Academic literature on the topic 'Object properties estimation'
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Journal articles on the topic "Object properties estimation"
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El-Dawy, Ahmed, Amr El-Zawawi, and Mohamed El-Habrouk. "MonoGhost: Lightweight Monocular GhostNet 3D Object Properties Estimation for Autonomous Driving." Robotics 12, no. 6 (2023): 155. http://dx.doi.org/10.3390/robotics12060155.
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Effective environmental perception is critical for autonomous driving; thus, the perception system requires collecting 3D information of the surrounding objects, such as their dimensions, locations, and orientation in space. Recently, deep learning has been widely used in perception systems that convert image features from a camera into semantic information. This paper presents the MonoGhost network, a lightweight Monocular GhostNet deep learning technique for full 3D object properties estimation from a single frame monocular image. Unlike other techniques, the proposed MonoGhost network first estimates relatively reliable 3D object properties depending on efficient feature extractor. The proposed MonoGhost network estimates the orientation of the 3D object as well as the 3D dimensions of that object, resulting in reasonably small errors in the dimensions estimations versus other networks. These estimations, combined with the translation projection constraints imposed by the 2D detection coordinates, allow for the prediction of a robust and dependable Bird’s Eye View bounding box. The experimental outcomes prove that the proposed MonoGhost network performs better than other state-of-the-art networks in the Bird’s Eye View of the KITTI dataset benchmark by scoring 16.73% on the moderate class and 15.01% on the hard class while preserving real-time requirements.
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Yang, Hua, Takeshi Takaki, and Idaku Ishii. "Simultaneous Dynamics-Based Visual Inspection Using Modal Parameter Estimation." Journal of Robotics and Mechatronics 23, no. 1 (2011): 180–95. http://dx.doi.org/10.20965/jrm.2011.p0180.
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In this study, we introduce the concept of dynamicsbased visual inspection with High-Frame-Rate (HFR) video analysis as a novel non-destructive active sensing method for verifying dynamic properties of a vibrating object. The HFR video is used for determining the structural dynamic properties of an object, such as its resonant frequencies and mode shapes, which can be estimated as modal parameters by modal analysis only when the object is excited. By improving and implementing a fast output-only modal parameter estimation algorithm on a real-time 2000-fps vision platform, the modal parameters of an excited object are simultaneously estimated as its input-invariant dynamic properties for dynamics-based visual inspection evenwhen the objects undergo different excitation conditions. Our simultaneous 2000-fps visual inspection system can facilitate non-destructive and longterm monitoring of the structures of beam-shaped objects vibrating at dozens or hundreds of hertz, and it can detect small changes in the dynamic properties of these objects caused by internal defects such as fatigue cracks in real time, even when their static appearances are similar. To demonstrate the performance of the proposed 2000-fps simultaneous dynamics-based visual inspection approach, the resonant frequencies and mode shapes for beam-shaped cantilevers with different artificial cracks and weights, excited by human finger tapping, were estimated in real time.
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Mavrovouniotis, Michael L., Suzanne Prickett, and Leonidas Constantinou. "Object-oriented estimation of properties from molecular structure." Computers & Chemical Engineering 16 (May 1992): S353—S360. http://dx.doi.org/10.1016/s0098-1354(09)80042-2.
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Tang, Jie, and Jian Li. "End-to-End Monocular Range Estimation for Forward Collision Warning." Sensors 20, no. 20 (2020): 5941. http://dx.doi.org/10.3390/s20205941.
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Estimating range to the closest object in front is the core component of the forward collision warning (FCW) system. Previous monocular range estimation methods mostly involve two sequential steps of object detection and range estimation. As a result, they are only effective for objects from specific categories relying on expensive object-level annotation for training, but not for unseen categories. In this paper, we present an end-to-end deep learning architecture to solve the above problems. Specifically, we represent the target range as a weighted sum of a set of potential distances. These potential distances are generated by inverse perspective projection based on intrinsic and extrinsic camera parameters, while a deep neural network predicts the corresponding weights of these distances. The whole architecture is optimized towards the range estimation task directly in an end-to-end manner with only the target range as supervision. As object category is not restricted in the training stage, the proposed method can generalize to objects with unseen categories. Furthermore, camera parameters are explicitly considered in the proposed method, making it able to generalize to images taken with different cameras and novel views. Additionally, the proposed method is not a pure black box, but provides partial interpretability by visualizing the produced weights to see which part of the image dominates the final result. We conduct experiments to verify the above properties of the proposed method on synthetic and real-world collected data.
