Visibility Of Objects Research Articles

Improvement of the University’s Management System Based on Optimization of Processes at the Level of Structural Divisions

The process approach is the main element of management in the organization. At the same time, one of the key aspects of this approach is to ensure the visibility (“transparency”) of the management object (organization or system) through its accurate, sufficient, concise, easy-to-understand and analyze description. The process approach to the management of the organization of higher education is based on the division of educational activities into components, which allows you to subdivide the processes into basic, managerial and supporting and bring the processes in line with the goals of the organization. the purpose of the work is to improve the management system of the university based on the optimization of processes at the level of institutions in such a way that the results of the process contribute to the achievement of the strategic goals of KNITU. The study is based on the study of the experience of Russian organizations represented in the scientific literature based on international standards in the field of quality management ISO 9000 series. The process approach to management is based on the understanding of the activity process itself as a set of interrelated or interacting activities that use resources and transform inputs into outputs. The article considers and analyzes the functions of the institute in the general system of the university, describes the processes of activity using the IDEF0 methodology and, based on expert evaluation, identifies the most significant processes for which process maps are compiled. Based on the assessment of customer satisfaction and identification of strengths and weaknesses using the SWOT analysis tool, opportunities for improving the institute's activities and ways of strategic development are identified. The practical significance is due to the fact that the results of the work can be used by KNITU to optimize the internal processes of institutions, as well as to improve the processes of the university as a whole.

UIR-ES: An unsupervised underwater image restoration framework with equivariance and stein unbiased risk estimator

Underwater imaging faces challenges for enhancing object visibility and restoring true colors due to the absorptive and scattering characteristics of water. Underwater image restoration (UIR) seeks solutions to restore clean images from degraded ones, providing significant utility in downstream tasks. Recently, data-driven UIR has garnered much attention due to the potent expressive capabilities of deep neural networks (DNNs). These DNNs are supervised, relying on a large amount of labeled training samples. However, acquiring such data is expensive or even impossible in real-world underwater scenarios. While recent researches suggest that unsupervised learning is effective in UIR, none of these frameworks consider signal physical priors. In this work, we present a novel physics-inspired unsupervised UIR framework empowered by equivariance and unbiased estimation techniques. Specifically, equivariance stems from the invariance, inherent in natural signals to enhance data-efficient learning. Given that degraded images invariably contain noise, we propose a noise-tolerant loss for unsupervised UIR based on the Stein unbiased risk estimator to achieve an accurate estimation of the data consistency. Extensive experiments on the benchmark UIR datasets, including the UIEB and RUIE datasets, validate the superiority of the proposed method in terms of quantitative scores, visual outcomes, and generalization ability, compared to state-of-the-art counterparts. Moreover, our method demonstrates even comparable performance with the supervised model.

The effect of illumination on the visibility of steps and ramps for people with low vision.

Poor visibility of indoor features such as steps and ramps can pose mobility hazards for people with low vision. For purposes of architectural design, it is important to understand how design parameters such as the illumination level of an indoor space affect the visibility of steps and ramps. This study was aimed to examine the effect of typical variation in photopic illumination level in an indoor space on the visibility of steps and ramps for individuals with low vision. Steps and ramps were constructed in a large windowless room illuminated by overhead lights. Subjects with low vision completed a 5-alternative forced choice task to recognize the targets at three levels of photopic illumination, i.e., 800, 80, and 8 lux, and gave confidence ratings about their judgments on a 5-point scale. Acuities and contrast sensitivities of the subjects were also measured at each illumination level. For comparison, a group of normally sighted subjects with simulated acuity reduction also completed the step-and-ramp recognition task. For both groups of subjects, recognition accuracy was not affected by illumination level. For subjects with low vision, however, there was a significant effect of illumination level on confidence rating: subjects became more confident about their judgments with increasing illumination. There was also a weak effect of illumination level on acuity and contrast sensitivity, both worsening with decreasing illumination. Recognition performance was best predicted by contrast sensitivity, whereas confidence was best predicted by visual acuity. Illumination variation over a typical photopic range in an indoor space had minimal effect on the objective visibility of steps and ramps for people with low vision. However, illumination level affected subjects' confidence in hazard recognition. Design decisions on parameters such as illumination should consider the consequences on both the objective and the subjective accessibility of a space.

