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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 (November 17, 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 (February 20, 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 (October 21, 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 (August 28, 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, Adam Boleslavský, Václav Krys, Jan Bém, Ivan Virgala, and Zdenko Bobovský. "Finding the Optimal Pose of 2D LLT Sensors to Improve Object Pose Estimation." Sensors 22, no. 4 (February 16, 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 (March 20, 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 (March 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 (December 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.
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KIM, SUNGHO, GIJEONG JANG, WANG-HEON LEE, and IN SO KWEON. "COMBINED MODEL-BASED 3D OBJECT RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 07 (November 2005): 839–52. http://dx.doi.org/10.1142/s0218001405004368.
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This paper presents a combined model-based 3D object recognition method motivated by the robust properties of human vision. The human visual system (HVS) is very efficient and robust in identifying and grabbing objects, in part because of its properties of visual attention, contrast mechanism, feature binding, multiresolution and part-based representation. In addition, the HVS combines bottom-up and top-down information effectively using combined model representation. We propose a method for integrating these aspects under a Monte Carlo method. In this scheme, object recognition is regarded as a parameter optimization problem. The bottom-up process initializes parameters, and the top-down process optimizes them. Experimental results show that the proposed recognition model is feasible for 3D object identification and pose estimation.
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Lanneau, Sylvain, Frédéric Boyer, Vincent Lebastard, and Stéphane Bazeille. "Model based estimation of ellipsoidal object using artificial electric sense." International Journal of Robotics Research 36, no. 9 (June 3, 2017): 1022–41. http://dx.doi.org/10.1177/0278364917709942.
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In this article we address the issue of shape estimation using electric sense inspired by the active electric fish. These fish can perceive their environment by measuring the perturbations in a self-generated electric field caused by nearby objects. The approach proceeded in three stages. Firstly, the object was detected and its electric properties (insulator or conductor) identified. Secondly, the object was localized using the multiple signal classification algorithm, which was originally developed to localize a radio wave emitter using a network of antennas. Thirdly, the shape estimation relied on the concept of generalized polarization tensor, which enabled us to model the electric response of an object polarized by an ambient electric field. We describe the implementation of the approach through numerous experiments. The system was able to estimate shape with an average error of 16%, and opened the way toward further improvements. In particular, self-aligning the sensor with the ellipsoid through a reactive feedback makes the shape estimation errors drop to 10%.
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Zhang, Qi Jun. "Modeling and Estimation of Rheological Properties Based on Finite Element Analysis and Computer Simulation Technology." Applied Mechanics and Materials 427-429 (September 2013): 293–97. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.293.
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By the relevant engineering and manufacturing of food rheological properties to estimate, and developing unified object based on 2D/3D dynamic finite element model, to carry out physical model simulation of five elements, then to further extended to deal with heterogeneous hierarchical objects. There are three kinds of food raw materials that are tested, its deformation and mechanical behavior are also assessed, and then according to the optimization FE, it is proposed to estimate the object's rheological properties. The results show that the FE model and the estimation method can accurately reproduce food rheological and deformation, and in the manufacturing process, the finite element model can be used to predict the rheological behavior of food products.
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Rothe, Christoph, and Sergio Firpo. "PROPERTIES OF DOUBLY ROBUST ESTIMATORS WHEN NUISANCE FUNCTIONS ARE ESTIMATED NONPARAMETRICALLY." Econometric Theory 35, no. 05 (December 3, 2018): 1048–87. http://dx.doi.org/10.1017/s0266466618000385.
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An estimator of a finite-dimensional parameter is said to be doubly robust (DR) if it imposes parametric specifications on two unknown nuisance functions, but only requires that one of these two specifications is correct in order for the estimator to be consistent for the object of interest. In this article, we study versions of such estimators that use local polynomial smoothing for estimating the nuisance functions. We show that such semiparametric two-step (STS) versions of DR estimators have favorable theoretical and practical properties relative to other commonly used STS estimators. We also show that these gains are not generated by the DR property alone. Instead, it needs to be combined with an orthogonality condition on the estimation residuals from the nonparametric first stage, which we show to be satisfied in a wide range of models.