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Imran, Abid, Sang-Hwa Kim, Young-Bin Park, Il Hong Suh, and Byung-Ju Yi. "Singulation of Objects in Cluttered Environment Using Dynamic Estimation of Physical Properties." Applied Sciences 9, no. 17 (2019): 3536. http://dx.doi.org/10.3390/app9173536.
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This paper presents a scattering-based technique for object singulation in a cluttered environment. An analytical model-based control scattering approach is necessary for controlled object singulation. Controlled scattering implies achieving the desired distances between objects after collision. However, current analytical approaches are limited due to insufficient information of the physical environment properties, such as the coefficient of restitution, coefficient of friction, and masses of objects. In this paper, this limitation is overcome by introducing a technique to learn these parameters from unlabeled videos. For the analytical model, an impulse-based approach is used. A virtual world simulator is designed based on a dynamic model and the estimated physical properties of all objects in the environment. Experiments are performed in a virtual world until the targeted scattering pattern is achieved. The targeted scattering pattern implies that all objects are singulated. Finally, the desired input from the virtual world is fed to the robot manipulator to perform real-world scattering.
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Heczko, Dominik, Petr Oščádal, Tomáš Kot, et al. "Finding the Optimal Pose of 2D LLT Sensors to Improve Object Pose Estimation." Sensors 22, no. 4 (2022): 1536. http://dx.doi.org/10.3390/s22041536.
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In this paper, we examine a method for improving pose estimation by correctly positioning the sensors relative to the scanned object. Three objects made of different materials and using different manufacturing technologies were selected for the experiment. To collect input data for orientation estimation, a simulation environment was created where each object was scanned at different poses. A simulation model of the laser line triangulation sensor was created for scanning, and the optical surface properties of the scanned objects were set to simulate real scanning conditions. The simulation was verified on a real system using the UR10e robot to rotate and move the object. The presented results show that the simulation matches the real measurements and that the appropriate placement of the sensors has improved the orientation estimation.
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Coenen, M., F. Rottensteiner, and C. Heipke. "DETECTION AND 3D MODELLING OF VEHICLES FROM TERRESTRIAL STEREO IMAGE PAIRS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 505–12. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-505-2017.
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The detection and pose estimation of vehicles plays an important role for automated and autonomous moving objects e.g. in autonomous driving environments. We tackle that problem on the basis of street level stereo images, obtained from a moving vehicle. Processing every stereo pair individually, our approach is divided into two subsequent steps: the vehicle detection and the modelling step. For the detection, we make use of the 3D stereo information and incorporate geometric assumptions on vehicle inherent properties in a firstly applied generic 3D object detection. By combining our generic detection approach with a state of the art vehicle detector, we are able to achieve satisfying detection results with values for completeness and correctness up to more than 86%. By fitting an object specific vehicle model into the vehicle detections, we are able to reconstruct the vehicles in 3D and to derive pose estimations as well as shape parameters for each vehicle. To deal with the intra-class variability of vehicles, we make use of a deformable 3D active shape model learned from 3D CAD vehicle data in our model fitting approach. While we achieve encouraging values up to 67.2% for correct position estimations, we are facing larger problems concerning the orientation estimation. The evaluation is done by using the object detection and orientation estimation benchmark of the KITTI dataset (Geiger et al., 2012).
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Papadaki, Alexandra, and Maria Pateraki. "6D Object Localization in Car-Assembly Industrial Environment." Journal of Imaging 9, no. 3 (2023): 72. http://dx.doi.org/10.3390/jimaging9030072.
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In this work, a visual object detection and localization workflow integrated into a robotic platform is presented for the 6D pose estimation of objects with challenging characteristics in terms of weak texture, surface properties and symmetries. The workflow is used as part of a module for object pose estimation deployed to a mobile robotic platform that exploits the Robot Operating System (ROS) as middleware. The objects of interest aim to support robot grasping in the context of human–robot collaboration during car door assembly in industrial manufacturing environments. In addition to the special object properties, these environments are inherently characterised by cluttered background and unfavorable illumination conditions. For the purpose of this specific application, two different datasets were collected and annotated for training a learning-based method that extracts the object pose from a single frame. The first dataset was acquired in controlled laboratory conditions and the second in the actual indoor industrial environment. Different models were trained based on the individual datasets and a combination of them were further evaluated in a number of test sequences from the actual industrial environment. The qualitative and quantitative results demonstrate the potential of the presented method in relevant industrial applications.
