Search Results (10)

Background/Objectives: The aim of this study was to establish the reliability and accuracy of a new noninvasive tool for FLLA (frontal plane lower limb alignment) assessment: a photometric method. Methods: Sixty-seven subjects (31 males, 36 females, age 11–47 years) participated in the study. Seventeen subjects with orthopedic disorders were marked with radiopaque markers over the anterior superior iliac spines and femoral condyles. One pelvis-to-floor radiograph and one photograph were taken in the same standardized standing position. The hip–knee–ankle (HKA) angle (radiography) and the pelvis–knee–ankle (PKA) angle (photography) were measured by one rater. In 50 healthy participants, anterior superior iliac spines and femoral condyles were marked, and two pelvis-to-floor photographs were taken in a standardized standing position. The PKA angle was measured two times by three raters. The accuracy of the photometric method was tested with Pearson’s correlation coefficient, simple linear regression, and Bland–Altman analysis. The reliability was tested with ICC(2,k) and Bland–Altman analysis. Results: The HKA angle was on average 3.9° more varus than the PKA angle, with a high correlation between measures (r = 0.97, p < 0.0001) and limits of agreement between −1.300 and −6.482. Intrarater (ICC(2,k) > 0.972), interrater (ICC(2,k) = 0.991), and test–retest (ICC(2,k) = 0.980) reliability were excellent. Conclusions: The photometric method is promising as a reliable and accurate noninvasive tool for assessing FLLA. Its accuracy across different study groups has yet to be confirmed in a larger cohort. The advantage of the presented photometric method is the use of the easily palpable anterior superior iliac spine as the proximal femoral axis point. Full article

Background: The alignment of a person’s body segments depends on their innate anatomy and neuromuscular status. Sagittal posture assessments provide valuable information on correctable deficits, which can be used to prevent possible health issues or injuries. Methods: This article provides practical guidance on how to perform a basic photometric sagittal posture analysis in a reproducible manner, which reference points should be used, and which errors should be avoided. For this purpose, based on the current literature, four important evidence-based parameters for evaluation are defined, and literature-based reference values are given for the assessment of posture. Conclusions: When done correctly, the sagittal posture analysis is a valuable tool in the fields of medicine and sports. Full article

In recent years, the rapid advancement and pervasive deployment of unmanned aerial vehicle (UAV) technology have catalyzed transformative applications across the military, civilian, and scientific domains. While aerial imaging has emerged as a pivotal tool in modern remote sensing systems, persistent challenges remain in achieving robust small-target detection under complex all-weather conditions. This paper presents an innovative multimodal fusion framework incorporating photometric perception and cross-attention mechanisms to address the critical limitations of current single-modality detection systems, particularly their susceptibility to reduced accuracy and elevated false-negative rates in adverse environmental conditions. Our architecture introduces three novel components: (1) a bidirectional hierarchical feature extraction network that enables the synergistic processing of heterogeneous sensor data; (2) a cross-modality attention mechanism that dynamically establishes inter-modal feature correlations through learnable attention weights; (3) an adaptive photometric weighting fusion module that implements spectral characteristic-aware feature recalibration. The proposed system achieves multimodal complementarity through two-phase integration: first by establishing cross-modal feature correspondences through attention-guided feature alignment, then performing weighted fusion based on photometric reliability assessment. Comprehensive experiments demonstrate that our framework achieves an improvement of at least 3.6% in mAP compared to the other models on the challenging LLVIP dataset, and with particular improvements in detection reliability on the KAIST dataset. This research advances the state of the art in aerial target detection by providing a principled approach for multimodal sensor fusion, with significant implications for surveillance, disaster response, and precision agriculture applications. Full article

