Image Distance Research Articles

Deep learning-assisted common temperature measurement based on visible light imaging

Abstract Real-time, contact-free temperature monitoring of low to medium range (30°C ~ 150 °C) has been extensively used in industry and agriculture, which is usually realized by costly infrared temperature detection methods. This paper proposes an alternative approach of extracting temperature information in real time from the visible light images of the monitoring target using a convolutional neural network (CNN). A mean square error of ＜ 1.119 °C was reached in the temperature measurements of low to medium range using the CNN and the visible light images. Imaging angle and imaging distance do not affect the temperature detection using visible optical images by the CNN. Moreover, the CNN has a certain illuminance generalization ability capable of detection temperature information from the images which were collected under different illuminance and were not used for training. Compared to the conventional machine learning algorithms mentioned in the recent literatures, this real-time, contact-free temperature measurement approach that does not require any further image processing operations facilitates temperature monitoring applications in the industrial and civil fields.

A varifocal augmented reality head‐up display using Alvarez freeform lenses

AbstractA crucial requirement of augmented reality head‐up displays (AR‐HUDs) is continuously adjustable virtual image distance (VID), which allows adaptation to various depths in road environments and thereby avoids visual fatigue. However, usual varifocal components for near‐eye displays are unavailable because AR‐HUDs require the varifocal component's aperture to be larger than 10 cm. This study considers the Alvarez lenses, which change the optical power by in‐plane sliding two freeform lenses. Under the paraxial assumption, classic Alvarez lenses can create a quadratic wavefront profile, but the large aperture and extensive diopter variation range required by AR‐HUDs lead to significant aberrations. Thus, the classic paraxial Alvarez lens design is extended by co‐optimizing Alvarez lenses with high‐order surface profiles and a primary freeform mirror. Therefore, a novel varifocal AR‐HUD containing Alvarez lenses with apertures larger than 15 cm is proposed. The AR‐HUD generates a varifocal plane whose VID can be continuously adjusted between 2.5 and 7.5 m, and another focal plane with a fixed VID at 7.5 m. In addition, merely one display panel is used for compactness. Finally, an AR‐HUD prototype with a reduced volume of 9.8 L was built. The expected varifocal performance and qualified imaging quality were experimentally verified through the field of view, VID, and image sharpness.

Integration into 3D Printing for Image Processing using AI ML

The challenges of physical measurement for prosthetic development and highlight how our integrated tool offers a remote solution to address these challenges, promoting accessibility, convenience, and active participation in the prosthetic design process. Our Measurement Module for Prosthetic Hand Dimensions is a comparison of Image Processing and Distance Measurement Methods The creation of a module to precisely measure finger dimensions for the creation of prosthetic hands is the main goal of this research project. The module provides distance measurement and image-based input as two different ways to get dimensions. Users place their hands at predetermined distances from a screen in the distance measurement option, while users submit photographs of their hands with reference objects for scale in the image based input option. In order To provide a proper fit and functionality, the collected dimensions are an essential component of the prosthetic hand construction process. The study contrasts the efficiency of distance measurement methods employing cameras or other cameras with image processing techniques like edge recognition and contour analysis. Accuracy, usability, and efficiency are among the factors that are assessed to identify the best method for acquiring dimensions. OpenCV for image processing, TensorFlow for machine learning-based analysis, Tkinter for UI design, Mediapipe for landmark identification and hand tracking, and NumPy for numerical operations are just a few of the open-source libraries that are used in this module. The module's possible influence on improving the creation and customization of prosthetic hands is also covered. By offering insights into effective and precise dimension measurement methodologies for customized prosthetic hand design, the study's findings advance the area of prosthetics

In vitro Comparison of the Mechanical and Optical Characteristics of 5 Disposable Flexible Ureteroscopes