An asymmetric road lighting concept: Probeam

An asymmetric road lighting concept known as Probeam is investigated in this study for energy efficiency, contrast and visibility of vertical objects and road markings in highway lighting. Calculations based on road lighting standards, road surface and markings reflection characteristics and typical Dutch highway geometry, reveal that Probeam lighting improves the average visibility of objects and road markings over symmetrical lighting, however, with a 14% increase in luminous flux as per current road lighting standards. This increase in luminous flux is attributed to the symmetrical lighting’s advantage in specular road surface reflection, leading to a reduction in the Counterbeam lobe size in the light intensity distribution when designed to meet the current road luminance requirement. Notably, by designing the Probeam luminaire to match symmetrical lighting’s average visibility of vertical objects, for removing the road luminance requirement and dimming the luminaire, energy consumption is reduced by 52%. Halving the Probeam luminaire’s mounting height leads to an impressive 82% energy savings. The study highlights the possibility of a balance in energy use and road safety using the Probeam concept, considering advancements in road luminaires and retroreflective materials. Due to glare, the relevance of the proposed Probeam concept is restricted to one-directional traffic on highways.

USNet: underwater image superpixel segmentation via multi-scale water-net

Underwater images commonly suffer from a variety of quality degradations, such as color casts, low contrast, blurring details, and limited visibility. Existing superpixel segmentation algorithms face challenges in achieving superior performance when directly applied to underwater images with quality degradation. In this paper, to alleviate the limitations of superpixel segmentation when applied to underwater scenes, we propose the first underwater superpixel segmentation network (USNet), specifically designed according to the intrinsic characteristics of underwater images. Considering the quality degradation, we propose a multi-scale water-net module (MWM) aimed at enhancing the quality of underwater images before superpixel segmentation. The degradation-aware attention (DA) mechanism is then created and incorporated into MWM to solve light scattering and absorption, which can decrease object visibility and cause blurred edges. By effectively directing the network to prioritize locations that exhibit a considerable decrease in quality, this method enhances the visibility of those specific areas. Additionally, we extract the deep spatial features using the coordinate attention method. Finally, these features are fused with the shallow spatial information using the dynamic spatiality embedding module to embed comprehensive spatial features. Training and testing were conducted on the SUIM dataset, the underwater change detection dataset, and UIEB dataset. Experimental results show that our method achieves the best scores in terms of achievable segmentation accuracy, undersegmentation error, and boundary recall evaluation metrics compared to other methods. Both quantitative and qualitative evaluations demonstrate that our method can handle complicated underwater scenes and outperform existing state-of-the-art segmentation methods.

Cascaded combination of total variation regularization and contrast limited adaptive histogram equalization based image dehazing

ABSTRACT Haze and fog can significantly reduce the visibility of distant objects by scattering light and creating blurry, washed-out images lacking in detail. Image dehazing is a technique used in computer vision to enhance the clarity of such obscured images. This study examines the effectiveness of various dehazing methods using real hazy images and synthetic images with artificially created haze. Performance metrics such as PSNR, SSIM, MSE, CII, and computation time are used to evaluate the proposed method. The evaluation is carried out on datasets like RESIDE, GMAN, O-Haze, I-Haze, NH-Haze, and Dense haze to compare the proposed method with existing models. The proposed method attains 27.46%, 20.63% and 21.09% higher PSNR and 12.36%, 23.95% and 36.12% lower Natural Image Quality Evaluator for RESIDE dataset when analyzed to the existing models, such as strategic method towards contrast enhancement by 2D histogram equalization under TV decomposition (CE-TDHE-TVD), multiple level framework basis contrast enhancement for uniform with non-uniform back ground imageries utilizing appropriate histogram equalization (CE-VHE), and Contrast enhancement with brightness preservation of low light pictures with the help of combined CLAHE and BPDHE histogram equalization (CE-CLAHE-BPDHE) respectively.

