tight, urban settings. This allows for close-range ground monitoring as a viable solution in these emerging environments. Recent studies explored automatic image registration techniques, showing promise in homogeneous settings, but faced uncertainties in mixed species systems or early plant growth stages. Feature detection techniques such as Speeded up Robust Features (SURF) using fixed and moving image spaces have been proposed for alignment. This study aimed to assess feature detection, matching, and image registration techniques in mixed species plant communities on green roofs throughout the growing season. This study developed a sophisticated monitoring system using close-range multispectral sensors for aligning bands in mixed species plant communities, contributing to enhanced green roof management and sustainability. The results of the study showed no significant differences in band alignment accuracy between growth stages for mixed species bush bean/sedum and single species sedum modules, but significant differences were found in single species bush bean systems. Additionally, mixed species modules showed better accuracy compared to single species bush bean modules, indicating that heterogenous systems provide better alignment accuracy due to more diverse feature extractions.

Energy Efficiency Measurement of Mechanical Crushing Based on Non-Contact Identification Method

The efficiency of mechanical crushing is a key metric for evaluating machinery performance. However, traditional contact-based methods for measuring this efficiency are unable to provide real-time data monitoring and can potentially disrupt the production process. In this paper, we introduce a non-contact measurement technique for mechanical crushing efficiency based on deep learning algorithms. This technique utilizes close-range imaging equipment to capture images of crushed particles and employs deeply trained algorithmic programs rooted in symmetrical logical structures to extract statistical data on particle size. Additionally, we establish a relationship between particle size and crushing energy through experimental analysis, enabling the calculation of crushing efficiency data. Taking cement crushing equipment as an example, we apply this non-contact measurement technique to inspect cement particles of different sizes. Using deep learning algorithms, we automatically categorize and summarize the particle size ranges of cement particles. The results demonstrate that the crushing efficiencies of ore crushing particles, raw material crushing particles, and cement crushing particles can respectively reach 80.7%, 70.15%, and 80.27%, which exhibit a high degree of consistency with the rated value of the samples. The method proposed in this paper holds significant importance for energy efficiency monitoring in industries that require mechanical crushing.

Simulation and experimental investigation of the effects of subdicing on a single element transducer

Structure design of medical ultrasonic transducers is of prime importance, as it not only affects the transducer's imaging performance, but also limits its application scene and scope. In this paper, a single element transducer with subdicing is fabricated and its performance is studied using finite element method. In addition, the comparison transducers (CPTs) are fabricated as well as single element transducers with and without subdicing the element. The experimental results show that by subdicing the element with cut width 120 μm and cut depth 500 μm of the single transducer with a center frequency of 8 MHz and dimension of 2 mm × 0.9 mm × 1.2 mm, the center frequency reduced by up to 58.3 %, the −6 dB bandwidth increased by up to 48.7 %, but the echo signal amplitude reduced by 52.4 %. Compared to CPT-5, the volume of the subdicing transducer reduced by 94.6 %, the center frequency reduced by 4.3 %, the −6 dB bandwidth increased by 1.26 times, the transmitted pulse signal length decreased by 50 %, and the echo signal amplitude achieved 28 % of CPT-5. Therefore, the proposed subdicing transducer may have great potential for miniaturization and close-range imaging.

A Hybrid GPU and CPU Parallel Computing Method to Accelerate Millimeter-Wave Imaging

The range migration algorithm (RMA) based on Fourier transformation is widely applied in millimeter-wave (MMW) close-range imaging because of its few operations and small approximation. However, its interpolation stage is not effective due to the involved intensive logic controls, which limits the speed performance in a graphics processing unit (GPU) platform. Therefore, in this paper, we present an acceleration optimization method based on the hybrid GPU and central processing unit (CPU) parallel computation for implementing the RMA. The proposed method exploits the strong logic-control capability of the CPU to assist the GPU in processing the logic controls of the interpolation stage. The common positions of wavenumber-domain components to be interpolated are calculated by the CPU and stored in the constant memory for broadcast at any time. This avoids the repetitive computation consumed in a GPU-only scheme. Then the GPU is responsible for the remaining matrix-related steps and outputs the needed wavenumber-domain values. The imaging experiments verify the acceleration efficiency of the proposed method and demonstrate that the speedup ratio of our proposed method is more than 15 times of that by the CPU-only method, and more than 2 times of that by the GPU-only method.

