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Journal articles on the topic 'Estimation de la distance'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Kronenfeld, Barry J. "A Plotless Density Estimator Based on the Asymptotic Limit of Ordered Distance Estimation Values." Forest Science 55, no. 4 (2009): 283–92. http://dx.doi.org/10.1093/forestscience/55.4.283.

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Abstract Estimation of tree density from point-tree distances is an attractive option for quick inventory of new sites, but estimators that are unbiased in clustered and dispersed situations have not been found. Noting that bias of an estimator derived from distances to the kth nearest neighbor from a random point tends to decrease with increasing k, a method is proposed for estimating the limit of an asymptotic function through a set of ordered distance estimators. A standard asymptotic model is derived from the limiting case of a clustered distribution. The proposed estimator is evaluated ag

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Makaremi, Masrour, Rafael Ristor, François de Brondeau, Agathe Choquart, Camille Mengelle, and Bernard N’Kaoua. "Estimation of Distances within Real and Virtual Dental Models as a Function of Task Complexity." Diagnostics 13, no. 7 (2023): 1304. http://dx.doi.org/10.3390/diagnostics13071304.

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Orthodontists have seen their practices evolve from estimating distances on plaster models to estimating distances on non-immersive virtual models. However, if the estimation of distance using real models can generate errors (compared to the real distance measured using tools), which remains acceptable from a clinical point of view, is this also the case for distance estimation performed on digital models? To answer this question, 50 orthodontists (31 women and 19 men) with an average age of 36 years (σ = 12.84; min = 23; max = 63) participated in an experiment consisting of estimating 3 types

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Wartenberg, Constanze, and Per Wiborg. "Precision of Exocentric Distance Judgments in Desktop and Cube Presentation." Presence: Teleoperators and Virtual Environments 12, no. 2 (2003): 196–206. http://dx.doi.org/10.1162/105474603321640941.

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Accuracy of space perception and distance estimation in virtual environments is an important precondition for the reliable use of virtual techniques in the design of products, workplaces, architecture, and production systems. The present study compares the accuracy of exocentric 1 distance estimations that a static perceiver achieves with two virtual presentation techniques: a desktop and an immersive cube presentation. Estimation accuracy in a physical mock-up is used as a point of reference. Subjects estimated exocentric distances in detailed models of a workplace previously unknown to them.

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Nam, Gyeong-Mo, and Eui-Rim Jeong. "Distance Estimation Based on Deep Convolutional Neural Network Using Ultra-Wideband Signals." Journal of Computational and Theoretical Nanoscience 17, no. 7 (2020): 3212–17. http://dx.doi.org/10.1166/jctn.2020.9163.

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Recently, high accuracy localization technique is required to provide indoor location services. The purpose of this paper is to propose a distance estimation technique based on deep convolutional neural network (DCNN) for indoor environments. Among distance estimation techniques based on wireless communication signals, the use of ultra-wideband (UWB) signals has the advantage of high accuracy in the time domain. The proposed distance estimation method uses UWB signals and proposes a new DCNN-based distance estimator. The superiority of the proposed method is confirmed through computer simulati

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Nadeau, Christopher P., and Courtney J. Conway. "Field evaluation of distance-estimation error during wetland-dependent bird surveys." Wildlife Research 39, no. 4 (2012): 311. http://dx.doi.org/10.1071/wr11161.

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Context The most common methods to estimate detection probability during avian point-count surveys involve recording a distance between the survey point and individual birds detected during the survey period. Accurately measuring or estimating distance is an important assumption of these methods; however, this assumption is rarely tested in the context of aural avian point-count surveys. Aims We expand on recent bird-simulation studies to document the error associated with estimating distance to calling birds in a wetland ecosystem. Methods We used two approaches to estimate the error associat

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Witmer, Bob G., and Paul B. Kline. "Judging Perceived and Traversed Distance in Virtual Environments." Presence: Teleoperators and Virtual Environments 7, no. 2 (1998): 144–67. http://dx.doi.org/10.1162/105474698565640.