UNDERWATER OBJECT SHAPE DETECTION BASED ON TONAL DISTRIBUTION AND EDGE DETECTION USING DIGITAL IMAGE PROCESSING

Underwater exploration activities always have their own charm, many exotic objects that exist in underwater ecosystems have not been mapped properly, due to the lack of related databases of the shapes and names of these underwater objects. Another factor that affects the visibility of objects related to the quantity of light intensity that enters under water, also not as much above the surface of the abundant water, especially during the day. This also hinders the process of documenting underwater objects. The main purpose of this study was to obtain the shape of underwater objects for several conditions of light intensity under water using a low cost digital image sensor camera. The method used in this research is to combine tonal distributions with object edge detection in digital image processing. The test results show that object detection tests in clear and turbid water can detect objects even though they are using a low-cost and low-resolution camera, but with the help of adequate lighting it can be done. From that it can be concluded that the detection of underwater objects is successful.

AIE-YOLO: Effective object detection method in extreme driving scenarios via adaptive image enhancement.

The widespread research and implementation of visual object detection technology have significantly transformed the autonomous driving industry. Autonomous driving relies heavily on visual sensors to perceive and analyze the environment. However, under extreme weather conditions, such as heavy rain, fog, or low light, these sensors may encounter disruptions, resulting in decreased image quality and reduced detection accuracy, thereby increasing the risk for autonomous driving. To address these challenges, we propose adaptive image enhancement (AIE)-YOLO, a novel object detection method to enhance road object detection accuracy under extreme weather conditions. To tackle the issue of image quality degradation in extreme weather, we designed an improved adaptive image enhancement module. This module dynamically adjusts the pixel features of road images based on different scene conditions, thereby enhancing object visibility and suppressing irrelevant background interference. Additionally, we introduce a spatial feature extraction module to adaptively enhance the model's spatial modeling capability under complex backgrounds. Furthermore, a channel feature extraction module is designed to adaptively enhance the model's representation and generalization abilities. Due to the difficulty in acquiring real-world data for various extreme weather conditions, we constructed a novel benchmark dataset named extreme weather simulation-rare object dataset. This dataset comprises ten types of simulated extreme weather scenarios and is built upon a publicly available rare object detection dataset. Extensive experiments conducted on the extreme weather simulation-rare object dataset demonstrate that AIE-YOLO outperforms existing state-of-the-art methods, achieving excellent detection performance under extreme weather conditions.

ILLUMINATING AUTONOMY FEDERATED LEARNING FOR OBJECT DETECTION IN AUTONOMOUS VEHICLES UNDER LOW LIGHT

Autonomous vehicles rely heavily on object detection systems to navigate safely, yet low-light conditions pose significant challenges to these systems. Traditional centralized machine learning approaches require substantial data aggregation, raising privacy and security concerns. This paper introduces an innovative approach, Illuminating Autonomy Federated Learning (IAFL), designed to enhance object detection in autonomous vehicles operating under low-light conditions. IAFL leverages federated learning to train object detection models across multiple vehicles without centralizing the data, thus preserving privacy while utilizing diverse realworld driving data. The approach integrates advanced image enhancement techniques to improve low-light object visibility, combined with robust federated learning algorithms to ensure model accuracy and generalization across varying environments. Extensive simulations and real-world testing demonstrate that IAFL significantly improves detection performance in low-light scenarios, maintaining high accuracy and efficiency while ensuring data privacy and security. This research marks a pivotal step toward safer and more reliable autonomous driving in challenging lighting conditions.

Nighttime Car Accident Prevention with AI Vision

Abstract: Nighttime car prevention with AI vision performs a vital function in diverse fields, which include surveillance, protection, and autonomous navigation. However, the demanding situations posed through low-mild situations make accurate and reliable object detection a complex undertaking. The proposed device leverages ultra-modern photograph enhancement algorithms to improve the visibility of objects in low-light environments. Utilizing an aggregate of adaptive histogram equalization, noise discount, and comparison enhancement, the machine enhances the uncooked input from nighttime imaginative and prescient sensors, presenting a clearer and greater particular image for subsequent analysis. the item detection module employs a deep knowledge of the primary-based method, making use of a pre-educated convolutional neural network (CNN) optimized for low-light eventualities. To deal with actual-time deployment requirements, the gadget is optimized for computational performance, making it suitable for integration into resource-restrained systems consisting of surveillance cameras and unmanned aerial cars (UAVs). The proposed answer is evaluated via good-sized experiments, demonstrating goodsized improvements in item detection accuracy as compared to standard nighttime imaginative and prescient systems.