Improved Threat Detection in Walk-Through Security Scanning Using an Optimized Polarimetric Target Decomposition Method

Reliable and convenient walk-through security scanning, which doesn't separate people or impede their movement, is an extremely challenging task. In this paper, a novel approach for a security check with an overhead observation and a polarimetric target decomposition is presented. The viewing angle of the scanner equals a side-looking airborne radar. However, it will be shown that the established polarimetric target decomposition methods of remote-sensing are not well suited for close-range radar imaging and need to be adapted due to the differences in the geometry of the imaging scenario. The usage of a multiple input and multiple output (MIMO) array and the shorter distance between array and target need a changed decomposition technique in order to distinguish between persons with or without worn threat objects. The differences between radar remote-sensing and close-range imaging scenarios are investigated. An optimized version of the Sato four-component polarimetric scattering decomposition is derived. The proposed close-range adjustment is applied to the model and investigated experimentally with a 4-to-12 GHz fully polarimetric MIMO imaging system. Polarimetric decomposition is carried out on defined test structures with known scattering mechanisms, as well as on mannequins and persons with different threat objects like guns or explosives. In test campaigns, promising results were achieved for a correct target decomposition in radar-based walk-through security scanning.

Transmit–receive isolation improvement by a switch sequence adjustment for MMW linear-arrays imaging

High transmit-receive isolation between any two adjacent elements in sequentially switched linear arrays guarantees the performance of high-resolution close-range imaging. However, the transmit-receive antenna spacing required in wavelength order makes its isolation deteriorated rapidly in the millimeter wave and higher frequency bands. In this paper, we propose a simple yet effective method to improve transmit-receive isolation by a switch sequence adjustment on the traditional linear arrays. The new switch-mode aims to simply increase the vertical spacing between each transmit-receive pair. To maintain the densely uniform-spatial effective sampling and Fourier transform-based image reconstruction, the principle of equivalent phase center is utilized to analyze the maximum of the possible vertical spacing of each transmit-receive pair is four wavelengths. Experiments verify that the method can efficiently improve transmit-receive isolation by up to 11.5 dB and guarantee the high performance of MMW imaging.

Accurate Range Migration for Fast Quantitative Fourier-Based Image Reconstruction With Monostatic Radar

Range migration (or range focusing) techniques are widely used in optical, acoustic, and microwave real-time image reconstruction methods. They have been successfully applied to far-field 3-D imaging where they rely on plane-wave assumptions, which ignore the data amplitude variation over the acquisition aperture. The accuracy of the plane-wave assumption, however, quickly degrades in close-range imaging, where amplitude variations are significant and where the range to the target is on the order of the range sampling step. Here, we present a range-focusing method of improved accuracy, which is applicable to both far-zone and close-range monostatic radar. It refocuses the system point-spread function (PSF) to any range location, taking into account both magnitude and phase changes. The approach can be applied with any Fourier-based imaging algorithm utilizing the Lippmann–Schwinger equation as the underlying scattering model. Here, it is validated through examples based on simulated and measured data where the images are reconstructed with quantitative microwave holography (QMH). QMH employs measured PSFs to achieve quantitative imaging in real-time. The proposed range-migration method is applicable with measured PSFs, too, leading to reduced system-calibration effort and the ability to focus an image at any desired range.

On the Adaptation of an AUV into a Dedicated Platform for Close Range Imaging Survey Missions

This study presents the redesign of an existing autonomous underwater vehicle (AUV) with limited maneuverability, transforming it into a platform optimized for autonomous, near-seabed visual imaging missions. This work describes the enhancement of the AUV’s maneuverability through the addition of thrusters, the leveraging of a state-of-the-art thrust allocation algorithm, and the development of both a path-following controller and a dedicated imaging system. The performance of the optimized platform is demonstrated in a simulation and in actual real sea visual survey missions.