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Dapkus, Paulius, Liudas Mažeika, and Vytautas Sliesoraitis. "A study of supervised combined neural-network-based ultrasonic method for reconstruction of spatial distribution of material properties." Information Technology And Control 49, no. 3 (September 23, 2020): 381–94. http://dx.doi.org/10.5755/j01.itc.49.3.26792.
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This paper examines the performance of the commonly used neural-network-based classifiers for investigating a structural noise in metals as grain size estimation. The biggest problem which aims to identify the object structure grain size based on metal features or the object structure itself. When the structure data is obtained, a proposed feature extraction method is used to extract the feature of the object. Afterwards, the extracted features are used as the inputs for the classifiers. This research studies is focused to use basic ultrasonic sensors to obtain objects structural grain size which are used in neural network. The performance for used neural-network-based classifier is evaluated based on recognition accuracy for individual object. Also, traditional neural networks, namely convolutions and fully connected dense networks are shown as a result of grain size estimation model. To evaluate robustness property of neural networks, the original samples data is mixed for three types of grain sizes. Experimental results show that combined convolutions and fully connected dense neural networks with classifiers outperform the others single neural networks with original samples with high SN data. The Dense neural network as itself demonstrates the best robustness property when the object samples not differ from trained datasets.
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Elkattan, Mohamed, and Aladin H. Kamel. "Estimation of Electromagnetic Properties for 2D Inhomogeneous Media Using Neural Networks." Journal of Electromagnetic Engineering and Science 22, no. 2 (March 31, 2022): 152–61. http://dx.doi.org/10.26866/jees.2022.2.r.72.
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Electromagnetic measurements are widely used to gain information about an object through interaction of electromagnetic fields with the physical properties of this object. The inversion problem is the process of estimating object parameters from electromagnetic records. This problem has a nonlinear nature and can be formulated as an optimization scheme. In this paper, we introduce an inversion methodology to estimate the electrical properties of a two-dimensional inhomogeneous layered scattering object. The proposed methodology deals with the inversion problem as a learning process through two multilayer perceptron artificial neural network designs. Several neural network design parameters were tuned to achieve the best inversion performance. Moreover, the proposed neural networks were tested against noise presence in terms of error criteria and proved to be effective in solving the inverse scattering problem.
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Desingh, Karthik, Shiyang Lu, Anthony Opipari, and Odest Chadwicke Jenkins. "Efficient nonparametric belief propagation for pose estimation and manipulation of articulated objects." Science Robotics 4, no. 30 (May 22, 2019): eaaw4523. http://dx.doi.org/10.1126/scirobotics.aaw4523.
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Robots working in human environments often encounter a wide range of articulated objects, such as tools, cabinets, and other jointed objects. Such articulated objects can take an infinite number of possible poses, as a point in a potentially high-dimensional continuous space. A robot must perceive this continuous pose to manipulate the object to a desired pose. This problem of perception and manipulation of articulated objects remains a challenge due to its high dimensionality and multimodal uncertainty. Here, we describe a factored approach to estimate the poses of articulated objects using an efficient approach to nonparametric belief propagation. We consider inputs as geometrical models with articulation constraints and observed RGBD (red, green, blue, and depth) sensor data. The described framework produces object-part pose beliefs iteratively. The problem is formulated as a pairwise Markov random field (MRF), where each hidden node (continuous pose variable) is an observed object-part’s pose and the edges denote the articulation constraints between the parts. We describe articulated pose estimation by a “pull” message passing algorithm for nonparametric belief propagation (PMPNBP) and evaluate its convergence properties over scenes with articulated objects. Robot experiments are provided to demonstrate the necessity of maintaining beliefs to perform goal-driven manipulation tasks.
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Janáček, Jiří, and Lucie Kubínová. "VARIANCES OF LENGTH AND SURFACE AREA ESTIMATES BY SPATIAL GRIDS: PRELIMINARY STUDY." Image Analysis & Stereology 29, no. 1 (May 3, 2011): 45. http://dx.doi.org/10.5566/ias.v29.p45-52.
