Journal articles: 'Estimation de la distance'

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Kronenfeld, Barry J. "A Plotless Density Estimator Based on the Asymptotic Limit of Ordered Distance Estimation Values." Forest Science 55, no. 4 (August 1, 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 against 13 types of simulated generating processes, including random, clustered, dispersed, and mixed. Performance is compared with ordered distance estimation of the same rank and with fixed-area sampling with the same number of trees tallied. The proposed estimator consistently performs better than ordered distance estimation and nearly as well as fixed-area sampling in all but the most clustered situations. The estimator also provides information regarding the degree of clustering or dispersion.

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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 (March 30, 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 of distances (mandibular crowding, inter-canine distance, and inter-molar distance) on 6 dental models, including 3 real and 3 virtual models. Moreover, these models were of three different levels of complexity (easy, medium, and difficult). The results showed that, overall, the distances were overestimated (compared to the distance measured using an instrument) regardless of the situation (estimates on real or virtual models), but this overestimation was greater for the virtual models than for the real models. In addition, the mental load associated with the estimation tasks was considered by practitioners to be greater for the estimation tasks performed virtually compared to the same tasks performed on plaster models. Finally, when the estimation task was more complex, the number of estimation errors decreased in both the real and virtual situations, which could be related to the greater number of therapeutic issues associated with more complex models.

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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 (April 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. All distances to be judged were located in the subjects' personal space (less than 1.5 m from the subject). Major differences between the two virtual presentation modes are that stereo information is available in the cube but not in desktop environment, and that, in the cube, changes in perspective are achieved by actually moving inside the cube instead of using a mouse. Furthermore, the cube provides a wider absolute field of view than the desktop environment. The experiment showed advantages of the cube over desktop presentation when estimating exocentric distances in “personal space” from a static position. The magnitude of distance estimation errors was significantly higher in the desktop than in the cube environment. However, estimation errors tended to be overestimations in the cube presentation, whereas over- and underestimation occurred with equal frequency in the desktop environment. In the discussion it is argued that the higher estimation accuracy in the cube environment may mainly be due to the availability of stereoscopic depth cues. According to Cutting (1997), these cues are especially relevant for spatial perception in “personal space.” 1 The term exocentric distance is used for distances between two points external to the perceiver indicating (for example) interobject distances or distances colinear with the side length of an object. These distances are to be distinguished from egocentric distances, those distances between the perceiver and one point in the environment (Waller, 1999).

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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 (July 1, 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 simulation. Widely used conventional distance estimators are based on the power threshold. The threshold is determined by signal to noise ratio (SNR) of the received signal. The arrival time of the received signal that exceeds the threshold is considered as the time-of-arrival (ToA) and the distance between transmitter and receiver is obtained from the ToA. On the other hand, the proposed distance estimator requires only the received signal without SNR estimation, which make the proposed technique simpler to implement. According to computer simulation, the conventional method is highly sensitive to SNR and distance. In contrast, the proposed method shows less than 2 m root mean square error (RMSE) performance in a wide range of SNR and the RMSE performance is not degraded in long distances. The proposed distance estimator shows excellent distance estimation performance at low SNR and long distance, so it can be applied to indoor localization system of large indoor space and can be used for precise location service.

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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 associated with five surveyor’s distance estimates between the survey point and calling birds, and to determine the factors that affect a surveyor’s ability to estimate distance. Key results We observed biased and imprecise distance estimates when estimating distance to simulated birds in a point-count scenario (error = –9 m, s.d.error = 47 m) and when estimating distances to real birds during field trials (error = 39 m, s.d.error = 79 m). The amount of bias and precision in distance estimates differed among surveyors; surveyors with more training and experience were less biased and more precise when estimating distance to both real and simulated birds. Three environmental factors were important in explaining the error associated with distance estimates, including the measured distance from the bird to the surveyor, the volume of the call and the species of bird. Surveyors tended to make large overestimations to birds close to the survey point, which is an especially serious error in distance sampling. Conclusions Our results suggest that distance-estimation error is prevalent, but surveyor training may be the easiest way to reduce distance-estimation error. Implications The present study has demonstrated how relatively simple field trials can be used to estimate the error associated with distance estimates used to estimate detection probability during avian point-count surveys. Evaluating distance-estimation errors will allow investigators to better evaluate the accuracy of avian density and trend estimates. Moreover, investigators who evaluate distance-estimation errors could employ recently developed models to incorporate distance-estimation error into analyses. We encourage further development of such models, including the inclusion of such models into distance-analysis software.