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Zarepour, Mohammad Saleh. "AVICENNA ON GRASPING MATHEMATICAL CONCEPTS." Arabic Sciences and Philosophy 31, no. 1 (2021): 95–126. http://dx.doi.org/10.1017/s0957423920000090.
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AbstractAccording to Avicenna, some of the objects of mathematics exist and some do not. Every existing mathematical object is a non-sensible connotational attribute of a physical object and can be perceived by the faculty of estimation. Non-existing mathematical objects can be represented and perceived by the faculty of imagination through separating and combining parts of the images of existing mathematical objects that are previously perceived by estimation. In any case, even non-existing mathematical objects should be considered as properties of material entities. They can never be grasped as fully immaterial entities. Avicenna believes that we cannot grasp any mathematical concepts unless we first have some specific perceptual experiences. It is only through the ineliminable and irreplaceable operation of the faculties of estimation and imagination upon some sensible data that we can grasp mathematical concepts. This shows that Avicenna endorses some sort of concept empiricism about mathematics.
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Freitas, Vander Luis de Souza, Barbara Maximino da Fonseca Reis, and Antonio Maria Garcia Tommaselli. "AUTOMATIC SHADOW DETECTION IN AERIAL AND TERRESTRIAL IMAGES." Boletim de Ciências Geodésicas 23, no. 4 (2017): 578–90. http://dx.doi.org/10.1590/s1982-21702017000400038.
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Abstract: Shadows exist in almost all aerial and outdoor images, and they can be useful for estimating Sun position estimation or measuring object size. On the other hand, they represent a problem in processes such as object detection/recognition, image matching, etc., because they may be confused with dark objects and change the image radiometric properties. We address this problem on aerial and outdoor color images in this work. We use a filter to find low intensities as a first step. For outdoor color images, we analyze spectrum ratio properties to refine the detection, and the results are assessed with a dataset containing ground truth. For the aerial case we validate the detections depending of the hue component of pixels. This stage takes into account that, in deep shadows, most pixels have blue or violet wavelengths because of an atmospheric scattering effect.
Dissertations / Theses on the topic "Object properties estimation"
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Fathollahi, Ghezelghieh Mona. "Estimation of Human Poses Categories and Physical Object Properties from Motion Trajectories." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6835.
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Despite the impressive advancements in people detection and tracking, safety is still a key barrier to the deployment of autonomous vehicles in urban environments [1]. For example, in non-autonomous technology, there is an implicit communication between the people crossing the street and the driver to make sure they have communicated their intent to the driver. Therefore, it is crucial for the autonomous car to infer the future intent of the pedestrian quickly. We believe that human body orientation with respect to the camera can help the intelligent unit of the car to anticipate the future movement of the pedestrians. To further improve the safety of pedestrians, it is important to recognize whether they are distracted, carrying a baby, or pushing a shopping cart. Therefore, estimating the fine- grained 3D pose, i.e. (x,y,z)-coordinates of the body joints provides additional information for decision-making units of driverless cars.
In this dissertation, we have proposed a deep learning-based solution to classify the categorized body orientation in still images. We have also proposed an efficient framework based on our body orientation classification scheme to estimate human 3D pose in monocular RGB images.
Furthermore, we have utilized the dynamics of human motion to infer the body orientation in image sequences. To achieve this, we employ a recurrent neural network model to estimate continuous body orientation from the trajectories of body joints in the image plane.
The proposed body orientation and 3D pose estimation framework are tested on the largest 3D pose estimation benchmark, Human3.6m (both in still images and video), and we have proved the efficacy of our approach by benchmarking it against the state-of-the-art approaches.
Another critical feature of self-driving car is to avoid an obstacle. In the current prototypes the car either stops or changes its lane even if it causes other traffic disruptions. However, there are situations when it is preferable to collide with the object, for example a foam box, rather than take an action that could result in a much more serious accident than collision with the object. In this dissertation, for the first time, we have presented a novel method to discriminate between physical properties of these types of objects such as bounciness, elasticity, etc. based on their motion characteristics . The proposed algorithm is tested on synthetic data, and, as a proof of concept, its effectiveness on a limited set of real-world data is demonstrated.