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The ability to accurately estimate distance is an essential component of navigating large-scale spaces. Although the factors that influence distance estimation have been a topic of research in real-world environments for decades and are well known, research on distance estimation in virtual environments (VEs) has only just begun. Initial investigations of distance estimation in VEs suggest that observers are less accurate in estimating distance in VEs than in the real world (Lampton et al., 1995). Factors influencing distance estimates may be divided into those affecting perceived distance (vi

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Givon-Benjio, Nur, Tom Marx, Marissa Hartston, Idan M. Aderka, Bat-Sheva Hadad, and Hadas Okon-Singer. "The relationship between interpersonal distance preference and estimation accuracy in autism." PLOS ONE 19, no. 9 (2024): e0306536. http://dx.doi.org/10.1371/journal.pone.0306536.

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People naturally seek an interpersonal distance that feels comfortable, striking a balance between not being too close or too far from others until reaching a state of equilibrium. Previous studies on interpersonal distance preferences among autistic individuals have yielded inconsistent results. Some show a preference for greater distance, while others indicate a preference for shorter distances, or reveal higher variance in preferences among autistic individuals. In a related vein, previous studies have also investigated the way autistics accurately judge distance, and these studies have rec

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Yoon, Jeonghyeon, Jisoo Oh, and Seungku Kim. "Transfer Learning Approach for Indoor Localization with Small Datasets." Remote Sensing 15, no. 8 (2023): 2122. http://dx.doi.org/10.3390/rs15082122.

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Indoor pedestrian localization has been the subject of a great deal of recent research. Various studies have employed pedestrian dead reckoning, which determines pedestrian positions by transforming data collected through sensors into pedestrian gait information. Although several studies have recently applied deep learning to moving object distance estimations using naturally collected everyday life data, this data collection approach requires a long time, resulting in a lack of data for specific labels or a significant data imbalance problem for specific labels. In this study, to compensate f

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Murray, John, Steven Richardson, Keith Joiner, and Graham Wild. "Aiding Depth Perception in Initial Drone Training: Evidence from Camera-Assisted Distance Estimation." Technologies 13, no. 7 (2025): 267. https://doi.org/10.3390/technologies13070267.

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Remotely Piloted Aircraft (RPA) pilots frequently experience difficulties with depth perception, particularly when estimating distances between the drone and environmental obstacles. This study evaluates whether the use of onboard camera imagery can improve exocentric distance estimation accuracy among ab initio drone pilots operating under visual line-of-sight (VLOS) conditions. Two groups of undergraduate students performed distance estimation tasks at 20 and 50 m. One group used direct observation only to estimate the exocentric distance between the drone and an obstacle. The second group,

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Allen, Robert C., Daniel P. McDonald, and Michael J. Singer. "Landmark Direction and Distance Estimation in Large Scale Virtual Environments." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 41, no. 2 (1997): 1213–17. http://dx.doi.org/10.1177/1071181397041002109.

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The current paper describes our classification of errors participants made when estimating direction and distances in a large scale (2000 m × 2000 m) Virtual Environment (VE). Two VE configuration groups (Low or High Interactivity) traversed a 400 m route through one of two Virtual Terrain's (Distinctive or Non-Distinctive or Terrain 1 and 2, respectively) in 100 m increments. The High VE group used a treadmill to move through the VE with head tracked visual displays; the Low VE group used a joystick for movement and visual display control. Results indicate that as experience within either ter

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Kim, Sunyong, Sun Young Park, Daehoon Kwon, Jaehyun Ham, Young-Bae Ko, and Hyuk Lim. "Two-hop distance estimation in wireless sensor networks." International Journal of Distributed Sensor Networks 13, no. 2 (2017): 155014771668968. http://dx.doi.org/10.1177/1550147716689683.

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In wireless sensor networks, the accurate estimation of distances between sensor nodes is essential. In addition to the distance information available for immediate neighbors within a sensing range, the distance estimation of two-hop neighbors can be exploited in various wireless sensor network applications such as sensor localization, robust data transfer against hidden terminals, and geographic greedy routing. In this article, we propose a two-hop distance estimation method, which first obtains the region in which the two-hop neighbor nodes possibly exist and then takes the average of the di

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Lampton, Donald R., Daniel P. McDonald, Michael Singer, and James P. Bliss. "Distance Estimation in Virtual Environments." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 39, no. 20 (1995): 1268–72. http://dx.doi.org/10.1177/154193129503902006.