Introduction: Disposable (single-use) flexible ureteroscopes are alternatives to reusable ureteroscopes. With their superior surgical efficacy and safety in the presence of upper urinary calculi, disposable ureteroscopes aim to overcome the main limitations of conventional reusable ureteroscopes. However, studies on the performance of the most recently developed models of single-use flexible ureteroscopes are scarce. This study aimed to compare the in vitro performance of several recently introduced, single-use, flexible ureteroscopes. Methods: Five disposable flexible ureteroscopes were tested in vitro to evaluate their mechanical and optical characteristics. To this end, their degrees of deflection, irrigation flow rates, and image qualities were investigated. The models examined were Innovex US31-B12, OTU-100RR, Redpine RP-U-C12, Sciavita SUV-2A-B, and Seplou URS3016E. Their performance was also compared with that of a reusable flexible ureteroscope, Olympus URV-F. Results: The OTU device had the highest degrees of deflection and the smallest loop diameter of the disposable ureteroscopes. The single-use ureteroscopes had identical image resolutions at a distance of 1 cm. The Innovex and Redpine devices had the best color representation. Conclusions: Of the tested disposable ureteroscopes, the OTU device had the best mechanical attributes, given its small loop diameter, high deflection angles, and low irrigation flow loss. As to their optical properties, the resolutions of all 5 single-use models were identical at an image distance of 1 cm.

Garbage In [formula omitted] Garbage Out: Exploring GAN Resilience to Image Training Set Degradations

Generative Adversarial Networks (GANs) have received immense attention in recent years due to their ability to capture complex, high-dimensional data distributions without the need for extensive labeling. Since their conception in 2014, a wide array of GAN variants have been proposed featuring alternative architectures, optimizers, and loss functions with the goal of improving performance and training stability. This manuscript focuses on quantifying the resilience of a GAN architecture to specific modes of image degradation. We conduct systematic experimentation to empirically determine the effects of 10 fundamental image degradation modes, applied to the training image dataset, on the Fréchet inception distance (FID) of images generated by a conditional deep convolutional GAN (cDCGAN). We find that at the α=0.05 level, brightening, darkening, and blurring are statistically significantly more detrimental to the resulting GAN image quality than removing the degraded data completely, while other degradations are typically safe to keep in training datasets. Additionally, we find that in the case of randomized partial occlusion, the FID of the resulting GAN images approaches that of the degraded training set for increasing levels of occlusion, with the surprising result that GAN FID performance is equal to that of the training set at 75% degradation.

YOLO-BLBE: A Novel Model for Identifying Blueberry Fruits with Different Maturities Using the I-MSRCR Method

Blueberry is among the fruits with high economic gains for orchard farmers. Identification of blueberry fruits with different maturities has economic significance to help orchard farmers plan pesticide application, estimate yield, and conduct harvest operations efficiently. Vision systems for automated orchard yield estimation have received growing attention toward fruit identification with different maturity stages. However, due to interfering factors such as varying outdoor illuminations, similar colors with the surrounding canopy, imaging distance, and occlusion in natural environments, it remains a serious challenge to develop reliable visual methods for identifying blueberry fruits with different maturities. This study constructed a YOLO-BLBE (Blueberry) model combined with an innovative I-MSRCR (Improved MSRCR (Multi-Scale Retinex with Color Restoration)) method to accurately identify blueberry fruits with different maturities. The color feature of blueberry fruit in the original image was enhanced by the I-MSRCR algorithm, which was improved based on the traditional MSRCR algorithm by adjusting the proportion of color restoration factors. The GhostNet model embedded by the CA (coordinate attention) mechanism module replaced the original backbone network of the YOLOv5s model to form the backbone of the YOLO-BLBE model. The BIFPN (Bidirectional Feature Pyramid Network) structure was applied in the neck network of the YOLO-BLBE model, and Alpha-EIOU was used as the loss function of the model to determine and filter candidate boxes. The main contributions of this study are as follows: (1) The I-MSRCR algorithm proposed in this paper can effectively amplify the color differences between blueberry fruits of different maturities. (2) Adding the synthesized blueberry images processed by the I-MSRCR algorithm to the training set for training can improve the model’s recognition accuracy for blueberries of different maturity levels. (3) The YOLO-BLBE model achieved an average identification accuracy of 99.58% for mature blueberry fruits, 96.77% for semi-mature blueberry fruits, and 98.07% for immature blueberry fruits. (4) The YOLO-BLBE model had a size of 12.75 MB and an average detection speed of 0.009 s.