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Pandharkar, Rohit (Rohit Prakash). "Hidden object doppler : estimating motion, size and material properties of moving non-line-of-sight objects in cluttered environments." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/67783.
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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 111-117).<br>The thesis presents a framework for Non-Line-of-Sight Computer Vision techniques using wave fronts. Using short-pulse illumination and a high speed time-of-flight camera, we propose algorithms that use multi path light transport analysis to explore the environments beyond line of sight. What is moving around the corner interests everyone including a driver taking a turn, a surgeon performing laparoscopy and a soldier entering enemy base. State of the art techniques that do range imaging are limited by (i) inability to handle multiple diffused bounces [LIDAR] (ii) Wavelength dependent resolution limits [RADAR] and (iii) inability to map real life objects [Diffused Optical Tomography]. This work presents a framework for (a) Imaging the changing Space-time-impulse-responses of moving objects to pulsed illumination (b) Tracking motion along with absolute positions of these hidden objects and (c) recognizing their default properties like material and size and reflectance. We capture gated space-time impulse responses of the scene and their time differentials allow us to gauge absolute positions of moving objects with knowledge of only relative times of arrival (as absolute times are hard to synchronize at femto second intervals). Since we record responses at very short time intervals we collect multiple readings from different points of illumination and thus capturing multi-perspective responses allowing us to estimate reflectance properties. Using this, we categorize and give parametric models of the materials around corner. We hope this work inspires further exploration of NLOS computer vision techniques.<br>by Rohit Pandharkar.<br>S.M.
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Chareyre, Maxime. "Apprentissage non-supervisé pour la découverte de propriétés d'objets par découplage entre interaction et interprétation." Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2023. http://www.theses.fr/2023UCFA0122.
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Les robots sont de plus en plus utilisés pour réaliser des tâches dans des environnements contrôlés. Leur utilisation en milieu ouvert est cependant encore confrontée à des difficultés. L'agent robotique est en effet susceptible de rencontrer des objets dont il ignore le comportement et la fonction. Dans certains cas, il doit interagir avec ces éléments pour réaliser sa mission en les collectant ou en les déplaçant mais, sans la connaissance de leurs propriétés dynamiques il n'est pas possible de mettre en place une stratégie de résolution de la mission efficace.Dans cette thèse, nous présentons une méthode visant à apprendre à un robot autonome une stratégie d'interaction physique avec des objets inconnus, sans aucune connaissance a priori, l'objectif étant d'extraire de l'information sur un maximum de propriétés physiques de l'objet à partir des interactions observées par ses capteurs. Les méthodes existantes pour la caractérisation d'objets par interactions physiques ne répondent pas entièrement à ces critères. En effet, les interactions établies ne permettent qu'une représentation implicite de la dynamique des objets, nécessitant une supervision pour identifier leurs propriétés. D'autre part, la solution proposée s'appuie sur des scénarios peu réalistes sans agent. Notre approche se distingue de l'état de l'art en proposant une méthode générique pour l'apprentissage de l'interaction, indépendante de l'objet et de ses propriétés, et pouvant donc être découplée de la phase de leurs prédictions. Cela permet notamment de mener à un pipeline global totalement non-supervisé.Dans une première phase, nous proposons d'apprendre une stratégie d'interaction avec l'objet via une méthode d'apprentissage par renforcement non-supervisée, en utilisant un signal de motivation intrinsèque qui repose sur l'idée de maximisation des variations d'un vecteur d'état de l'objet. Le but est d'obtenir une série d'interactions contenant des informations fortement corrélées aux propriétés physiques de l'objet. Cette méthode a été testée sur un robot simulé interagissant par poussée et a permis d'identifier avec précision des propriétés telles que la masse, la forme de l'objet et les frottements.Dans une seconde phase, nous réalisons l'hypothèse que les vraies propriétés physiques définissent un espace latent explicatif des comportements de l'objet et que cet espace peut être identifié à partir des observations recueillies grâce aux interactions de l'agent. Nous mettons en place une tâche de prédiction auto-supervisée dans laquelle nous adaptons une architecture de l'état de l'art pour construire cet espace latent. Nos simulations confirment que la combinaison du modèle comportemental avec cette architecture permet de faire émerger une représentation des propriétés de l'objet dont les composantes principales s'avèrent fortement corrélées avec les propriétés physiques de l'objet.Les propriétés des objets étant extraites, l'agent peut les exploiter pour améliorer son efficacité dans des tâches impliquant ces objets. Nous concluons cette étude par une mise en avant du gain de performance de l'agent au travers d'un entraînement via l'apprentissage par renforcement sur une tâche simplifiée de repositionnement d'objet où les propriétés sont parfaitement connues.L'intégralité du travail effectué en simulation confirme l'efficacité d'une méthode novatrice visant à découvrir en autonomie les propriétés physiques