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Periodic spatial grids can be used for unbiased estimation of length and surface area of objects by counting or measuring intersections of the objects with the grids. The estimators are theoretically based on discrete approximation of well established integral geometric formulas. The variance of the estimates depends on properties of both the grid and the measured objects. Main results of the theory of variance of the isotropic uniform random (IUR) volume estimation by spatial grids, especially a formula relating the variance of the volume estimator with the object surface area and the grid constant, are recapitulated. To identify main features of length and surface area IUR estimates the variance due to rotation and simple asymptotic formulas for the residual variance of estimates of selected model objects is calculated. Surface area estimates by multiple grids of parallel lines in 3D and of the variance of length estimates by periodic grids of planes or spheres in ddimensional space are studied.
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Jiang, H., J. Liu, and H. W. Cheng. "ORBITAL UNCERTAINTY ESTIMATION SUPPORT FOR AUTONOMOUS SPACE DEBRIS OBSERVATION." Revista Mexicana de Astronomía y Astrofísica Serie de Conferencias 53 (September 1, 2021): 158–60. http://dx.doi.org/10.22201/ia.14052059p.2021.53.32.
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The continually increased space debris have posed great impact risks to existing space systems and human space flight. Accurate knowledge of propagation errors of space debris orbit is essential for many types of uses, such as space surveillance network tasking, conjunction analysis etc. Unfortunately, propagation error is not available for a two-line element (TLE). In this paper, a new TLE uncertainty estimation method based on neural network model is proposed. Object properties, space environment and predicted time-span are considered as the input of the network, the propagation errors in the direction of downrange, normal and conormal are as the output of the network. In order to assure the chosen orbit for training is not stable, only debris and rocket bodies are used. The network's effciency is demonstrated with some objects with continuous TLE data. Overall, the method proves accurate, computationally fast, and robust, and is applicable to any object in the satellite catalogue, especially for those newly launched objects.
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Kuznetsova, Ya V. "Object methods of geostatistical analysis for facies modeling." Oil and Gas Studies, no. 1 (March 19, 2021): 20–29. http://dx.doi.org/10.31660/0445-0108-2021-1-20-29.
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Facies cube is a required part of a static model, especially concerning fields characterized by complicated geological structure. The important quantitative limitations for modeling are facies proportions in the formation volume. Nowadays these proportions are calculated using standard geostatistical methods without considering particular properties of facies data. These properties are specific geometrical characteristics of sedimentological units. The consequences are significant differences between calculated and actual data and unreliable hydrocarbon reserves estimation.In order to enhance reliability of reserves estimation on the basis of 3D static models, this article is devoted to special methods of geostatistical analysis for facies data: object geometrization and object clustering. These methods allow taking into account specific geometrical parameters of formations deposited in different environments, therefore, allow reducing differences between calculated and actual facies data and enhancing reliability of reserves estimation.
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Chen, Yuhan, Xiao Luo, Baoling Han, Jianfeng Jiang, and Yang Liu. "Closed-form camera pose and plane parameters estimation for moments-based visual servoing of planar objects." International Journal of Advanced Robotic Systems 19, no. 3 (May 1, 2022): 172988062210997. http://dx.doi.org/10.1177/17298806221099701.
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Image moments are global descriptors of an image and can be used to achieve control-decoupling properties in visual servoing. However, only a few methods completely decouple the control. This study introduces a novel camera pose estimation method, which is a closed-form solution, based on the image moments of planar objects. Traditional position-based visual servoing estimates the pose of a camera relative to an object, but the pose estimation method directly estimates the pose of an initial camera relative to a desired camera. Because the estimation method is based on plane parameters, a plane parameters estimation method based on the 2D rotation, 2D translation, and scale invariant moments is also proposed. A completely decoupled position-based visual servoing control scheme from the two estimation methods above was adopted. The new scheme exhibited asymptotic stability when the object plane was in the camera field of view. Simulation results demonstrated the effectiveness of the two estimation methods and the advantages of the visual servo control scheme compared with the classical method.
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Xia, Fan, Umme Zakia, Carlo Menon, and Behraad Bahreyni. "Improved Capacitive Proximity Detection for Conductive Objects through Target Profile Estimation." Journal of Sensors 2019 (September 8, 2019): 1–11. http://dx.doi.org/10.1155/2019/3891350.