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This paper describes an experiment to evaluate a procedure for measuring distance perception in immersive VEs. Forty-eight subjects viewed a VE with a Head Mounted Display (HMD), a Binocular Omni-Oriented Monitor (BOOM), or a computer monitor. Subjects estimated the distance to a figure of known height that was initially 40 ft away. As the figure moved forward, subjects indicated when the figure was perceived to be 30, 20, 10, 5, and 2.5 ft away. A separate group of 36 subjects performed the task in a real-world setting roughly comparable to the VE. VE distance estimation was highly variable a

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Yang, L., and L. Zhou. "Numerical Realization of Minimum Distance Estimation for Poisson Processes." Journal of Physics: Conference Series 2890, no. 1 (2024): 012043. http://dx.doi.org/10.1088/1742-6596/2890/1/012043.

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Abstract The statistical inference for minimum distance parametric estimation has been well developed in recent years, concerning the asymptotic behaviors of the minimum L 1-distance estimator, minimum L 2-distance estimator and minimum Skorohod-distance estimator. The question is how to realize the numerical solution of the minimum Skorohod-distance estimator, due to its complicated analytic structure. This paper aims to discuss the minimum distance estimation problem for Poisson processes. The main contribution includes the consistency and the approximate numerical solution of the minimum Sk

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Pathi, Sai Krishna, Andrey Kiselev, Annica Kristoffersson, Dirk Repsilber, and Amy Loutfi. "A Novel Method for Estimating Distances from a Robot to Humans Using Egocentric RGB Camera." Sensors 19, no. 14 (2019): 3142. http://dx.doi.org/10.3390/s19143142.

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Estimating distances between people and robots plays a crucial role in understanding social Human–Robot Interaction (HRI) from an egocentric view. It is a key step if robots should engage in social interactions, and to collaborate with people as part of human–robot teams. For distance estimation between a person and a robot, different sensors can be employed, and the number of challenges to be addressed by the distance estimation methods rise with the simplicity of the technology of a sensor. In the case of estimating distances using individual images from a single camera in a egocentric posit

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Wang, Chuyang, Wai Yin Mung, Karhang Chu, and Tracy Y. Choy. "Use of Schwarz-ox direct-to-reverberant power ratio estimator for distance estimation near diffuse noise point in a complex plane." Journal of the Acoustical Society of America 156, no. 4_Supplement (2024): A125. https://doi.org/10.1121/10.0035337.

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This study investigated the time- and frequency- dependent direct-to-reverberant power ratio (DRR) estimators from spatial coherence value of omnidirectional microphone array. We use a mathematical 2D geometry interpretation, complex plane, to analysis the deficiency of different DRR estimators bias and construct a better estimator near the diffuse noise point in the same plane. The diffuse noise point is a reflection of a certain distance in practical environment, where the received signals in microphone array are ideal diffuse. This estimator was then applied to distance estimation in a reve

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Efemenah, Endurance Idohen, Matthew Olatunde Afolayan, and Ali Umar Umar. "Object distance estimation using a monovision camera." IAES International Journal of Robotics and Automation (IJRA) 12, no. 4 (2023): 325–31. https://doi.org/10.11591/ijra.v12i4.pp325-331.

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In computer vision, most monovision cameras used for estimating the position of an object only estimate the 2D information of the object without the depth information. Estimating the depth information, which is the distance between the target object and the camera is quite challenging but, in this paper, a less computationally intensive method was used to estimate the object's distance to complete the 3D information needed to determine the object's location in cartesian space. In this method, the object was positioned in front of the camera at a sequential distance and was measured directly. T

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Dai, Deliang. "Mahalanobis Distances on Factor Model Based Estimation." Econometrics 8, no. 1 (2020): 10. http://dx.doi.org/10.3390/econometrics8010010.