Attenuation of electromagnetic waves in liquid foams in the frequency range of 1 – 1250 MHz

The purpose of this work is to study the attenuation of electromagnetic waves in the megahertz range (1 – 1250 MHz) in liquid foam structures. We consider issues related to shielding electromagnetic sources and reducing the visibility of metal objects from radar detection and tracking in this range, since such problem is poorly researched. A method of measuring the attenuation of foam structures in segments of strip lines of this range is proposed. Measurements are carried out using an experimental setup based on avector meter and an original strip cell of samples of foam structures with different foaming ratios in the ranges of 13 – 15, 28 – 30, and 50 – 51 units for different foaming agents. The dependencies of foam attenuation on frequency and the dependence on salt additives with concentrations of 1%, 4%, and 6% are found.

A gated cross-domain collaborative network for underwater object detection

Underwater object detection (UOD) plays a significant role in aquaculture and marine environmental protection. Considering the challenges posed by low contrast and low-light conditions in underwater environments, several underwater image enhancement (UIE) methods have been proposed to improve the quality of underwater images. However, only using the enhanced images does not improve the performance of UOD, since it may unavoidably remove or alter critical patterns and details of underwater objects. In contrast, we believe that exploring the complementary information from the two domains is beneficial for UOD. The raw image preserves the natural characteristics of the scene and texture information of the objects, while the enhanced image improves the visibility of underwater objects. Based on this perspective, we propose a Gated Cross-domain Collaborative Network (GCC-Net) to address the challenges of poor visibility and low contrast in underwater environments, which comprises three dedicated components. Firstly, a real-time UIE method is employed to generate enhanced images, which can improve the visibility of objects in low-contrast areas. Secondly, a cross-domain feature interaction module is introduced to facilitate the interaction and mine complementary information between raw and enhanced image features. Thirdly, to prevent the contamination of unreliable generated results, a gated feature fusion module is proposed to adaptively control the fusion ratio of cross-domain information. Our method presents a new UOD paradigm from the perspective of cross-domain information interaction and fusion. Experimental results demonstrate that the proposed GCC-Net achieves state-of-the-art performance on four underwater datasets.

Comparison of Road Luminance Data Estimated by Fuzzy Logic and ANN, A Case study of Kocaeli Sakıp Sabancı Caddesi

Road lighting constitutes the most important part of city lighting. While correct road lighting solves the chaos in transportation, it also paves the way for smooth urban traffic flow. It also allows people to have a comfortable and safe journey. In order to reduce accidents in the evening hours, safe driving and visual comfort must be provided to drivers. Just like inadequate road lighting, excessive lighting also causes accidents and waste of electrical energy. Good visibility of objects on the road and visual comfort for drivers is possible by ensuring an equal brightness distribution on the road surface. In this article, a prediction model was created with Artificial Neural Network (ANN) and Fuzzy Logic (FL) methods to find the luminance values of the road in Sakıp Sabancı street in Kocaeli province. When the predictions obtained by ANN and FL were compared, the results were seen to be accurate and compatible. When the estimation results obtained as a result of the application were examined, it was observed that the estimation of luminance values in good and qualified road lighting could be achieved with these methods.

Fast 19 F spectroscopic imaging with pseudo-spiral k-space sampling.