Documenting Cultural Heritage in very hostile fruition contexts: the synoptic visualization of Giottesque frescoes by Multispectral and 3D Close-range Imaging

Abstract The paper reports the results obtained from the digital survey of Giottesque frescoes in the Archiepiscopal Palace of Milan. Multispectral Imaging and Image Based Modelling techniques have allowed the study, the high-resolution documentation and the virtual reassembly of the fragmented frescoes: the integration of spectral data with RGB ortho-images and 3D models has supported the interpretation of the pictorial cycle by virtually relocating the fresco fragments and evaluating their consistency from a technical, iconographic and art-historical point of view. The experimental results show that the proposed approach has great potential for the documentation of wall paintings preserved in highly hostile contexts of fruition. Due to its versatility and portability in the field, its sub-millimetric accuracy, and the different outputs that can be generated, Close-Range Digital Photogrammetry has proven to be the ideal tool in case of scattered and difficult-to-access data.

Interior of top-shaped asteroids with cohesionless surface

Multiple sources of evidence suggest that asteroids ranging from hundreds meters to few kilometers in size are rubble piles, i.e. gravitational aggregates of loosely consolidated material. However, no direct data on their internal structure is available to date. Cohesion between rubble-pile building blocks has been invoked in the past to explain the stability of top-shaped asteroids, which in most cases would not be capable of maintaining their large-scale shape features (low flattening, and a pronounced equatorial ridge) otherwise. However, the physical origin of cohesion is unclear and there is no direct evidence of it. Recent close-range imaging and local sampling of the surfaces of top-shaped Near Earth Asteroids (NEA) suggest the presence of very porous surface structure with minimal strength and nearly no cohesion. This raises new questions about the internal structure of such objects, with important implications on their origin and evolution. Here we show by numerically simulating the dynamics of irregular rocky fragments, that the presence of a rigid core within the asteroid’s rubble-pile structure can explain the top shape and surface features observed recently on Bennu and Ryugu, without the need of cohesion between building blocks. Also, we find that the rigid core model produces more easily equatorial mass shedding, which is thought to be responsible for satellite formation. The presence of a rigid core has never been revealed so far, but is consistent with the accretion history of those objects, and with recent estimates of their internal mass distribution. Our findings will be tested directly by ESA’s Hera mission, which will scan the interior of Dimorphos, the small moon of Didymos binary system, providing for the first time direct data on the interior of a NEA.

Linking the Spectra of Decomposing Litter to Ecosystem Processes: Tandem Close-Range Hyperspectral Imagery and Decomposition Metrics

Efforts to monitor terrestrial decomposition dynamics at broad spatial scales are hampered by the lack of a cost-effective and scalable means to track the decomposition process. Recent advances in remote sensing have enabled the simulation of litter spectra throughout decomposition for grasses in general, yet unique decomposition pathways are hypothesized to create subtly different litter spectral signatures with unique ecosystem functional significance. The objectives of this study were to improve spectra–decomposition linkages and thereby enable the more comprehensive monitoring of ecosystem processes such as nutrient and carbon cycles. Using close-range hyperspectral imaging, litter spectra and multiple decomposition metrics were concurrently monitored in four classes of naturally decayed litter under four decomposition treatments. The first principal component accounted for approximately 94% of spectral variation in the close-range imagery and was attributed to the progression of decomposition. Decomposition-induced spectral changes were moderately correlated with the leaf carbon to nitrogen ratio (R2 = 0.52) and sodium hydroxide extractables (R2 = 0.45) but had no correlation with carbon dioxide flux. Temperature and humidity strongly influenced the decomposition process but did not influence spectral variability or the patterns of surface decomposition. The outcome of the study is that litter spectra are linked to important metrics of decomposition and thus remote sensing could be utilized to assess decomposition dynamics and the implications for nutrient recycling at broad spatial scales. A secondary study outcome is the need to resolve methodological challenges related to inducing unique decomposition pathways in a lab environment. Improving decomposition treatments that mimic real-world conditions of temperature, humidity, insolation, and the decomposer community will enable an improved understanding of the impacts of climatic change, which are expected to strongly affect microbially mediated decomposition.