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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 (October 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 terrain increased, participants demonstrated an improved ability to directionally locate landmarks. Experience in the environment did not affect distance estimation accuracy. Terrain 1 participants were more accurate in locating proximal, as opposed to distal, landmarks. They also overestimated distances to near landmarks and underestimated distances to far landmarks. In Terrain 2, the Low VE group gave more accurate distance estimations. We believe this result can be explained in terms of increased task demands placed on the High VE Group.

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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 (April 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 (visual cues only) and those affecting traversed distance to include visual, cognitive, and proprioceptive cues. To assess the contribution of the various distance cues in VEs, two experiments were conducted. The first required a static observer to estimate the distance to a cylinder placed at various points along a 130-foot hallway. This experiment examined the effects of floor texture, floor pattern, and object size on distance estimates in a VE. The second experiment required a moving observer to estimate route segment distances and total route distances along four routes, each totaling 1210 feet. This experiment assessed the effects of movement method, movement speed, compensatory cues, and wall texture density. Results indicate that observers underestimate distances both in VEs and in the real world, but the underestimates are more extreme in VEs. Texture did not reliably affect the distance estimates, providing no compensation for the gross underestimates of distance in VE. Traversing a distance improves the ability to estimate that distance, but more natural means of moving via a treadmill do not necessarily improve distance estimates over traditional methods of moving in VE (e.g., using a joystick). The addition of compensatory cues (tone every 10 feet traversed on alternate route segments) improves VE distance estimation to almost perfect performance.

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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 (April 17, 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 for the problems of the existing PDR, a method based on transfer learning and data augmentation is proposed for estimating moving object distances for pedestrians. Consistent high-performance moving object distance estimation is achieved using only a small training dataset, and the problem of the concentration of training data only on labels within a certain range is solved using window warping and scaling methods. The training dataset consists of the three-axes values of the accelerometer sensor and the pedestrian’s movement speed calculated based on GPS coordinates. All data and GPS coordinates are collected through the smartphone. A performance evaluation of the proposed moving pedestrian distance estimation system shows a high distance error performance of 3.59 m with only approximately 17% training data compared to other moving object distance estimation techniques.

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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 (October 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 across subjects. For distance perception involving a moving figure, in the VE conditions most subjects called out before the figure had closed to the specified distances. Distance estimation was least accurate with the monitor. In the real world, most subjects called out after the figure had closed to or passed the specified distances. Ways to improve the procedure are discussed.

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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 (February 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 distances to the points in that region. The improvement in the estimation accuracy achieved by the proposed method is analyzed in comparison with a simple summation method that adds two single-hop distances as an estimate of a two-hop distance. Numerical simulation results show that in comparison with other existing distance estimation methods, the proposed method significantly reduces the distance estimation error over a wide range of node densities.

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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 (July 17, 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 position, it is often required that individuals in the scene are facing the camera, do not occlude each other, and are fairly visible so specific facial or body features can be identified. In this paper, we propose a novel method for estimating distances between a robot and people using single images from a single egocentric camera. The method is based on previously proven 2D pose estimation, which allows partial occlusions, cluttered background, and relatively low resolution. The method estimates distance with respect to the camera based on the Euclidean distance between ear and torso of people in the image plane. Ear and torso characteristic points has been selected based on their relatively high visibility regardless of a person orientation and a certain degree of uniformity with regard to the age and gender. Experimental validation demonstrates effectiveness of the proposed method.