Fluorine MRI is finding wider acceptance in theranostics applications where imaging of 19 F hotspots of fluorinated contrast material is central. The essence of such applications is to capture ghosting-artifact-free images of the inherently low MR response under clinically viable conditions. To serve this purpose, this work introduces the balanced spiral spectroscopic imaging (BaSSI) sequence, which is implemented on a 3.0 T clinical scanner and is capable of generating 19 F hotspot images in an efficient manner. The sequence utilizes an all-phase-encoded pseudo-spiral k-space trajectory, enabling the acquisition of broadband (80 ppm) fluorine spectra free from chemical shift ghosting. BaSSI can acquire a 64 × 64 image with 1 mm × 1 mm voxels in just 14 s, significantly outperforming typical MRSI sequences used in 1 H or 31 P imaging. The study employed in silico characterization to verify essential design choices such as the excitation pulse, as well as to identify the boundaries of the parameter space explored for optimization. BaSSI's performance was further benchmarked against the 3D ultrashort-echo-time balanced steady-state free precession (3D UTE BSSFP) sequence, a well established method used in 19 F MRI, in vitro. Both sequences underwent extensive optimization through exploration of a wide parameter space on a small phantom containing 10 μL of non-diluted bulk perfluorooctylbromide (PFOB) prior to comparative experiments. Subsequent to optimization, BaSSI and 3D UTE BSSFP were employed to capture images of small non-diluted bulk PFOB samples (0.10 and 0.05 μL), with variations in the number of signal averages, and thus the total scan time, in order to assess the detection sensitivities of the sequences. In these experiments, the detection sensitivity was evaluated using the Rose criterion (Rc ), which provides a quantitative metric for assessing object visibility. The study further demonstrated BaSSI's utility as a (pre)clinical tool through postmortem imaging of polymer microspheres filled with PFOB in a BALB/c mouse. Anatomic localization of 19 F hotspots was achieved by denoising raw data obtained with BaSSI using a filter based on the Rose criterion. These data were then successfully registered to 1 H anatomical images. BaSSI demonstrated superior detection sensitivity in the benchmarking analysis, achieving Rc values approximately twice as high as those obtained with the 3D UTE BSSFP method. The technique successfully facilitated imaging and precise localization of 19 F hotspots in postmortem experiments. However, it is important to highlight that imaging 10 mM PFOB in small mice postmortem, utilizing a 48 × 48 × 48 3D scan, demanded a substantial scan time of 1 h and 45 min. Further studies will explore accelerated imaging techniques, such as compressed sensing, to enhance BaSSI's clinical utility.

The increase in the surface brightness of the night sky and its importance in visual astronomical observations

The problem of sky pollution with artificial light currently affects practically all branches that are related to the broadly understood environment. This is especially true for astronomical observations. This paper presents the results of measurements of the surface brightness of the night sky for the city of Rzeszów and the surrounding area, which were made in 2015, 2018 and 2021 using the photometer Sky Quality Meter (SQM-L). The measurements show that the surface brightness of the night sky in Rzeszów in 2015 was in the range from 19.20 to 18.67 mag/arcsec2, in 2018 the value of brightness oscillated in the range from 18.53 to 16.47 mag/arcsec2, and in 2021 this value was in the range from 17.13 by 15.11 mag/arcsec2. Translating the obtained values on the Bortle scale, we can see an increase in the brightness of the night sky from class VI in 2015 to class IX in 2021. A similar trend applies to neighboring towns, for which an increase in brightness from class IV in 2015 to class VIII in 2021 was also observed. An increase in the surface brightness of the night sky causes a deterioration or even loss of visibility of astronomical objects in the night sky. This is especially true for comets and low-brightness stars. Based on the measurement results, a forecast of the development of light pollution was prepared for the study areas until 2025. This forecast shows that we will still have to deal with an increase in the surface brightness of the night sky. Due to the further projected increase in the brightness of the night sky, it should be expected that observers in small towns will experience changes in the quality of the sky over the coming years, which will undoubtedly make astronomical observations difficult, in particular for faint celestial bodies such as comets.