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A factor model based covariance matrix is used to build a new form of Mahalanobis distance. The distribution and relative properties of the new Mahalanobis distances are derived. A new type of Mahalanobis distance based on the separated part of the factor model is defined. Contamination effects of outliers detected by the new defined Mahalanobis distances are also investigated. An empirical example indicates that the new proposed separated type of Mahalanobis distances predominate the original sample Mahalanobis distance.

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Bartunek, Marian, Oliver Moravčik, and Peter Schreiber. "Braking Distance Estimation by Simulation." Applied Mechanics and Materials 128-129 (October 2011): 1131–34. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.1131.

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The present-day computer power enables (parallel to tests) the simulation of processes of cars safety assurance by specialized software. This article describes a simulation of car braking and braking distance estimation in different conditions. The simulating model has been made in MATLAB/Simulink. The results of simulation give the braking distances, which are necessary for safety cars distance estimation for different road surfaces, cars speeds and other conditions.

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Zhang, Jianyi, Yu Luo, Shihang Qu, Shuai Wang, and Jianke Ma. "Research and Application of Methods for Estimating Setback Distance of Buildings on Active Fault Sites." Journal of Civil and Transportation Engineering 1, no. 2 (2024): 105–10. http://dx.doi.org/10.62517/jcte.202406217.

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From the perspective of earthquake disaster reduction planning and earthquake resilience of urban buildings, the earthquake damage and disaster reduction analysis of buildings on the surface track line of strong earthquakes are mainly concentrated in the aspect of building setback distance. Based on the examples of surface rupture damage caused by strong earthquakes, statistical analysis of the length and width of the surface rupture zone, the failure rate of structure collapse under different setback distances and the probability of surface rupture of the main fault rupture in strong earthqua

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Sugiyama, Masashi, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, and Ichiro Takeuchi. "Density-Difference Estimation." Neural Computation 25, no. 10 (2013): 2734–75. http://dx.doi.org/10.1162/neco_a_00492.

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We address the problem of estimating the difference between two probability densities. A naive approach is a two-step procedure of first estimating two densities separately and then computing their difference. However, this procedure does not necessarily work well because the first step is performed without regard to the second step, and thus a small estimation error incurred in the first stage can cause a big error in the second stage. In this letter, we propose a single-shot procedure for directly estimating the density difference without separately estimating two densities. We derive a nonp

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Jerome, Christian, and Bob Witmer. "The Perception and Estimation of Egocentric Distance in Real and Augmented Reality Environments." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 26 (2005): 2249–52. http://dx.doi.org/10.1177/154193120504902607.

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The perception of distance to real and virtual objects using two methods of distance estimation (verbal estimation and manual replication) along a 110 foot hallway was tested. Results suggest that verbal estimates of distance may not accurately reflect perceived distances. Replication procedure significantly improves the estimation of the previously viewed object distance. Furthermore, the effects of distance judgment method were greater than were the effects of environment type. The magnitude of the distance judgment error was considerably larger for the estimation condition in the real envir

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Aunga, Yu Yu, Moe Moe Lwinb, and Devasis Pradhan. "Real Time Multi-Object Detection and Ditance Estimation Based on YOLO8 Using Webcam." Scientia. Technology, Science and Society 2, no. 1 (2025): 55–67. https://doi.org/10.59324/stss.2025.2(1).05.

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An essential component of automated driving technology is vehicle movement estimation. It is essential for detection, distance estimation, and rear-end collision avoidance. This paper presents a technique for calculating vehicle distances on highways and in downtown areas. One of the most traditional deep learning challenges is distance estimation using a camera. Based on multi-object detection and machine learning approaches, this paper proposes an algorithm for determining the distance from a camera to an object. The first step of the algorithm is to collect various object types of images an

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Aunga, Yu Yu, Moe Moe Lwinb, and Devasis Pradhan. "Real Time Multi-Object Detection and Ditance Estimation Based on YOLO8 Using Webcam." Scientia. Technology, Science and Society 2, no. 1 (2025): 55–67. https://doi.org/10.59324/stss.2025.2(1).05.