Improved polarization scattering imaging using local-global context polarization feature learning framework

Polarization scattering imaging (PSI) is an extremely challenging problem due to the fact that scattering media can lead to severe degradation of the object information. In this paper, we propose a novel high-performance computational method, the PSI based on a well-designed local-global context polarization feature learning (LGCPFL) framework, named PSI-LGCPFL, to efficiently retrieve the target’s information. In the LGCPFL framework, the dilated convolutional neural network and Swin Transformer are concurrently introduced to capture local polarization feature information and long-range polarization dependencies in the polarization scattering images, respectively. We in-depth investigate the relationship between the geometry of target, the scattering imaging distance, and the constituent materials and the PSI quality. The performance of our proposed PSI-LGCPFL is benchmarked and evaluated on three testing datasets established in real-life scenarios, i.e., STR24, DIS50 and MAT18, which cover untrained target’s geometry, various untrained targets lying in untrained distances between scattering medium and targets, and diverse untrained target’s materials with different background materials, respectively. The experimental results demonstrate that the proposed PSI-LGCPFL has a superior performance on retrieving the target’s information with high-generalization abilities and reasonably inferring speed, and outperforms several existing state-of-the-art methods. To our knowledge, PSI-LGCPFL is the first approach to achieve the PSI by polarization characteristics and a deep learning model with Swin Transformer. It also highlights the prospect of accurately reconstruction of remote sensing target’s information in scattering medium via using polarization information and deep learning

Miniaturized time-correlated single-photon counting module for time-of-flight non-line-of-sight imaging applications.

Single-photon time-of-flight (TOF) non-line-of-sight (NLOS) imaging enables the high-resolution reconstruction of objects outside the field of view. The compactness of TOF NLOS imaging systems, entailing the miniaturization of key components within such systems, is crucial for practical applications. Here, we present a miniaturized four-channel time-correlated single-photon counting module dedicated to TOF NLOS imaging applications. The module achieves excellent performance with a 10ps bin size and 27.4ps minimum root-mean-square time resolution. We present the results of the TOF NLOS imaging experiment using an InGaAs/InP single-photon detector and the time-correlated single-photon counting module and show that a 6.3cm lateral resolution and 2.3cm depth resolution can be achieved under the conditions of 5m imaging distance and 1ms pixel dwell time.

Model-based monocular 6-degree-of-freedom pose tracking for asteroid

In this paper, we present a novel vision-based framework to track the 6-DoF pose of an asteroid in real time with the 3D contour of the asteroid as a feature. During pose tracking, at the beginning time of tracking, the tracking system is initialized by a pose retrieval method. At each subsequent time instant, given the 3D mesh model of an asteroid, with the initial pose and its covariance given by the square root cubature Kalman Filter (SCKF), the 3D mesh segments constituting the 3D asteroid contour are efficiently extracted from the 3D mesh model. Then, in the input asteroid image, we search the image points corresponding to the extracted 3D segments within the searching range defined by the initial pose and its covariance. After that, the asteroid pose is determined in real time by minimizing the angles between the back-projection lines of the searched image points and the projection planes of the corresponding 3D segments, which is much more robust to the position change of the asteroid and asteroid size. The covariance matrix of the pose is inferred from the Cartesian noise model in the first order. Eventually, the SCKF is derived from the second-order auto regression to generate the final pose estimate and give the initial pose and its covariance for the next time instant. The synthetic trials quantitatively validate the real-time performance, robustness, and accuracy of our algorithm in dark space, different imaging distances, lighting conditions, image noise, model error, and initial pose error, and meanwhile, the real trial qualitatively shows the effectiveness of our method.