d'un objet au travers d'interactions physiques d'un robot. Les perspectives d'extension de ces travaux concernent le transfert vers un robot réel en milieu encombré<br>Robots are increasingly used to achieve tasks in controlled environments. However, their use in open environments is still fraught with difficulties. Robotic agents are likely to encounter objects whose behaviour and function they are unaware of. In some cases, it must interact with these elements to carry out its mission by collecting or moving them, but without knowledge of their dynamic properties it is not possible to implement an effective strategy for resolving the mission.In this thesis, we present a method for teaching an autonomous robot a physical interaction strategy with unknown objects, without any a priori knowledge, the aim being to extract information about as many of the object's physical properties as possible from the interactions observed by its sensors. Existing methods for characterising objects through physical interactions do not fully satisfy these criteria. Indeed, the interactions established only provide an implicit representation of the object's dynamics, requiring supervision to identify their properties. Furthermore, the proposed solution is based on unrealistic scenarios without an agent. Our approach differs from the state of the art by proposing a generic method for learning interaction that is independent of the object and its properties, and can therefore be decoupled from the prediction phase. In particular, this leads to a completely unsupervised global pipeline.In the first phase, we propose to learn an interaction strategy with the object via an unsupervised reinforcement learning method, using an intrinsic motivation signal based on the idea of maximising variations in a state vector of the object. The aim is to obtain a set of interactions containing information that is highly correlated with the object's physical properties. This method has been tested on a simulated robot interacting by pushing and has enabled properties such as the object's mass, shape and friction to be accurately identified.In a second phase, we make the assumption that the true physical properties define a latent space that explains the object's behaviours and that this space can be identified from observations collected through the agent's interactions. We set up a self-supervised prediction task in which we adapt a state-of-the-art architecture to create this latent space. Our simulations confirm that combining the behavioural model with this architecture leads to the emergence of a representation of the object's properties whose principal components are shown to be strongly correlated with the object's physical properties.Once the properties of the objects have been extracted, the agent can use them to improve its efficiency in tasks involving these objects. We conclude this study by highlighting the performance gains achieved by the agent through training via reinforcement learning on a simplified object repositioning task where the properties are perfectly known.All the work carried out in simulation confirms the effectiveness of an innovative method aimed at autonomously discovering the physical properties of an object through the physical interactions of a robot. The prospects for extending this work involve transferring it to a real robot in a cluttered environment
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Taati, BABAK. "Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces." Thesis, 2009. http://hdl.handle.net/1974/5107.
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We formulate Local Shape Descriptor selection for model-based object recognition in range data as an optimization problem and offer a platform that facilitates a solution. The goal of object recognition is to identify and localize objects of interest in an image. Recognition is often performed in three phases: point matching, where correspondences are established between points on the 3-D surfaces of the models and the range image; hypothesis generation, where rough alignments are found between the image and the visible models; and pose refinement, where the accuracy of the initial alignments is improved. The overall efficiency and reliability of a recognition system is highly influenced by the effectiveness of the point matching phase. Local Shape Descriptors are used for establishing point correspondences by way of encapsulating local shape, such that similarity between two descriptors indicates geometric similarity between their respective neighbourhoods.
We present a generalized platform for constructing local shape descriptors that subsumes a large class of existing methods and allows for tuning descriptors to the geometry of specific models and to sensor characteristics. Our descriptors, termed as Variable-Dimensional Local Shape Descriptors, are constructed as multivariate observations of several local properties and are represented as histograms. The optimal set of properties, which maximizes the performance of a recognition system, depend on the geometry of the objects of interest and the noise characteristics of range image acquisition devices and is selected through pre-processing the models and sample training images. Experimental analysis confirms the superiority of optimized descriptors over generic ones in recognition tasks in LIDAR and dense stereo range images.<br>Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2009-09-01 11:07:32.084
Book chapters on the topic "Object properties estimation"
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Bader, Markus. "Chapter 2. How free is the position of German object pronouns?" In Studies in Language Companion Series. John Benjamins Publishing Company, 2023. http://dx.doi.org/10.1075/slcs.234.02bad.