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The accuracy of a capacitive proximity sensor is affected by various factors, including the geometry and composition of the nearby object. The quantitative regression models that are used to seek out the relationship between the measured capacitances and distances to objects are highly dependent on the geometrical properties of the objects. Consequently, the application of capacitive proximity sensors has been mainly limited to detection of objects rather than estimation of distances to them. This paper presents a capacitive proximity sensing system for the detection of metallic objects with improved accuracy based on target profile estimation. The presented approach alleviates large errors in distance estimation by implementing a classifier to recognize the surface profiles before using a suitable regression model to estimate the distance. The sensing system features an electrode matrix that is configured to sweep a series of inner-connection patterns and produce features for profile classification. The performance of the sensing modalities is experimentally assessed with an industrial robot. Two-term exponential regression models provide a high degree of fittings for an object whose shape is known. Recognizing the shape of the object improved the regression models and reduced the close-distance measurement error by a factor of five compared to methods that did not take the geometry into account. The breakthroughs made through this work will make capacitive sensing a viable low-cost alternative to existing technologies for proximity detection in robotics and other fields.
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LEE, MAL-REY, and TAE-EUN KIM. "ESTIMATION OF HYBRID REFLECTANCE PROPERTIES AND SHAPE RECONSTRUCTION USING THE LMS METHOD." International Journal on Artificial Intelligence Tools 08, no. 01 (March 1999): 1–17. http://dx.doi.org/10.1142/s0218213099000026.
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This paper presents a new method to estimate reflectance properties of non–Lambertian surface by the least-mean-square (LMS) algorithm. In this paper, hybrid reflectance of an object is represented by the Torrance–Sparrow model. We determine reflectance parameters which minimize the sum squared difference of the intensity distribution between the image of a sample sphere and the calculated image. The estimated reflectance parameters provide the range data with intensity distributions. Therefore, we generate three reference images of a range sphere, which has the same diameter as that of the sample, from the same viewpoint with different light directions. Direct matching of the object images to the references can precisely reconstruct the shape of the object. This paper uses a plate diffuse illumination to alleviate the effects of specular spike and highlights. The simulation results show that the proposed method can estimate reflectance properties of the hybrid surface, and also recover the object shape.
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Gigilashvili, Davit, Philipp Urban, Jean-Baptiste Thomas, Jon Yngve Hardeberg, and Marius Pedersen. "Impact of Shape on Apparent Translucency Differences." Color and Imaging Conference 2019, no. 1 (October 21, 2019): 132–37. http://dx.doi.org/10.2352/issn.2169-2629.2019.27.25.
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Translucency is one of the major appearance attributes. Apparent translucency is impacted by various factors including object shape and geometry. Despite general proposals that object shape and geometry have a significant effect on apparent translucency, no quantification has been made so far. Quantifying and modeling the impact of geometry, as well as comprehensive understanding of the translucency perception process, are a point of not only academic, but also industrial interest with 3D printing as an example among many. We hypothesize that a presence of thin areas in the object facilitates material translucency estimation and changes in material properties have larger impact on apparent translucency of the objects with thin areas. Computergenerated images of objects with various geometry and thickness have been used for a psychophysical experiment in order to quantify apparent translucency difference between objects while varying material absorption and scattering properties. Finally, absorption and scattering difference thresholds where the human visual system starts perceiving translucency difference need to be identified and its consistency needs to be analyzed across different shapes and geometries.
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Kuang, Zhengfei, Kyle Olszewski, Menglei Chai, Zeng Huang, Panos Achlioptas, and Sergey Tulyakov. "NeROIC." ACM Transactions on Graphics 41, no. 4 (July 2022): 1–12. http://dx.doi.org/10.1145/3528223.3530177.