The contribution of semantic distance knowledge to size constancy in perception and grasping when visual cues are limited

To achieve a stable perception of object size in spite of variations in viewing distance, our visual system needs to combine retinal image information and distance cues. Previous research has shown that, not only retinal cues, but also extraretinal sensory signals can provide reliable information about depth and that different neural networks (perception versus action) can exhibit preferences in the use of these different sources of information during size-distance computations. Semantic knowledge of distance, a purely cognitive signal, can also provide distance information. Do the perception and action systems show differences in their ability to use this information in calculating object size and distance? To address this question, we presented ‘glow-in-the-dark’ objects of different physical sizes at different real distances in a completely dark room. Participants viewed the objects monocularly through a 1-mm pinhole. They either estimated the size and distance of the objects or attempted to grasp them. Semantic knowledge was manipulated by providing an auditory cue about the actual distance of the object: “20 cm”, “30 cm”, and “40 cm”. We found that semantic knowledge of distance contributed to some extent to size constancy operations during perceptual estimation and grasping, but size constancy was never fully restored. Importantly, the contribution of knowledge about distance to size constancy was equivalent between perception and action. Overall, our study reveals similarities and differences between the perception and action systems in the use of semantic distance knowledge and suggests that this cognitive signal is useful but not a reliable depth cue for size constancy under restricted viewing conditions.

A Novel Flexible Endomicroscopic Scanning Instrument with Visual Servoing Control

Optical biopsy using probe‐based confocal laser endomicroscopy (pCLE) and optical coherence tomography (OCT) technologies performs in vivo tissue scanning of organ surfaces and plays a crucial role in early cancer diagnosis. Herein, a flexible endomicroscopic scanning instrument based on a 3‐degree‐of‐freedom flexible hinge structure and visual servoing control is presented. The instrument integrates OCT and pCLE. OCT information compensates for the imaging distance of pCLE, which can combine the advantages of the two technologies. The mechanism allows for tissue surface scanning with a wire‐driven active bending part and dynamic adjustment of the probe‐tissue distance using a combination of wire and spring‐driven, which satisfy large‐area scanning while maintaining consistence tissue contacts. Local optimization‐based visual servoing control is proposed to optimize the tissue scanning. Through the scanning experiments, high‐quality mosaics of uneven surfaces are obtained with an effective area that is larger than 4 mm2. In vivo animal trial further verifies the feasibility and clinical potential of the instrument.

Local geometric edge features based registration for textureless object in augmented reality assisted assembly

Image-based methods have been widely used in augmented reality (AR) assistant assembly systems. However, due to the lack of sufficient texture information on the surface of assembly part, traditional image feature matching methods still face challenges. This paper proposes a coarse-to-fine AR registration method for textureless assembly part. In the first stage, a new feature matching method which is called line neighborhood edge descriptor (LNED) is presented to find the coarse camera pose from textureless image. The LNED take the contour line of assembly part as the description object, and use local geometric edge of assembly part to describe the contour line. During the image matching, the binary encoding is used to reduce the computational consumption for LNED. In the second stage, spatial points in the CAD model of assembly part are reverse projected to the textureless image based on the coarse camera pose. And the bundle adjustment method based on the edge distance of the textureless image is adopted to iteratively calculate the precise camera pose. In the experimental evaluation, the proposed registration method shows high accuracy and fast speed in comparison with conventional registration methods, which demonstrates that our method can effectively solve the problem of AR registration for textureless assembly part.

Experimental Study on Measuring and Tracking Structural Displacement Based on Surveillance Video Image Analysis.

The digital image method of monitoring structural displacement is receiving more attention today, especially in non-contact structure health monitoring. Some obvious advantages of this method, such as economy and convenience, were shown while it was used to monitor the deformation of the bridge structure during the service period. The image processing technology was used to extract structural deformation feature information from surveillance video images containing structural displacement in order to realize a new non-contact online monitoring method in this paper. The influence of different imaging distances and angles on the conversion coefficient (η) that converts the pixel coordinates to the actual displacement was first studied experimentally. Then, the measuring and tracking of bridge structural displacement based on surveillance video images was investigated by laboratory-scale experiments under idealized conditions. The results showed that the video imaging accuracy can be affected by changes in the relative position of the imaging device and measured structure, which is embodied in the change in η (actual size of individual pixel) on the structured image. The increase in distance between the measured structure and the monitoring equipment will have a significant effect on the change in the η value. The value of η varies linearly with the change in shooting distance. The value of η will be affected by the changes in shooting angle. The millimeter-level online monitoring of the structure displacement can be realized using images based on surveillance video images. The feasibility of measuring and tracking structural displacement based on surveillance video images was confirmed by a laboratory-scale experiment.