Accurate calibration between LiDAR and camera sensors is crucial for autonomous driving systems to perceive and understand the environment effectively. Typically, LiDAR–camera extrinsic calibration requires feature alignment and overlapping fields of view. Aligning features from different modalities can be challenging due to noise influence. Therefore, this paper proposes a targetless extrinsic calibration method for monocular cameras and LiDAR sensors that have a non-overlapping field of view. The proposed solution uses pose transformation to establish data association across different modalities. This conversion turns the calibration problem into an optimization problem within a visual SLAM system without requiring overlapping views. To improve performance, line features serve as constraints in visual SLAM. Accurate positions of line segments are obtained by utilizing an extended photometric error optimization method. Moreover, a strategy is proposed for selecting appropriate calibration methods from among several alternative optimization schemes. This adaptive calibration method selection strategy ensures robust calibration performance in urban autonomous driving scenarios with varying lighting and environmental textures while avoiding failures and excessive bias that may result from relying on a single approach. Full article

The purpose of this paper is to investigate the impact of circadian lighting-induced melatonin suppression on patients with psychiatric and neurological disorders in hospital wards by using an ad-hoc metrology framework and the subsequent metrics formalized by the CIE in 2018. A measurement scheme was conducted in hospital ward rooms in the Department of Neurology, Zealand University Hospital, at Roskilde in Denmark, to evaluate the photometric and colorimetric characteristics of the lighting system, as well as its influence on the circadian rhythm of the occupants. The measurement scheme included point measurements and data logging, using a spectrophotometer mounted on a tripod with adjustable height to assess the newly installed circadian lighting system. The measured spectra were uploaded to the Luox platform to calculate illuminance, CCT, MEDI, etc., in accordance with the CIE S026 standard. Furthermore, the MLIT based on MEDI data logging results was calculated. In addition to CIE S026, we have investigated the usefulness of melatonin suppression models for the assessment of circadian performance regarding measured light. From the results, the lighting conditions in the patient room for both minimal and abundant daylight access were evaluated and compared; we found that access to daylight is essential for both illumination and circadian entrainment. It can be concluded that the measurement scheme, together with the use of the Luox platform and Canva template, is suitable for the accurate and satisfactory measurement of integrative lighting that aligns with CIE requirements and recommendations. Full article

The use of RGB cameras in photometric applications has grown over the last few decades in many fields such as industrial applications, light engineering and the analysis of the quality of the night sky. In this last field, they are often used in conjunction with a Sky Quality Meter (SQM), an instrument used for the measurement of night sky brightness (NSB), mainly when there is a significant amount of artificial light at night (ALAN). The performances of these two instruments are compared here. A simple source composed of nine narrowband LEDs in an integrating sphere was used to excite the two instruments and therefore measure the spectral responsivity of the SQM and of the three channels of the camera. The estimated uncertainties regarding spectral responsivity were less than 10%. A synthetic instrument approximating the SQM’s responsivity can be created using a combination of the R, G and B channels. The outputs of the two instruments were compared by measuring the spectral radiance of the night sky. An evaluation of the spectral mismatch between the two instruments completed the analysis of their spectral sensitivity. Finally, the measurements of real SQMs in four sites experiencing different levels of light pollution were compared with the values obtained by processing the recorded RGB images. Overall, the analysis shows that the two instruments have significantly different levels of spectral responsivity, and the alignment of their outputs requires the use of a correction which depends on the spectral distribution of the light coming from the sky. A synthetic SQM will always underestimate real SQM measures; an average correction factor was evaluated considering nine sky spectra under low and medium levels of light pollution; this was determined to be 1.11 and, on average, compensated for the gap. A linear correction was also supposed based on the correlation between the NSB levels measured by the two instruments; the mean squared error after the correction was 0.03 mag arcsec⁻². Full article