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We present a novel method to acquire object representations from online image collections, capturing high-quality geometry and material properties of arbitrary objects from photographs with varying cameras, illumination, and backgrounds. This enables various object-centric rendering applications such as novel-view synthesis, relighting, and harmonized background composition from challenging in-the-wild input. Using a multi-stage approach extending neural radiance fields, we first infer the surface geometry and refine the coarsely estimated initial camera parameters, while leveraging coarse foreground object masks to improve the training efficiency and geometry quality. We also introduce a robust normal estimation technique which eliminates the effect of geometric noise while retaining crucial details. Lastly, we extract surface material properties and ambient illumination, represented in spherical harmonics with extensions that handle transient elements, e.g. sharp shadows. The union of these components results in a highly modular and efficient object acquisition framework. Extensive evaluations and comparisons demonstrate the advantages of our approach in capturing high-quality geometry and appearance properties useful for rendering applications.
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SPINA, THIAGO V., PAULO A. V. DE MIRANDA, and ALEXANDRE X. FALCÃO. "INTELLIGENT UNDERSTANDING OF USER INTERACTION IN IMAGE SEGMENTATION." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 02 (March 2012): 1265001. http://dx.doi.org/10.1142/s0218001412650016.
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We have developed interactive tools for graph-based segmentation of natural images, in which the user guides object delineation by drawing strokes (markers) inside and outside the object. A suitable arc-weight estimation is paramount to minimize user time and maximize segmentation accuracy in these tools. However, it depends on discriminative image properties for object and background. These properties can be obtained from some marker pixels, but their identification is a hard problem during delineation. Careless arc-weight re-estimation reduces user control and drops performance, while interactive arc-weight estimation in a step before interactive object extraction is the best option so far, albeit it is not intuitive for nonexpert users. We present an effective solution using the unified framework of the image foresting transform (IFT) with three operators: clustering for interpreting user interaction and determining when and where arc weights need to be re-estimated; fuzzy classification for arc-weight estimation; and marker competition based on optimum connectivity for object extraction. For validation, we compared the proposed approach with another interactive IFT-based method, which computes arc weights before extraction. Evaluation involved multiple users (experts and nonexperts), a dataset with several natural images, and measurements to quantify accuracy, precision, efficiency (user time and computation time), and user control, being some of them novel measurements, proposed in this work.
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Setterfield, Timothy P., David W. Miller, Alvar Saenz-Otero, Emilio Frazzoli, and John J. Leonard. "Inertial Properties Estimation of a Passive On-orbit Object Using Polhode Analysis." Journal of Guidance, Control, and Dynamics 41, no. 10 (October 2018): 2214–31. http://dx.doi.org/10.2514/1.g003394.
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Gianelli, Claudia, Riccardo Dalla Volta, Filippo Barbieri, and Maurizio Gentilucci. "Automatic grasp imitation following action observation affects estimation of intrinsic object properties." Brain Research 1218 (July 2008): 166–80. http://dx.doi.org/10.1016/j.brainres.2008.04.046.
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Rudenko, O. G., О. О. Bessonov, N. М. Serdyuk, К. О. Olijnik, and О. S. Romanyuk. "Robust object identification in the presence of non-Gaussian interference." Bionics of Intelligence 2, no. 93 (December 2, 2019): 7–12. http://dx.doi.org/10.30837/bi.2019.2(93).02.
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The problem of identifying the parameters of a linear object in the presence of non-Gaussian interference is considered based on minimizing a combined functional that combines the properties of OLS and IIS. The conditions for the convergence of the gradient identification algorithm in mean and mean square are determined. Analytical estimates are obtained for non-asymptotic and asymptotic values of the parameter estimation error and the identification accuracy. It is shown that these values of the estimation error and identification accuracy depend on the choice of the mixing parameter.
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Krushel, Elena Georgievna, Ekaterina Sergeevna Potafeeva, Tatyana Petrovna Ogar, Ilya Viktorovich Stepanchenko, and Ivan Mikhailovich Kharitonov. "POSSIBILITIES OF CYBER-PHYSICAL APPROACH TO STUDYING FREQUENCY PROPERTIES OF CLOSED SYSTEM WITH INCOMPLETE INFORMATION ABOUT CONTROL OBJECT MODEL." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2021, no. 4 (October 29, 2021): 21–34. http://dx.doi.org/10.24143/2072-9502-2021-4-21-34.