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Corpus studies show that weak object pronouns in German directly precede or follow the subject, depending on properties of the subject, including weight, animacy and thematic role. Whether the same factors also affect the acceptability of sentences with object pronouns was investigated in three magnitude-estimation experiments. The results show that both orders (object pronoun before/after subject) are highly acceptable, with some small acceptability differences depending on weight, animacy and thematic roles. Based on these results, the hypothesis is advanced that the position of weak object pronouns in German relative to the subject is an instance of free variation within the grammar but choosing a specific order during language production follows general production preferences and is thus not random.
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Yao, Wei, and Jianwei Wu. "Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics." In Urban Informatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_22.
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AbstractIn this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR. Both static and dynamical properties of large-scale urban areas can be characterized in a highly automatic way. First, LiDAR point clouds are colorized by co-registration with images if available. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per-pixel or per-point. Then, various spatial-statistical and spectral features can be extracted using a cuboid volumetric neighborhood. The most important features highlighted by the feature-relevance assessment, such as LiDAR intensity, NDVI, and planarity or covariance-based features, are selected to span the feature space for the AdaBoost classifier. Classification results as labeled points or pixels are acquired based on pre-selected training data for the objects of building, tree, vehicle, and natural ground. Based on the urban classification results, traffic-related vehicle motion can further be indicated and determined by analyzing and inverting the motion artifact model pertinent to airborne LiDAR. The performance of the developed strategy towards detecting various urban objects is extensively evaluated using both public ISPRS benchmarks and peculiar experimental datasets, which were acquired across European and Canadian downtown areas. Both semantic and geometric criteria are used to assess the experimental results at both per-pixel and per-object levels. In the datasets of typical city areas requiring co-registration of imagery and LiDAR point clouds a priori, the AdaBoost classifier achieves a detection accuracy of up to 90% for buildings, up to 72% for trees, and up to 80% for natural ground, while a low and robust false-positive rate is observed for all the test sites regardless of object class to be evaluated. Both theoretical and simulated studies for performance analysis show that the velocity estimation of fast-moving vehicles is promising and accurate, whereas slow-moving ones are hard to distinguish and yet estimated with acceptable velocity accuracy. Moreover, the point density of ALS data tends to be related to system performance. The velocity can be estimated with high accuracy for nearly all possible observation geometries except for those vehicles moving in or (quasi-)along the track. By comparative performance analysis of the test sites, the performance and consistent reliability of the developed strategy for the detection and characterization of urban objects and traffic dynamics from airborne LiDAR data based on selected features was validated and achieved.
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Ivanov, Sergey S. "“Counter—Scaling” Method for Estimation of Fractal Properties of Self—Affine Objects." In Fractals and Dynamic Systems in Geoscience. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-07304-9_30.
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Fugl, Andreas Rune, Andreas Jordt, Henrik Gordon Petersen, Morten Willatzen, and Reinhard Koch. "Simultaneous Estimation of Material Properties and Pose for Deformable Objects from Depth and Color Images." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32717-9_17.
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Sidorkina, Irina, and Aleksey Rуbakov. "Creating Model of E-Course." In Handbook of Research on Estimation and Control Techniques in E-Learning Systems. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9489-7.ch019.
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Modern educational systems function is as a medium for rapid analysis of shared information (FASMI), which defines information as analytical. The chapter shows the purpose of such analysis systems: work with distributed data in global computer networks, production and processing of semi structured information and knowledge. The purpose of this chapter based on last publications professors Irina Sidorkina (Volga State University of Technology) and Vardan Mkrttchian (HHH University) create model and program of Development E-course – as an object of Computer – Aided Design (CAD). The development of the methods and programming tools of synthesis of the new design object combining the record of optimization criteria, adaptive and intelligent components of design, distributed information processing and the Internet-based interaction creates favorable conditions for automation and adds new system properties to the present-day CAD. Dr. Alexey Rybakov (Omega-R Inc.) was a designing a software on the subject of this chapter.
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Dospisil, Jana. "Software Metrics, Information and Entropy." In Practicing Software Engineering in the 21st Century. IGI Global, 2003. http://dx.doi.org/10.4018/978-1-93177-750-6.ch009.