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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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Dai, Deliang. "Mahalanobis Distances on Factor Model Based Estimation." Econometrics 8, no. 1 (March 5, 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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Sugiyama, Masashi, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, and Ichiro Takeuchi. "Density-Difference Estimation." Neural Computation 25, no. 10 (October 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 nonparametric finite-sample error bound for the proposed single-shot density-difference estimator and show that it achieves the optimal convergence rate. We then show how the proposed density-difference estimator can be used in L2-distance approximation. Finally, we experimentally demonstrate the usefulness of the proposed method in robust distribution comparison such as class-prior estimation and change-point detection.

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Wu, Jingjing, and Rohana J. Karunamuni. "Profile Hellinger distance estimation." Statistics 49, no. 4 (August 12, 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 (June 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 (February 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 (December 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 (January 1995): 297–314. http://dx.doi.org/10.1016/0083-6656(95)00089-6.

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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 (June 26, 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 out-of-distribution samples. We show that the proposed distance-aware uncertainty estimator is able to offer better uncertainty estimation compared to previous methods. Experimental results demonstrate that our proposed DARL method is competitive to the state-of-the-art methods in offline evaluation tasks.

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Esch, H., and J. Burns. "Distance estimation by foraging honeybees." Journal of Experimental Biology 199, no. 1 (January 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 a feeder suspended from a balloon at 70 m from their hive underestimate the distance flown dramatically when the balloon lifts the feeder from ground level to 90 m, even though the energy required to reach the feeder increases considerably. Foragers from a hive situated on a high building (50 m) that fly to a feeder on the roof of another high building (34 m) signal a much shorter distance than the actual distance flown. We propose instead an 'optical flow hypothesis': bees use the speed of retinal image motion perceived from the ground to estimate the distance flown. Flight altitude is important for distance estimation by retinal image flow, because objects move faster and farther across the retina when the bee flies closer to the ground. When the forager's flight behavior is considered, the optical flow hypothesis does not conflict with previous findings.

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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 (September 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 environment than it was for the replication condition in the augmented environment. These results lend further support to the notion that verbal estimates of distance do not accurately represent perceived distance. Unless the task being performed specifically requires a numerical estimate of distance, it is recommended that methods similar to our distance replication method be used to accurately determine perceived distance.

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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 problematic, because they might lead to distortions in cognitive representations of space acquired via interaction with VR environments. By conducting a VR-based distance estimation study, we demonstrate how changes of artificial locomotion speed affect distance estimations in VR. Increasing locomotion speeds after letting users adapt to a lower locomotion speed led to lower distance estimations and decreasing locomotion speeds led to higher distance estimations. These findings should sensitize VR developers to consider the choice of applied locomotion techniques when a developed VR application is supposed to communicate distance information or to support the acquisition of a cognitive representation of geographic space.

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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 (December 28, 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 Vessel-Focused Distance Estimation (VFDE) and the novel Vessel Object-Focused Distance Estimation (VOFDE). While VFDE uses the predefined height of a vessel for distance estimation, VOFDE uses standardized dimensions of objects on the vessel, detected with a Convolutional Neural Network (CNN) for instance segmentation to enhance estimation accuracy. Our evaluation covers distances up to 414 m, which is significantly beyond the scope of previous studies. When compared to the distances measured with a precise instrument, VOFDE achieves a Percentage Deviation Index (PDI) of 1.34% to 9.45%. This advance holds significant potential for improving maritime surveillance with monocular cameras and is also applicable in other areas, such as low-cost maritime vehicles equipped with single cameras.