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An essential component of automated driving technology is vehicle movement estimation. It is essential for detection, distance estimation, and rear-end collision avoidance. This paper presents a technique for calculating vehicle distances on highways and in downtown areas. One of the most traditional deep learning challenges is distance estimation using a camera. Based on multi-object detection and machine learning approaches, this paper proposes an algorithm for determining the distance from a camera to an object. The first step of the algorithm is to collect various object types of images an

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Guzsvinecz, Tibor, Judit Szűcs, and Erika Perge. "How Egocentric Distance Estimation Changes in Virtual Environments by Using a Desktop Display or the Gear VR." Acta Cybernetica 27, no. 1 (2025): 15–41. https://doi.org/10.14232/actacyb.307740.

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Due to the importance of depth perception in virtual spaces, the combined effects of display devices and human factors on egocentric distance estimation were investigated. We developed a virtual environment that can assess distance estimation skills of users at 10 various distances, starting from 25 cm and ending at 160 cm. Our results show that people are either accurate or overestimate distances on a desktop display, while underestimation occurs with the Gear VR in most cases. Combined with display devices, human factors also had effects on distance estimates. With the Gear VR, 35.73%-57.14%

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Zhang, Hongchang, Jianzhun Shao, Shuncheng He, Yuhang Jiang, and Xiangyang Ji. "DARL: Distance-Aware Uncertainty Estimation for Offline Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 11210–18. http://dx.doi.org/10.1609/aaai.v37i9.26327.

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To facilitate offline reinforcement learning, uncertainty estimation is commonly used to detect out-of-distribution data. By inspecting, we show that current explicit uncertainty estimators such as Monte Carlo Dropout and model ensemble are not competent to provide trustworthy uncertainty estimation in offline reinforcement learning. Accordingly, we propose a non-parametric distance-aware uncertainty estimator which is sensitive to the change in the input space for offline reinforcement learning. Based on our new estimator, adaptive truncated quantile critics are proposed to underestimate the

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Petković, Miro, and Igor Vujović. "Distance Estimation Approach for Maritime Traffic Surveillance Using Instance Segmentation." Journal of Marine Science and Engineering 12, no. 1 (2023): 78. http://dx.doi.org/10.3390/jmse12010078.

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Maritime traffic monitoring systems are particularly important in Mediterranean ports, as they provide more comprehensive data collection compared to traditional systems such as the Automatic Identification System (AIS), which is not mandatory for all vessels. This paper improves the existing real-time maritime traffic monitoring systems by introducing a distance estimation algorithm for monocular cameras, which aims to provide high quality maritime traffic metadata collection for traffic density analysis. Two distance estimation methods based on a pinhole camera model are presented: the Vesse

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Ćirić, Ivan, Milan Pavlović, Danijela Ristić-Durrant, Lubomir Dimitrov, and Vlastimir Nikolić. "FuzzyH Method for Distance Estimation in Autonomous Train Operation." Symmetry 17, no. 4 (2025): 509. https://doi.org/10.3390/sym17040509.

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For reliable autonomous train operation, detecting and classifying obstacles on or near rail tracks, and accurately estimating the distance to these obstacles, is essential. This task is more challenging in low-light conditions, common for freight trains that operate primarily at night. This paper proposes a novel method, FuzzyH, for estimating the distance between a thermal camera and detected obstacles using image-plane homography. By leveraging the homography between the image and rail track planes, and incorporating a fuzzy logic system, the method improves distance estimation accuracy and

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Wu, Jingjing, and Rohana J. Karunamuni. "Profile Hellinger distance estimation." Statistics 49, no. 4 (2014): 711–40. http://dx.doi.org/10.1080/02331888.2014.946928.

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Kézdi, Gábor, Jinyong Hahn, and Gary Solon. "Jackknife minimum distance estimation." Economics Letters 76, no. 1 (2002): 35–45. http://dx.doi.org/10.1016/s0165-1765(02)00016-2.

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Amores, J., N. Sebe, and P. Radeva. "Boosting the distance estimation." Pattern Recognition Letters 27, no. 3 (2006): 201–9. http://dx.doi.org/10.1016/j.patrec.2005.08.019.