Investigating the Kokhanovsky snow reflectance model in closerange spectral imaging

The internal structure of the snow and its reflectance function play a major contribution in its appearance. We investigate the snow reflectance model introduced by Kokhanovsky and Zege in a close-range imaging scale. By monitoring the evolution of melting snow through time using hyperspectral cameras in a laboratory, we estimate snow grain sizes from 0.24 to 8.49 mm depending on the grain shape assumption chosen. Using our experimental results, we observe differences in the reconstructed reflectance spectra with the model regarding the spectra's shape or magnitude. Those variations may be due to our data or to the grain shape assumption of the model. We introduce an effective parameter describing both the snow grain size and the snow grain shape, to give us the opportunity to select the adapted assumption. The computational technique is ready, but more ground truths are required to validate the model.

Close-range, heavy-duty hyperspectral imaging for tracking drought impacts using the PROCOSINE model

Leaf response to stressors is heterogeneously distributed within the canopy, creating complex light-matter interactions that hinder translation of spectra into meaningful plant traits at larger scales. A deeper understanding of the linkages between leaf traits and canopy spectra has been achieved by a variety of close-range hyperspectral imaging systems. Yet the majority are scaled for small plants in pots, and thereby face numerous challenges in replicating field conditions for temporal drought monitoring. In monocots, drought alters foliar biochemistry and canopy structure, leading to the eventual senescence and decay of individual leaves, yet few radiative transfer models have been applied to track drought stress at the submillimeter scale in mixed canopies of photosynthetic and non-photosynthetic vegetation. Our objective was to compare the retrieved parameter set for PROCOSINE and PROSPECT radiative transfer model inversions utilizing two decay pigments. Although the average inversion values for both models indicated an increase in initial decay pigments after drought, the per-pixel inversion results for the PROCOSINE and PROSPECT inversions were unique (Mann-Whitney U-test p<0.05). Model retrievals for PROCOSINE were consistently higher for N, and lower for chlorophyll and carotenoids. In PROCOSINE, the Thatai and BSpec trended towards smaller values due to the increasing percentage of non-photosynthetic leaves, suggesting that mixed canopies are less prone to bidirectional reflectance. Regardless, the spatial-temporal trend of the inversion estimated parameter set was similar between both models, demonstrating the efficacy of the close-range imaging platform to monitor drought effects over time.