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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 (January 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 and time estimation using a virtual navigation task. We found that a negative aftereffect occurred in the distance reproduction task after repeated exposure to self-motion with a fixed travel distance. No such aftereffect occurred in the time reproduction task after repeated exposure to self-motion with a fixed elapsed time. Further, the aftereffect in distance reproduction occurred only when the distance of the adapting stimulus was fixed, suggesting that it did not reflect adaptation to time, which varied with distance. The estimation of spatial and temporal aspects of self-motion is thus processed by partially separable mechanisms, with the distance estimation being similar to the estimation of other magnitude dimensions.

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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 (September 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 analysis of the percentage error in estimation revealed that police performed as well as the rest of the population. The age of the subjects and their rating of their own vision acuity made no statistically significant difference in their average estimation error. Males had significantly lower estimation error than females, as did subjects who rated themselves highly with respect to their distance estimation ability.

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Liang, Hong, Zizhen Ma, and Qian Zhang. "Self-Supervised Object Distance Estimation Using a Monocular Camera." Sensors 22, no. 8 (April 12, 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, a multi-scale resolution is applied to improve estimation accuracy by enriching the expression ability of depth information. We validated the results of object detection and distance estimation on the KITTI dataset and demonstrated that our approach is efficient and accurate. Finally, we construct a dataset and conduct similar experiments to verify the generality of the network in other scenarios. The results show that our proposed methods outperform alternative approaches on object-specific distance estimation.

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Keyson, David V. "Estimation of Virtually Perceived Length." Presence: Teleoperators and Virtual Environments 9, no. 4 (August 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 distance judgments was examined. Based on the method of direct estimation of magnitude, a high correlation between tactual and visual estimates for eight path lengths was found. The results of the tactual condition with simulated friction indicated that the perceived distance between felt objects can be manipulated without requiring longer movements of an input device. In general, results indicated that the spatial relations between objects can be accurately communicated by virtual tactual paths.

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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 (July 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 hectare, and volume per hectare.

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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 conditions can be calculated by interpolation of critical distance on fatigue limit (rc) with critical distance on static strength (rc). By unifying these low cycle fatigue life estimation method with high cycle fatigue limit estimation method we can estimate the full range fatigue life easily. And to confirm the availability of this estimation method we perform the fatigue test for any stress concentration specimens.s

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Daum, S. Oliver, and Heiko Hecht. "Distance estimation in vista space." Attention, Perception, & Psychophysics 71, no. 5 (July 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 (January 2004): 621–29. http://dx.doi.org/10.1534/genetics.166.1.621.

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PÎRJOL, Pătru. "PASSIVE RADIOLOCATION. DISTANCE ESTIMATION METHODS." STRATEGIES XXI - Command and Staff College 17, no. 1 (August 11, 2021): 111–18. http://dx.doi.org/10.53477/2668-2028-21-12.

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Abstract: The timely discovery of existing threats in the airspace is a permanent concern of the relevant military powers of this century and sensor networks have been developed in this regard, arranged over very large geographical areas in order to achieve a permanent and continuous surveillance of the areas of interest. Research conducted by the scientific community has demonstrated the potential of bistatic (passive) radar as a means of surveillance, the efforts focusing on improving receiver parameters and signal processing algorithms. An important role in these scientific approaches is played by the geodetic distance estimation methods, as well as by the diversification of technical solutions that provide the necessary support for their application and the establishment of algorithms for refining the data provided.

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Jie Yu, J. Amores, N. Sebe, P. Radeva, and Qi Tian. "Distance Learning for Similarity Estimation." IEEE Transactions on Pattern Analysis and Machine Intelligence 30, no. 3 (March 2008): 451–62. http://dx.doi.org/10.1109/tpami.2007.70714.

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Anderson, Paul W., and Pavel Zahorik. "Auditory and visual distance estimation." Journal of the Acoustical Society of America 129, no. 4 (April 2011): 2487. http://dx.doi.org/10.1121/1.3588197.