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Chaloner, E., and R. J. Ham. "Estimation of Claudication Distance." European Journal of Vascular and Endovascular Surgery 18, no. 6 (1999): 539–40. http://dx.doi.org/10.1053/ejvs.1999.0917.

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Hendry, M. A., and J. F. L. Simmons. "Distance estimation in cosmology." Vistas in Astronomy 39, no. 3 (1995): 297–314. http://dx.doi.org/10.1016/0083-6656(95)00089-6.

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Keil, Julian, Annika Korte, Dennis Edler, Denise O‘Meara, and Frank Dickmann. "Changes of Locomotion Speed Affect Distance Estimations in Virtual Reality." Proceedings of the ICA 4 (December 3, 2021): 1–5. http://dx.doi.org/10.5194/ica-proc-4-57-2021.

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Abstract. Modern Virtual Reality (VR) applications often use artificial locomotion to allow users to travel distances within VR space that exceed the available space used to transfer real-world and real-time motion into the virtual environment. The locomotion speed is usually not fixed and can be selected dynamically by the user. Due to motion adaptation effects, variations of locomotion speed could affect how distances in VR are perceived. In the context of cartographic VR applications aimed to experience and communicate spatial information, such effects on distance perception could be proble

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Otsuka, Taku, and Yuko Yotsumoto. "Partially Separable Aspects of Spatial and Temporal Estimations in Virtual Navigation as Revealed by Adaptation." i-Perception 13, no. 1 (2022): 204166952210788. http://dx.doi.org/10.1177/20416695221078878.

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Recent studies claim that estimating the magnitude of the spatial and temporal aspects of one's self-motion shows similar characteristics, suggesting shared processing mechanisms between these two dimensions. While the estimation of other magnitude dimensions, such as size, number, and duration, exhibits negative aftereffects after prolonged exposure to the stimulus, it remains to be elucidated whether this could occur similarly in the estimation of the distance travelled and time elapsed during one's self-motion. We sought to fill this gap by examining the effects of adaptation on distance an

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Esch, H., and J. Burns. "Distance estimation by foraging honeybees." Journal of Experimental Biology 199, no. 1 (1996): 155–62. http://dx.doi.org/10.1242/jeb.199.1.155.

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Honeybees are widely believed to assess feeder distances by the energy spent on foraging flights. However, a critical review of this 'energy hypothesis' reveals many inconsistencies in the experiments from which it was derived. In fact, new evidence shows that the energy hypothesis cannot be correct. Foragers loaded with weights do not overestimate distance, as indicated by their waggle dances performed upon return to the hive. Bees that climb to a feeder on top of a high building (50 m) signal the same distance as hive mates that visit an equidistant feeder at ground level. Foragers visiting

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Zubaidah Sadikin, Nurzalina Harun, Elaziera Panji, Nur Nadhirah Mohd Rohaidi, Sidik Rathi, and Mohd Agos Salim Nasir. "Simulation Approach for Time-Distance Calculations Based on Newton Forward Divided Difference Interpolation Formula." Malaysia Journal of Invention and Innovation 3, no. 6 (2024): 19–31. https://doi.org/10.64382/mjii.v3i6.103.

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i-Dash is an innovative application designed to revolutionize the estimation of distance traveled by implementing sophisticated mathematical models such as the Newton Forward Divided Difference Interpolation Formula. By integrating this formula within its graphical user interface (GUI), i-Dash addresses the challenges associated with manually determining optimal routes, offering users enhanced journey experiences and enabling industries to achieve heightened cost, time, and operational efficiencies. Through its utilization of mathematical algorithms, i-Dash dynamically derives a distance funct

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Csóka, Bence, Péter Fiala, and Péter Rucz. "Direction and Distance Estimation of Sound Sources with Microphone Arrays." Infocommunications journal 16, no. 2 (2024): 43–50. http://dx.doi.org/10.36244/icj.2024.2.6.