Three-dimensional reconstruction based on micro-imaging under wavelength-tunable illumination.

The three-dimensional reconstruction technique has been widely applied across various fields, with imaging serving as a fundamental approach to achieve this reconstruction. In the present study, we employed micro-imaging to realize 3D reconstruction based on the "shape from focus" and the chromatic aberration effect. This approach eliminates the need for sample or imaging lens movement to locate the focal plane for obtaining clear images. Instead, by utilizing tunable illuminance, we can adjust the imaging distance through the chromatic aberration, thereby achieving accurate reconstructions. As a means of verification, a simple system was accordingly constructed with an adjustable illuminance range (500-750nm) at a magnification of 10× for imaging purposes. The fine reconstruction achieved high precision in micrometers; however, the depth of field emerged as an issue during the reconstruction process. To assess this method, a coin was employed, and the resulting reconstruction bias was determined to be as low as 0.01mm. These findings indicate that the proposed method is practical for surface reconstruction and its capabilities will be further enhanced through optical design improvements.

A Multi-Dimensional Feature Fusion Recognition Method for Space Infrared Dim Targets Based on Fuzzy Comprehensive with Spatio-Temporal Correlation

Space infrared (IR) target recognition has always been a key issue in the field of space technology. The imaging distance is long, the target is weak, and the feature discrimination is low, making it difficult to distinguish between high-threat targets and decoys. However, most existing methods ignore the fuzziness of multi-dimensional features, and their performance mainly depends on the accuracy of feature extraction, with certain limitations in handling uncertainty and noise. This article proposes a space IR dim target fusion recognition method, which is based on fuzzy comprehensive of spatio-temporal correlation. First, we obtained multi-dimensional IR features of the target through multi-time and multi-spectral detectors, then we established and calculated the adaptive fuzzy-membership function of the features. Next, we applied the entropy weight method to ascertain the objective fusion weights of each feature and computed the spatially fuzzified fusion judgments for the targets. Finally, the fuzzy comprehensive function was used to perform temporal recursive judgment, and the ultimate fusion recognition result was obtained by integrating the results of each temporal recursive judgment. The simulation and comparative experimental results indicate that the proposed method improved the accuracy and robustness of IR dim target recognition in complex environments. Under ideal conditions, it can achieve an accuracy of 88.0% and a recall of 97.5% for the real target. In addition, this article also analyzes the impact of fusion feature combinations, fusion frame counts, different feature extraction errors, and feature database size on recognition performance. The research in this article can enable space-based IR detection systems to make more accurate and stable decisions, promoting defense capabilities and ensuring space security.

Calcium ions have a detrimental impact on the boundary lubrication property of hyaluronic acid and lubricin (PRG-4) both alone and in combination

Cartilage demineralisation in Osteoarthritis (OA) patients can elevate calcium ion levels in synovial fluid, as evidenced by the prevalence of precipitated calcium phosphate crystals in OA synovial fluid. Although it has been reported that there is a potential connection between elevated concentrations of calcium ions and a deterioration in the lubrication and wear resistance of cartilage tissues, the mechanism behind the strong link between calcium ion concentration and decreased lubrication performance is unclear. In this work, the AFM friction, imaging, and normal force distance measurements were used to investigate the lubrication performances of hyaluronic acid (HA), Lubricin (LUB), and HA-LUB complex in the presence of calcium ions (5 mM, 15 mM, and 30 mM), to understand the possible mechanism behind the change of lubrication property. The results of AFM friction measurements suggest that introducing calcium ions to the environment effectively eliminated the lubrication ability of HA and HA-LUB, especially with relatively low loading applied. The AFM images indicate that it is unlikely that structural or morphological changes in the surface-bound layer upon calcium ions addition are primarily responsible for the friction results demonstrated. Further, the poor correlation between the effect of calcium ions on the adhesion forces and its impact on friction suggests that the decrease in the lubricating ability of both layers is likely a result of changes in the hydration of the HA-LUB surface bound layers than changes in intermolecular or intramolecular binding. This work provides the first experimental evidence lending towards the relationship between bone demineralisation and articular cartilage degradation at the onset of OA and the mechanism through which elevated calcium levels in the synovial fluid act on joint lubrication.