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Garg, Vikram V., Luis Tenorio, and Karen Willcox. "Minimum local distance density estimation." Communications in Statistics - Theory and Methods 46, no. 1 (September 30, 2016): 148–64. http://dx.doi.org/10.1080/03610926.2014.988260.

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Zachariah, Dave, Isaac Skog, Magnus Jansson, and Peter Handel. "Bayesian Estimation With Distance Bounds." IEEE Signal Processing Letters 19, no. 12 (December 2012): 880–83. http://dx.doi.org/10.1109/lsp.2012.2224865.

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Cao, Ricardo, Antonio Cuevas, and Ricardo Fraiman. "Minimum distance density-based estimation." Computational Statistics & Data Analysis 20, no. 6 (December 1995): 611–31. http://dx.doi.org/10.1016/0167-9473(94)00065-4.

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Kuroki, F., Y. Wagatsuma, and T. Yoneyama. "Distance Estimation at 60 GHz." IEEE Aerospace and Electronic Systems Magazine 21, no. 5 (May 2006): 26–29. http://dx.doi.org/10.1109/maes.2006.1635171.

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Gizon, L., and A. C. Birch. "Time‐Distance Helioseismology: Noise Estimation." Astrophysical Journal 614, no. 1 (October 10, 2004): 472–89. http://dx.doi.org/10.1086/423367.

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Wu, Jingjing, and Rohana J. Karunamuni. "On minimum Hellinger distance estimation." Canadian Journal of Statistics 37, no. 4 (December 2009): 514–33. http://dx.doi.org/10.1002/cjs.10042.

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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 2 independent distance estimates were obtained for 21 sightings of minke whale, and there was a good agreement between the 2 types of estimates. The relative absolute deviations between the 2 estimates were on average 23% (se: 6%), with the errors in the observer–based distance estimates resembling that of a log-normal distribution. The linear regression of the observer–based estimates (obs) on the image–based estimates (img) was Obs=1.1Img (R2=0.85) with an intercept ﬁxed at zero. There was no evidence of a distance estimation bias that could generate a positive bias in the absolute abundance estimated by cue–counting.

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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 (December 1, 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. The distances measured in the experiment with a set of training data obtained from the image were fitted into a curve using the least-square framework to derive a non-linear function that was used for estimating the object’s distance also known as the z-coordinate. The result from the experiment showed that there was an average error of 1.33 mm between the actual distance and the estimated distance of the object. Hence, this method can be applied in many robotic and autonomous systems applications.

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Ding, Rui, and Andrew Mullhaupt. "Empirical Squared Hellinger Distance Estimator and Generalizations to a Family of α-Divergence Estimators." Entropy 25, no. 4 (April 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 proposed Hellinger affinity estimators to approximately bounding the Neyman–Pearson regions.

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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 (July 19, 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 increasing. This study learned convolutional neural network (CNN)-based faster regions with CNN (Faster R-CNN) and You Only Look Once (YOLO) V2 to improve the recognition techniques of vehicle-mounted monocular cameras for the design of preventive automated driving systems, recognizing surrounding vehicles in black box highway driving videos and estimating distances from surrounding vehicles through more suitable models for automated driving systems. Moreover, we learned the PASCAL visual object classes (VOC) dataset for model comparison. Faster R-CNN showed similar accuracy, with a mean average precision (mAP) of 76.4 to YOLO with a mAP of 78.6, but with a Frame Per Second (FPS) of 5, showing slower processing speed than YOLO V2 with an FPS of 40, and a Faster R-CNN, which we had difficulty detecting. As a result, YOLO V2, which shows better performance in accuracy and processing speed, was determined to be a more suitable model for automated driving systems, further progressing in estimating the distance between vehicles. For distance estimation, we conducted coordinate value conversion through camera calibration and perspective transform, set the threshold to 0.7, and performed object detection and distance estimation, showing more than 80% accuracy for near-distance vehicles. Through this study, it is believed that it will be able to help prevent accidents in automated vehicles, and it is expected that additional research will provide various accident prevention alternatives such as calculating and securing appropriate safety distances, depending on the vehicle types.