A novel deep learning based underwater image de-noising and detecting suspicious object

Exploration of underwater resource play a vital role for nation development. Underwater surveillance systems play a crucial role in security applications, requiring accurate detection of suspicious objects in underwater images. However, the presence of noise, poor visibility, and uneven lighting conditions in underwater environments pose significant challenges for reliable object detection. This work proposes an integrated approach for underwater image de-noising, pre-processing, enhancement, and subsequent suspicious object detection by combining the DnCNN (Deep Convolutional Neural Network), CLAHE (Contrast Limited Adaptive Histogram Equalization), and additional image enhancement techniques. In addition to de-noising and pre-processing, it incorporate various image enhancement techniques to further improve object detection performance. These techniques include color correction, contrast adjustment, and edge enhancement, aiming to enhance the visual characteristics and saliency of suspicious objects in underwater images. To evaluate the effectiveness of proposed approach, this work conducted extensive experiments on an underwater image dataset containing diverse scenes and suspicious objects. The work compares proposed method with existing de-noising, preprocessing, and object detection techniques, analyzing the results using quantitative performance metrics, including precision, recall, and F1 score. The experimental results demonstrate that proposed integrated approach outperforms individual methods and achieves superior detection performance by enhancing the quality of underwater images and improving the visibility of suspicious objects.

USCFormer: Unified Transformer With Semantically Contrastive Learning for Image Dehazing

Haze severely degrades the visibility of scene objects and deteriorates the performance of autonomous driving, traffic monitoring, and other vision-based intelligent transportation systems. As a potential remedy, we propose a novel unified Transformer with semantically contrastive learning for image dehazing, dubbed USCFormer. USCFormer has three key contributions. First, USCFormer absorbs the respective strengths of CNN and Transformer by incorporating them into a unified Transformer format. Thus, it allows the simultaneous capture of global-local dependency features for better image dehazing. Second, by casting clean/hazy images as the positive/negative samples, the contrastive constraint encourages the restored image to be closer to the ground-truth images (positives) and away from the hazy ones (negatives). Third, we regard the semantic information as important prior knowledge to help USCFormer mitigate the effects of haze on the scene and preserve image details and colors by leveraging intra-object semantic correlation. Experiments on synthetic datasets and real-world hazy photos fully validate the superiority of USCFormer in both perceptual quality assessment and subjective evaluation. Code is available at https://github.com/yz-wang/USCFormer.

Automated Hooking of Biomedical Microelectrode Guided by Intelligent Microscopic Vision

Biomedical microelectrode implantation is a promising approach to achieve invasive brain-machine interface with high bandwidth and good biocompatibility. However, it is difficult to manipulate a microelectrode probe because of its micron-level width and flexible material. Toward the challenging robotic implantation, this article addresses an essential operation step: automated hooking of flexible microelectrode probe with a micro-needle. First, a point-contour of interest extraction network for microscopic image (MicroPCIE) is proposed to obtain key image features for visual guidance. Based on few-shot learning mechanism, MicroPCIE trained on nonspecific dataset can be rapidly deployed to the specific hooking task without requiring data-driven optimization. Second, a robotic hooking control system is designed considering the distributed probe locations, variable needle direction, and efficiency requirements. The needle-probe alignment control is executed under low and high microscope magnifications, to realize larger operation range and permit high alignment precision. The optical design and camera aiming control are proposed to guarantee the visibility and clearness of objects after low-to-high magnification switch. The effectiveness of the proposed methods is validated with a series of experiments.

Effective Radiation Surface as a Universal Parameter for Monitoring Optical Visibility of Objects in the Infrared Wavelength Range

Effective Radiation Surface as a Universal Parameter for Monitoring Optical Visibility of Objects in the Infrared Wavelength Range

Application of K-Nearest Neighbor Classification for Static Webcams Visibility Observation

Visibility observations and accurate forecasts are essential in meteorology, requiring a dense network of observation stations. This paper investigates image processing techniques for object detection and visibility determination using static cameras. It proposes a comprehensive method that includes image preprocessing, landmark identification, and visibility estimation, mirroring the observation process of professional meteorological observers. This study validates the visibility observation procedure using the k-nearest neighbors machine learning method across six locations, including four in the Czech Republic, one in the USA, and one in Germany. By comparing our results with professional observations, the paper demonstrates the suitability of the proposed method for operational application, particularly in foggy and low visibility conditions. This versatile method holds potential for adoption by meteorological services worldwide.