Preoperative Assessment of Vessel-to-acetabular Rim Distances in Non-contrast CT Images for Total Hip Arthroplasty

Preoperative Assessment of Vessel-to-acetabular Rim Distances in Non-contrast CT Images for Total Hip Arthroplasty

The practical use of UAV systems in mine excavations and inspection works at LW “Bogdanka” S.A.

This publication presents the application of the Elios 3 caged drone in underground geodetic measurements at the LW “Bogdanka” coal mine. Traditional surveying methods are increasingly being replaced by modern technologies such as laser scanners and unmanned aerial vehicles (UAV). LW “Bogdanka” mine has wide experience in the use of drones in surface surveys, which has enabled the implementation of these devices also in underground mine excavations. The Elios 3 aircraft, equipped with a 4K camera, thermal imaging camera, distance sensor and LiDAR technology, provides precise scanning of the environment and creating high-density point clouds. The SLAM stabilization system ensures the accuracy of measurements even in difficult conditions. This article focuses on the practical applications of the Elios 3 drone in the mine, such as inspections of coal storage tanks, assessment of the technical condition of dewatering roadways and measurements in hard-to-reach areas. The use of UAV significantly improves operational efficiency, reduces survey time and minimizes risks to employees. Thanks to LiDAR technology, Elios 3 accurately represents the details of the object being measured, which is crucial for maintaining and improving the mine’s infrastructure. The introduction of a drone for surveying at the LW “Bogdanka” mine brings numerous benefits, including increased workplace safety and advanced measurement capabilities in hard-to-reach and dangerous areas.

Lensless multi-core-fiber-based imaging from speckle autocorrelation under highly coherent illumination

Flexible fiber-optic lensless endoscopic imaging plays a significant role in medical and biological applications. Lensless imaging based on far-field speckle of multi-core fibers under spatially incoherent light source illumination relied on the angular optical memory effect, resulting in limited field of view and susceptibility to interference effects. Image reconstruction from fiber speckle intensity under the highly coherent light illumination has been investigated using classical coherent diffraction theory, but it imposes limitations on imaging distance and detection positions. However, existing alternative solutions involve reducing coherence level to satisfy the angular optical memory effect, which increases system complexity and sacrifices the advantages of single-frame imaging. The reconstruction of intensity-based images remains a significant challenge under highly coherent illumination, primarily due to the interference effect. In this study, an imaging algorithm that reconstructs the original image solely from the intensity of far-field fiber speckle under coherent illumination conditions is proposed. The proposed method is based on statistical optics theory and aims to extract the relevant information within coherent speckle patterns to reconstruct the image. Our approach expands the imaging field of view, simplifies the imaging setup, and broadens the range of viable light sources for speckle correlation imaging. The effectiveness of the algorithm is verified by simulated experiments and a real imaging system.

SOLID: a novel similarity metric for mono-modal and multi-modal deformable image registration

Medical image registration is an integral part of various clinical applications including image guidance, motion tracking, therapy assessment and diagnosis. We present a robust approach for mono-modal and multi-modal medical image registration. To this end, we propose the novel shape operator based local image distance (SOLID) which estimates the similarity of images by comparing their second-order curvature information. Our similarity metric is rigorously tailored to be suitable for comparing images from different medical imaging modalities or image contrasts. A critical element of our method is the extraction of local features using higher-order shape information, enabling the accurate identification and registration of smaller structures. In order to assess the efficacy of the proposed similarity metric, we have implemented a variational image registration algorithm that relies on the principle of matching the curvature information of the given images. The performance of the proposed algorithm has been evaluated against various alternative state-of-the-art variational registration algorithms. Our experiments involve mono-modal as well as multi-modal and cross-contrast co-registration tasks in a broad variety of anatomical regions. Compared to the evaluated alternative registration methods, the results indicate a very favorable accuracy, precision and robustness of the proposed SOLID method in various highly challenging registration tasks.