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Choi, Jae-Woong, and Eui-Rim Jeong. "CNN based Distance and Velocity Estimation for OFDM Radar Systems." Webology 19, no. 1 (January 20, 2022): 4692–705. http://dx.doi.org/10.14704/web/v19i1/web19313.

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The objective of this paper is to propose a new target distance and velocity estimation technique for OFDM radar systems. First, the 2D periodogram is collected from the reflected signal via FFT of received OFDM symbols. The largest value of a 2D periodogram often represents the target so that its position indicates the distance and velocity. The CFAR is one of the famous conventional techniques to find the peak in the 2D periodogram. In this paper, a CNN based estimator is proposed. The proposed CNN directly finds the distance and velocity from the 2D periodogram. The proposed method requires only 2D periodogram to estimate the target’s distance and velocity. On the other hand, the conventional methods need noise variance as well as the periodogram. The performance is examined through computer simulation. In the simulation, the MAEs are compared between the conventional and proposed methods. According to the results, the MAEs of the proposed method are lower approximately 8 m in distance and 7 km/h in speed to the conventional method. The proposed OFDM radar technique can be applied to 6G mobile communications to identify the moving targets without additional frequency resource allocation for the radar system. In other words, by using the proposed technique, the convergence of the communication and radar can be possible.

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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 (July 1, 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 distance estimations obtained by different methods for stationary and moving sound sources are summarized. The review includes the results of the authors' own studies and a description of promising experimental and applied approaches of this research field.

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Moon, Hyungsik Roger, and Frank Schorfheide. "MINIMUM DISTANCE ESTIMATION OF NONSTATIONARY TIME SERIES MODELS." Econometric Theory 18, no. 6 (September 24, 2002): 1385–407. http://dx.doi.org/10.1017/s0266466602186063.

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This paper analyzes the limit distribution of minimum distance (MD) estimators for nonstationary time series models that involve nonlinear parameter restrictions. A rotation for the restricted parameter space is constructed to separate the components of the MD estimator that converge at different rates. We derive regularity conditions for the restriction function that are easier to verify than the stochastic equicontinuity conditions that arise from direct estimation of the restricted parameters. The sequence of matrices that is used to weigh the discrepancy between the unrestricted estimates and the restriction function is allowed to have a stochastic limit. For MD estimators based on unrestricted estimators with a mixed normal asymptotic distribution the optimal weight matrix is derived and a goodness-of-fit test is proposed. Our estimation theory is illustrated in the context of a permanent-income model and a present-value model.

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Alvarez, Juan, Diego Álvarez, and Antonio López. "Accelerometry-Based Distance Estimation for Ambulatory Human Motion Analysis." Sensors 18, no. 12 (December 15, 2018): 4441. http://dx.doi.org/10.3390/s18124441.

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In human motion science, accelerometers are used as linear distance sensors by attaching them to moving body parts, with their measurement axes its measurement axis aligned in the direction of motion. When double integrating the raw sensor data, multiple error sources are also integrated integrated as well, producing inaccuracies in the final position estimation which increases fast with the integration time. In this paper, we make a systematic and experimental comparison of different methods for position estimation, with different sensors and in different motion conditions. The objective is to correlate practical factors that appear in real applications, such as motion mean velocity, path length, calibration method, or accelerometer noise level, with the quality of the estimation. The results confirm that it is possible to use accelerometers to estimate short linear displacements of the body with a typical error of around 4.5% in the general conditions tested in this study. However, they also show that the motion kinematic conditions can be a key factor in the performance of this estimation, as the dynamic response of the accelerometer can affect the final results. The study lays out the basis for a better design of distance estimations, which are useful in a wide range of ambulatory human motion monitoring applications.

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Journal articles on the topic 'Estimation de la distance'

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