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This paper is concerned with the estimation of the direction and distance of sound sources with the MUSIC beamforming algorithm, and their tracking with the help of Kalman filter. Direction-of-arrival (DOA) estimations can be performed using a combination of acoustical focusing and beamforming. Distance estimation is usually not part of the process, but it is possible through an extension of the beamforming algorithm. MUSIC (Multiple Signal Classification) is a relatively fast and simple method to locate sound sources. It is based on the separation of the received signals’ cross-spectral matri

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Liang, Hong, Zizhen Ma, and Qian Zhang. "Self-Supervised Object Distance Estimation Using a Monocular Camera." Sensors 22, no. 8 (2022): 2936. http://dx.doi.org/10.3390/s22082936.

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Distance estimation using a monocular camera is one of the most classic tasks for computer vision. Current monocular distance estimating methods need a lot of data collection or they produce imprecise results. In this paper, we propose a network for both object detection and distance estimation. A network-based on ShuffleNet and YOLO is used to detect an object, and a self-supervised learning network is used to estimate distance. We calibrated the camera, and the calibrated parameters were integrated into the overall network. We also analyzed the parameter variation of the camera pose. Further

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Hattori, Toshio, Muhammad Amiruddin Bin Ab Wahab, and Minoru Yamashita. "Low Cycle Fatigue Estimation Based on the Critical Distance Stress Theory." Advanced Materials Research 891-892 (March 2014): 1773–78. http://dx.doi.org/10.4028/www.scientific.net/amr.891-892.1773.

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Generally the critical distance stress theory was applied for the fatigue limit estimation of general structures using fatigue limit of smooth specimen (σw0), and threshold stress intensity factor range (Kth). In this paper we extended this method for the estimation of low cycle fatigue life too. In this method we define the critical distance (rc) on static strength conditions, which is calculated using ultimate tensile strength (σB) and fracture toughness (KIC), in addition to the critical distance on fatigue limit condition (rc). Then the critical distances of any low cycle fatigue condition

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Strauss, Mark, and James Carnahan. "Distance Estimation Error in a Roadway Setting." Police Journal: Theory, Practice and Principles 82, no. 3 (2009): 247–64. http://dx.doi.org/10.1350/pojo.2009.82.3.458.

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The purpose of this study was to determine the accuracy of the typical adult's ability to view, then estimate, distances along a roadway. A survey was conducted along a roadway, where 123 subjects were asked to look at, and then estimate the distance to common objects along the roadway. The entire population tended to underestimate distances to objects that were from 20 to 400 feet away. After none outliers were identified and removed, the average estimation error was –23.4%. The variation in performance among individuals was extremely large, with error extremes ranging from –96% to +71%. An a

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Keyson, David V. "Estimation of Virtually Perceived Length." Presence: Teleoperators and Virtual Environments 9, no. 4 (2000): 394–98. http://dx.doi.org/10.1162/105474600566899.

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Increasingly, information is presented to users in a spatial domain in which distances and orientation between objects imply some meaning. Perception of distances between objects may be influenced by actual movement through space, and distances may be represented by visual, tactual, or auditory means. This paper examines the judgment of linear path distances that were either tactually, visually, or visually and tactually presented to subjects. Tactual paths were virtually created using force-feed-back fields. Additionally, the influence of a constant simulated-friction force in terms of distan

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Ducey, Mark J., Michael S. Williams, Jeffrey H. Gove, and Harry T. Valentine. "Simultaneous unbiased estimates of multiple downed wood attributes in perpendicular distance sampling." Canadian Journal of Forest Research 38, no. 7 (2008): 2044–51. http://dx.doi.org/10.1139/x08-019.

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Perpendicular distance sampling (PDS) is a fast probability-proportional-to-size method for inventory of downed wood. However, previous development of PDS had limited the method to estimating only one variable (such as volume per hectare, or surface area per hectare) at a time. Here, we develop a general design-unbiased estimator for PDS. We then show how that estimator can be used to develop simple measurement protocols that allow simultaneous, unbiased estimation of multiple downed wood variables, including logs per hectare, length of logs per hectare, surface area or area coverage per hecta

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Lee, Wooseop, Min-Hee Kang, Jaein Song, and Keeyeon Hwang. "The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network." Electronics 10, no. 14 (2021): 1737. http://dx.doi.org/10.3390/electronics10141737.