Low-light image enhancement can effectively assist high-level vision tasks that often fail in poor illumination conditions. Most previous data-driven methods, however, implemented enhancement directly from severely degraded low-light images that may provide undesirable enhancement results, including blurred detail, intensive noise, and distorted color. In this paper, inspired by a coarse-to-fine strategy, we propose an end-to-end image-level alignment with pixel-wise perceptual information enhancement pipeline for low-light image enhancement. A coarse adaptive global photometric alignment sub-network is constructed to reduce style differences, which facilitates improving illumination and revealing under-exposure area information. After the learned aligned image, a hierarchy pyramid enhancement sub-network is used to optimize image quality, which helps to remove amplified noise and enhance the local detail of low-light images. We also propose a multi-residual cascade attention block (MRCAB) that involves channel split and concatenation strategy, polarized self-attention mechanism, which leads to high-resolution reconstruction images in perceptual quality. Extensive experiments have demonstrated the effectiveness of our method on various datasets and significantly outperformed other state-of-the-art methods in detail and color reproduction. Full article

Compared with pairwise registration, the groupwise one is capable of handling a large-scale population of images simultaneously in an unbiased way. In this work we improve upon the state-of-the-art pixel-level, Least-Squares (LS)-based groupwise image registration methods. Specifically, the registration technique is properly adapted by the use of Self Quotient Images (SQI) in order to become capable for solving the groupwise registration of photometrically distorted, partially occluded as well as unimodal and multimodal images. Moreover, the proposed groupwise technique is linear to the cardinality of the image set and thus it can be used for the successful solution of the problem on large image sets with low complexity. From the application of the proposed technique on a series of experiments for the groupwise registration of photometrically and geometrically distorted, partially occluded faces as well as unimodal and multimodal magnetic resonance image sets and its comparison with the Lucas–Kanade Entropy (LKE) algorithm, the obtained results look very promising, in terms of alignment quality, using as figures of merit the mean Peak Signal to Noise Ratio ( $m_{PSNR}$ ) and mean Structural Similarity ( $m_{SSIM}$ ), and computational cost. Full article

The autonomous navigation of unmanned vehicles in GPS denied environments is an incredibly challenging task. Because cameras are low in price, obtain rich information, and passively sense the environment, vision based simultaneous localization and mapping (VSLAM) has great potential to solve this problem. In this paper, we propose a novel VSLAM framework based on a stereo camera. The proposed approach combines the direct and indirect method for the real-time localization of an autonomous forklift in a non-structured warehouse. Our proposed hybrid method uses photometric errors to perform image alignment for data association and pose estimation, extracts features from keyframes, and matches them to acquire the updated pose. By combining the efficiency of the direct method and the high accuracy of the indirect method, the approach achieves higher speed with comparable accuracy to a state-of-the-art method. Furthermore, the two step dynamic threshold feature extraction method significantly reduces the operating time. In addition, a motion model of the forklift is proposed to provide a more reasonable initial pose for direct image alignment based on photometric errors. The proposed algorithm is experimentally tested on a dataset constructed from a large scale warehouse with dynamic lighting and long corridors, and the results show that it can still successfully perform with high accuracy. Additionally, our method can operate in real time using limited computing resources. Full article

In this paper, we propose a unified framework to generate a pleasant and high-quality street-view panorama by stitching multiple panoramic images captured from the cameras mounted on the mobile platform. Our proposed framework is comprised of four major steps: image warping, color correction, optimal seam line detection and image blending. Since the input images are captured without a precisely common projection center from the scenes with the depth differences with respect to the cameras to different extents, such images cannot be precisely aligned in geometry. Therefore, an efficient image warping method based on the dense optical flow field is proposed to greatly suppress the influence of large geometric misalignment at first. Then, to lessen the influence of photometric inconsistencies caused by the illumination variations and different exposure settings, we propose an efficient color correction algorithm via matching extreme points of histograms to greatly decrease color differences between warped images. After that, the optimal seam lines between adjacent input images are detected via the graph cut energy minimization framework. At last, the Laplacian pyramid blending algorithm is applied to further eliminate the stitching artifacts along the optimal seam lines. Experimental results on a large set of challenging street-view panoramic images captured form the real world illustrate that the proposed system is capable of creating high-quality panoramas. Full article