New Power Technology for Venus Aerial Missions

In-situ exploration of Venus is challenging due to its severe environment, which is benign (25oC) at an altitude of 55 km, but rapidly becoming more hostile at lower altitudes. The temperature increases at ~7oC/km to ~465°C, with the pressure reaching 90 bars at the surface.1 These challenging conditions have limited in-situ exploration missions to high altitude balloons at 55 km (above the clouds) that lasted for 48 h, or even shorter duration surface missions that survived for two hours.2,3 The high-altitude (55-65km) balloon missions are stymied by the opaqueness of the Venusian clouds, which underlines the need for more long-duration in-situ missions for a better understanding of the Venus atmosphere across the cloud layers and below, as recommended by the Venus science community, Venus Exploration Analysis Group (VEXAG).4 Long-duration variable-altitude balloons (VABs) extending below the clouds have gained particular interest. Durable VABs would allow i) long-term measurements across Venus clouds, ii) determination of chemical species and isotopes underneath the clouds, iii) transport to different longitudes on the planet and measure atmospheric flow patterns, especially with the altitude control, iv) probing the interior structure through close-range imaging, and v) investigation of the seismic activity from acoustic measurements at various altitudes. For these missions, conventional power technologies are inadequate. For example, the performance of photovoltaics (PV) is hampered by the decreasing solar flux deeper in the clouds, the selective loss of short wavelength radiation, and the performance loss from the high temperatures.5 An energy storage system tolerant to high temperatures is needed to compensate for the reduced power generation of PVs at low altitudes, and to support nighttime operations for the VABs. In this paper, we will describe a novel ‘Venus Interior Probe using in-situ Power and Propulsion (VIP-INSPR) architecture we have been developing under NASA-NIAC (Novel Innovative and Advanced Concepts) program for sustained Venus atmospheric exploration. The probe concept utilizes: i) PV as a power source to the probe at high altitudes, and to electrolyze water carried from ground using regenerative solid oxide fuel/ electrolysis cell (SOEC), ii) Solid oxide fuel cell (SOFC)6 to provide power at low altitudes, iii) hydrogen storage bed for on-demand storage or release of hydrogen,7 iv) and a balloon filled with hydrogen and with hydrogen buoyancy-based altitude control system. Both H2 and O2 would be regenerated through electrolysis of the water produced in the fuel cell (a closed–system) at high altitudes. Acknowledgments: This work presented here was carried out at the Jet Propulsion Laboratory, California Institute of Technology under a contract with National Aeronautics and Space Administration and supported NASA-NIAC.

Target Feature Extraction of Legume Based on Polarization Information

In this paper, a method of extracting and sorting the characteristics of legumes using the close range simultaneous polarization imaging system is proposed. The polarization detection system is built. The target imaging experiment is carried out. The effective experimental data and processing analysis are obtained. The results show that the close range imaging detection system based on simultaneous polarization imaging can provide more abundant information of target details. Compared with the original image, the clarity is also greatly improved. And through fusion, the surface characteristics of different targets are displayed to the greatest extent. This information can effectively improve the accuracy of sorting.

Three-Dimensional Terahertz Coded-Aperture Imaging Based on Geometric Measures.

For synthetic aperture radars, it is difficult to achieve forward-looking and staring imaging with high resolution. Fortunately, terahertz coded-aperture imaging (TCAI), an advanced radar imaging technology, can solve this problem by producing various irradiation patterns with coded apertures. However, three-dimensional (3D) TCAI has two problems, including a heavy computational burden caused by a large-scale reference signal matrix, and poor resolving ability at low signal-to-noise ratios (SNRs). This paper proposes a 3D imaging method based on geometric measures (GMs), which can reduce the computational burden and achieve high-resolution imaging for low SNR targets. At extremely low SNRs, it is difficult to detect the range cells containing scattering information with an ordinary range profile. However, this difficulty can be overcome through GMs, which can enhance the useful signal and restrain the noise. By extracting useful data from the range profile, target information in different imaging cells can be simultaneously reconstructed. Thus, the computational complexity is distinctly reduced when the 3D image is obtained by combining reconstructed 2D imaging results. Based on the conventional TCAI (C-TCAI) model, we deduce and build a GM-based TCAI (GM-TCAI) model. Compared with C-TCAI, the experimental results demonstrate that GM-TCAI achieves a more impressive performance with regards to imaging ability and efficiency. Furthermore, GM-TCAI can be widely applied in close-range imaging fields, for instance, medical diagnosis, nondestructive detection, security screening, etc.

Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform

The potential of close-range hyperspectral imaging (HSI) as a tool for detecting early drought stress responses in plants grown in a high-throughput plant phenotyping platform (HTPPP) was explored. Reflectance spectra from leaves in close-range imaging are highly influenced by plant geometry and its specific alignment towards the imaging system. This induces high uninformative variability in the recorded signals, whereas the spectral signature informing on plant biological traits remains undisclosed. A linear reflectance model that describes the effect of the distance and orientation of each pixel of a plant with respect to the imaging system was applied. By solving this model for the linear coefficients, the spectra were corrected for the uninformative illumination effects. This approach, however, was constrained by the requirement of a reference spectrum, which was difficult to obtain. As an alternative, the standard normal variate (SNV) normalisation method was applied to reduce this uninformative variability.Once the envisioned illumination effects were eliminated, the remaining differences in plant spectra were assumed to be related to changes in plant traits. To distinguish the stress-related phenomena from regular growth dynamics, a spectral analysis procedure was developed based on clustering, a supervised band selection, and a direct calculation of a spectral similarity measure against a reference. To test the significance of the discrimination between healthy and stressed plants, a statistical test was conducted using a one-way analysis of variance (ANOVA) technique.The proposed analysis techniques was validated with HSI data of maize plants (Zea mays L.) acquired in a HTPPP for early detection of drought stress in maize plant. Results showed that the pre-processing of reflectance spectra with the SNV effectively reduces the variability due to the expected illumination effects. The proposed spectral analysis method on the normalized spectra successfully detected drought stress from the third day of drought induction, confirming the potential of HSI for drought stress detection studies and further supporting its adoption in HTPPP.

Depth accuracy of the RealSense F200: Low-cost 4D facial imaging

The RealSense F200 represents a new generation of economically viable 4-dimensional imaging (4D) systems for home use. However, its 3D geometric (depth) accuracy has not been clinically tested. Therefore, this study determined the depth accuracy of the RealSense, in a cohort of patients with a unilateral facial palsy (n = 34), by using the clinically validated 3dMD system as a gold standard. The patients were simultaneously recorded with both systems, capturing six Sunnybrook poses. This study has shown that the RealSense depth accuracy was not affected by a facial palsy (1.48 ± 0.28 mm), compared to a healthy face (1.46 ± 0.26 mm). Furthermore, the Sunnybrook poses did not influence the RealSense depth accuracy (p = 0.76). However, the distance of the patients to the RealSense was shown to affect the accuracy of the system, where the highest depth accuracy of 1.07 mm was measured at a distance of 35 cm. Overall, this study has shown that the RealSense can provide reliable and accurate depth data when recording a range of facial movements. Therefore, when the portability, low-costs, and availability of the RealSense are taken into consideration, the camera is a viable option for 4D close range imaging in telehealth.

Mapping plant species in mixed grassland communities using close range imaging spectroscopy

Grasslands are one of the ecosystems that have been strongly affected by anthropogenic impacts. The state-of-the-art in monitoring changes in grassland species composition is to conduct repeated plot-based vegetation surveys that assess the occurrence and cover of plants. These plot-based surveys are typically limited to comparably small areas and the quality of the cover estimates depends strongly on the experience and performance of the surveyors. Here, we investigate the possibility of a semi-automated, image-based method for cover estimates, by analyzing the applicability of very high spatial resolution hyperspectral data to classify grassland species at the level of individuals. This individual-oriented approach is seen as an alternative to community-oriented remote sensing depicting canopy reflectance as the total of mixed species reflectance. An AISA+ imaging spectrometer mounted on a scaffold was used to scan 1m2 grassland plots and assess the impact of four sources of variation on the predicted species cover: (1) the spatial resolution of the scans, (2) complexity, i.e. species number and structural diversity, (3) the species cover and (4) the share of functional types (graminoids and forbs). Classifications were conducted using a support vector machine classification with a linear kernel, obtaining a median Kappa of ~0.8. Species cover estimations reached median r2 and root mean square errors (RMSE) of ~0.6 and ~6.2% respectively. We found that the spatial resolution and diversity level (mainly structural diversity) were the most important sources of variation affecting the performance of the proposed approach. A spatial resolution below 1cm produced relatively good models for estimating species-specific coverages (r2=~0.6; RMSE=~7.5%) while predictions using pixel sizes over that threshold failed in this individual-oriented approach (r2=~0.17; RMSE=~20.7%). Areas with low inter-species overlap were better suited than areas with frequent inter-species overlap. We conclude that the application of very high resolution hyperspectral remote sensing in environments with low structural heterogeneity is suited for individual-oriented mapping of grassland plant species.