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As automated vehicles have been considered one of the important trends in intelligent transportation systems, various research is being conducted to enhance their safety. In particular, the importance of technologies for the design of preventive automated driving systems, such as detection of surrounding objects and estimation of distance between vehicles. Object detection is mainly performed through cameras and LiDAR, but due to the cost and limits of LiDAR’s recognition distance, the need to improve Camera recognition technique, which is relatively convenient for commercialization, is increa

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Ding, Rui, та Andrew Mullhaupt. "Empirical Squared Hellinger Distance Estimator and Generalizations to a Family of α-Divergence Estimators". Entropy 25, № 4 (2023): 612. http://dx.doi.org/10.3390/e25040612.

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We present an empirical estimator for the squared Hellinger distance between two continuous distributions, which almost surely converges. We show that the divergence estimation problem can be solved directly using the empirical CDF and does not need the intermediate step of estimating the densities. We illustrate the proposed estimator on several one-dimensional probability distributions. Finally, we extend the estimator to a family of estimators for the family of α-divergences, which almost surely converge as well, and discuss the uniqueness of this result. We demonstrate applications of the

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Andreeva, I. G., V. M. Sitdikov, and E. A. Ogorodnikova. "Experimental methods to study the sound source localization by distance in humans." Сенсорные системы 37, no. 3 (2023): 183–204. http://dx.doi.org/10.31857/s0235009223030022.

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The review presents current methods used for researches of the auditory distance perception. The monaural and binaural cues of stationary and moving sources localization are considered. The role of binaural hearing in estimating the distance to a sound source is discussed in detail. The involvement of localization cues in absolute and relative distance estimation is described. The advantages and limitations of different experimental practices for forming virtual sound images are discussed. The special section discusses approaches to the creation of moving sound images. The results of auditory

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Idohen, Efemenah Endurance, Olatunde Mathew Afolayan, and Umar Ali Umar. "Object distance estimation using a monovision camera." IAES International Journal of Robotics and Automation (IJRA) 12, no. 4 (2023): 325. http://dx.doi.org/10.11591/ijra.v12i4.pp325-331.

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In computer vision, most monovision cameras used for estimating the position of an object only estimate the 2D information of the object without the depth information. Estimating the depth information, which is the distance between the target object and the camera is quite challenging but, in this paper, a less computationally intensive method was used to estimate the object’s distance to complete the 3D information needed to determine the object’s location in cartesian space. In this method, the object was positioned in front of the camera at a sequential distance and was measured directly. T

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Witting, Lars, and Daniel G. Pike. "Distance estimation experiment for aerial minke whale surveys." NAMMCO Scientific Publications 7 (September 1, 2009): 111. http://dx.doi.org/10.7557/3.2709.

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A comparative study between aerial cue–counting and digital photography surveys for minke whales conducted in Faxaﬂói Bay in September 2003 is used to check the perpendicular distances estimated by the cue-counting observers. The study involved 2 aircraft with the photo plane at 1,700 feet flying above the cue–counting plane at 750 feet. The observer–based distance estimates were calculated from head angles estimated by angle-boards and declination angles estimated by declinometers. These distances were checked against image–based estimates of the perpendicular distance to the same whale. The

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Daum, S. Oliver, and Heiko Hecht. "Distance estimation in vista space." Attention, Perception, & Psychophysics 71, no. 5 (2009): 1127–37. http://dx.doi.org/10.3758/app.71.5.1127.

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YAMASHITA, Hikari. "Characteristics of body distance estimation." Proceedings of the Annual Convention of the Japanese Psychological Association 74 (September 20, 2010): 2EV054. http://dx.doi.org/10.4992/pacjpa.74.0_2ev054.

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Durrett, Richard, Rasmus Nielsen, and Thomas L. York. "Bayesian Estimation of Genomic Distance." Genetics 166, no. 1 (2004): 621–29. http://dx.doi.org/10.1534/genetics.166.1.621.

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