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This chapter describes the foundation and properties of object-oriented software measures. Many software measures for object-oriented applications have been developed and tested in the development environment. However, the process of defining new measures is still alive. The reason for such development lies in difficulties associated with understanding and maintaining object-oriented applications. It is still difficult to relate the measures to the phenomena we want to improve. Do our measurements indicate problems in reliability, maintenance, or too much complexity of some portions of the application? In order to reduce the complexity of software, new development methodologies and tools are being introduced. An example of the new approach to development is separation of concern. The tools, such as Aspect/J (Kiezales et al., 1997) or Hyper/J (Ossher & Tarr, 1998), facilitate the development process. There does not seem to be a sound metrics suite to measure complexity and efficiency of applications developed and coded with Aspect/J or Hyper/J. In this chapter, we attempt to review the current research into object-oriented software metrics and suggest theoretical framework for complexity estimation and ranking of compositional units in object-oriented applications developed with Hyper/J.
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Chernyshenko, Serge V. "Design of Avatars With “Differential” Logic." In Avatar-Based Control, Estimation, Communications, and Development of Neuron Multi-Functional Technology Platforms. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1581-5.ch006.
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The chapter is devoted to the analysis of possibilities in designing dynamic properties of avatars in multi-agent systems. It is shown that determination of “differential logic” of avatars, based on the use of differential equations, gives sufficient flexibility in describing their behavior over time. At the same time, the differential description usually involves a smooth response of the object to external or internal influences, which for avatars is usually not correct. To eliminate this weak point, it is proposed to use the technology of internal bifurcations, which allows to simulate discontinues effects in the avatar dynamics. It is shown that even when using relatively simple quadratic models of the Lotka-Volterra type, the technique allows to describe rather complex information interactions in the multi-agent systems.
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"Probability and Statistics." In Examining an Operational Approach to Teaching Probability. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3871-5.ch009.
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Statistics and probability both fall within the broader scope of the theory of random phenomena. The first deals with providing probability distributions adaptable to the various real random phenomena, and the second deals very often with a random sample to describe its properties or infer to the underlying probabilistic model and the estimation of its parameters. The chapter tries to show this connection by reporting examples that are more or less known but that are characterized by being unconventional. Other objects could have been taken into consideration but those chosen are characterized by a closer link with the calculation of probabilities.
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Connolly, Andrew J., Jacob T. VanderPlas, Alexander Gray, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray. "Classification." In Statistics, Data Mining, and Machine Learning in Astronomy. Princeton University Press, 2014. http://dx.doi.org/10.23943/princeton/9780691151687.003.0009.
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Chapter 6 described techniques for estimating joint probability distributions from multivariate data sets and for identifying the inherent clustering within the properties of sources. This approach can be viewed as the unsupervised classification of data. If, however, we have labels for some of these data points (e.g., an object is tall, short, red, or blue) we can utilize this information to develop a relationship between the label and the properties of a source. We refer to this as supervised classification, which is the focus of this chapter. The motivation for supervised classification comes from the long history of classification in astronomy. Possibly the most well known of these classification schemes is that defined by Edwin Hubble for the morphological classification of galaxies based on their visual appearance. This chapter discusses generative classification, k-nearest-neighbor classifier, discriminative classification, support vector machines, decision trees, and evaluating classifiers.
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Gouda, Maha, Mostafa Atiaa, and Omar Abdel-Kareem. "Investigation and Analysis of Ancient Dyed Textiles." In Preservation and Restoration Techniques for Ancient Egyptian Textiles. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-4811-0.ch005.
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A material investigation is a very important step in the documentation of the component's properties for materials of an object, in estimating its condition, and in considering appropriate conservation treatments. Textile fibers are the fundamental unit of material that makes up fabrics and textiles. Fibers are frequently classed as either natural or man-made, though there are also several categories and sub-types. Natural fibers are further categorized based on their origin (vegetable, animal, or mineral). According to that, this chapter deals with investigation methods for both main components of ancient textiles, fibers, and dyes; as the analysis of the materials utilized in textile dyeing could also be a valuable tool to know how an object originally looked, how old it is, and where it comes from. This data allows conservators to settle on appropriate procedures for restoration and dating textiles depending upon its manufacturing discovery date and its type of dye.
Conference papers on the topic "Object properties estimation"
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Avant, Trevor, and Kristi A. Morgansen. "Observability Properties of Object Pose Estimation." In 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8814791.
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Liu, T., A. Klotzsche, M. Pondkule, H. Vereecken, J. van der Kruk, and Y. Su. "Estimation of subsurface cylindrical object properties from GPR full-waveform inversion." In 2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR). IEEE, 2017. http://dx.doi.org/10.1109/iwagpr.2017.7996064.
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Ahola, Jari M., and Tapio Heikkilä. "Object Recognition and Pose Estimation Based on Combined Use of Projection Histograms and Surface Fitting." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67315.
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In this paper we present a configurable object recognition and locating system for 3D point cloud sensors. The objects are recognized based on cylindrical projection histograms and after the object is recognized, the initial pose of the object is computed based on the eigenvectors of the modelled and measured 3D point clusters. The optimal 6 degree of freedom pose is estimated by fitting the CAD-model surfaces to the measured 3D-points, where the model surfaces and 3D points are correlated based on the modelled and measured eigenvectors. The novelty of our system is the combination of reliable histogram based object recognition and accurate CAD-based pose estimation in the object recognition system with configurability options according to application requirements and point cloud properties.
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Kurrant, Douglas J., and Elise C. Fear. "Estimation of regional geometric and spatially averaged dielectric properties of an object." In 2012 IEEE Antennas and Propagation Society International Symposium and USNC/URSI National Radio Science Meeting. IEEE, 2012. http://dx.doi.org/10.1109/aps.2012.6348049.
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Pavlic, Marko, Timo Markert, Sebastian Matich, and Darius Burschka. "RobotScale: A Framework for Adaptable Estimation of Static and Dynamic Object Properties with Object-dependent Sensitivity Tuning." In 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, 2023. http://dx.doi.org/10.1109/ro-man57019.2023.10309315.
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Petsch, Susanne, and Darius Burschka. "Estimation of spatio-temporal object properties for manipulation tasks from observation of humans." In 2010 IEEE International Conference on Robotics and Automation (ICRA 2010). IEEE, 2010. http://dx.doi.org/10.1109/robot.2010.5509885.
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Tanaka, M., S. Amari, and T. Horiuchi. "MODELLING OF PERCEPTUAL GLOSS UNDER MIXED LIGHTING CONDITIONS." In CIE 2023 Conference. International Commission on Illumination, CIE, 2023. http://dx.doi.org/10.25039/x50.2023.po174.
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In everyday life, humans acquire the appearance of objects in various lighting environments. However, the physical and perceived glosses do not always match in environments with diverse object properties and lighting conditions. Therefore, we challenge the construction of a perceptual gloss estimation model for diffuse and highly directional illuminations. We analysed the relationship between physical features and perceptual gloss by measuring the physical properties of object surfaces, including the physical gloss. The perceptual gloss was visually evaluated using the magnitude estimation method. Real object samples consisting of 10 different materials, including paper, resin, and metal, were prepared for the experiment. Using these physical features and perceptual gloss, a prediction model of perceptual gloss was constructed via regression analysis, indicating that physical quantities can predict perceptual gloss with reasonable accuracy, depending on the lighting conditions.
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Cretu, Ana-Maria, Emil M. Petriu, Pierre Payeur, and Fouad F. Khalil. "Estimation of deformable object properties from shape and force measurements for virtualized reality applications." In 2010 IEEE International Workshop on Haptic Audio Visual Environments and Games (HAVE 2010). IEEE, 2010. http://dx.doi.org/10.1109/have.2010.5623970.
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Murooka, Masaki, Shunichi Nozawa, Yohei Kakiuchi, Kei Okada, and Masayuki Inaba. "Feasibility evaluation of object manipulation by a humanoid robot based on recursive estimation of the object's physical properties." In 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017. http://dx.doi.org/10.1109/icra.2017.7989469.
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Seacat, R. H., Harrison H. Barrett, R. L. Shoemaker, T. J. Roney, and W. J. Smith. "Parallel processor for optimization and estimation problems: algorithms." In OSA Annual Meeting. Optica Publishing Group, 1988. http://dx.doi.org/10.1364/oam.1988.fo3.
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The simulated annealing algorithm is a generalized optimization algorithm suitable for the reconstruction of images from data obtained through coded aperture projections. The best estimate of an unknown object is the estimate that minimizes an energy function derived from the known and inferred properties of the original object and the process.