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The article considers a method of reducing the time spent on the experimental study of the frequency properties of an object with an unknown mathematical model by using the cyber-physical approach to the automation of the experiment. Nonparametric estimates of unknown frequency characteristics of an object are received from experimental data on the reaction of the object's output to the input harmonic signal in the form of a mixture of sinusoidal signals of different frequencies. To divide the output signal into components corresponding to each frequency, a computer technology is used that implements an optimization procedure for finding the values of both real and imaginary frequency characteristics, according to the frequencies represented in the harmonic input signal. The method is also suitable for accelerated evaluation of the frequency characteristics of an object with an unknown delay. There are considered the aspects of frequency properties estimation in the problem of closed system stability analysis, which is supposed to control an object with incomplete information about its model using a series-connected proportional-integral controller. The results of quick estimating the frequency characteristics of the object are used to identify the parameters of its transfer function. To solve the parameterization problem, there are used automation tools for calculating the transfer function according to data on the points of frequency characteristics implemented as part of the open-access computer mathematics system Scilab. There is given an example illustrating the possibilities of developing a control system using
a reduced-order object model, as one of the applications of the results of parametric identification of the transfer function
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Karageorgos, Konstantinos, Anastasios Dimou, Federico Alvarez, and Petros Daras. "Implicit and Explicit Regularization for Optical Flow Estimation." Sensors 20, no. 14 (July 10, 2020): 3855. http://dx.doi.org/10.3390/s20143855.
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In this paper, two novel and practical regularizing methods are proposed to improve existing neural network architectures for monocular optical flow estimation. The proposed methods aim to alleviate deficiencies of current methods, such as flow leakage across objects and motion consistency within rigid objects, by exploiting contextual information. More specifically, the first regularization method utilizes semantic information during the training process to explicitly regularize the produced optical flow field. The novelty of this method lies in the use of semantic segmentation masks to teach the network to implicitly identify the semantic edges of an object and better reason on the local motion flow. A novel loss function is introduced that takes into account the objects’ boundaries as derived from the semantic segmentation mask to selectively penalize motion inconsistency within an object. The method is architecture agnostic and can be integrated into any neural network without modifying or adding complexity at inference. The second regularization method adds spatial awareness to the input data of the network in order to improve training stability and efficiency. The coordinates of each pixel are used as an additional feature, breaking the invariance properties of the neural network architecture. The additional features are shown to implicitly regularize the optical flow estimation enforcing a consistent flow, while improving both the performance and the convergence time. Finally, the combination of both regularization methods further improves the performance of existing cutting edge architectures in a complementary way, both quantitatively and qualitatively, on popular flow estimation benchmark datasets.
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Hu, Keli, Wei He, Jun Ye, Liping Zhao, Hua Peng, and Jiatian Pi. "Online Visual Tracking of Weighted Multiple Instance Learning via Neutrosophic Similarity-Based Objectness Estimation." Symmetry 11, no. 6 (June 25, 2019): 832. http://dx.doi.org/10.3390/sym11060832.
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An online neutrosophic similarity-based objectness tracking with a weighted multiple instance learning algorithm (NeutWMIL) is proposed. Each training sample is extracted surrounding the object location, and the distribution of these samples is symmetric. To provide a more robust weight for each sample in the positive bag, the asymmetry of the importance of the samples is considered. The neutrosophic similarity-based objectness estimation with object properties (super straddling) is applied. The neutrosophic theory is a new branch of philosophy for dealing with incomplete, indeterminate, and inconsistent information. By considering the surrounding information of the object, a single valued neutrosophic set (SVNS)-based segmentation parameter selection method is proposed, to produce a well-built set of superpixels which can better explain the object area at each frame. Then, the intersection and shape-distance criteria are proposed for weighting each superpixel in the SVNS domain, mainly via three membership functions, T (truth), I (indeterminacy), and F (falsity), for each criterion. After filtering out the superpixels with low response, the newly defined neutrosophic weights are utilized for weighting each sample. Furthermore, the objectness estimation information is also applied for estimating and alleviating the problem of tracking drift. Experimental results on challenging benchmark video sequences reveal the superior performance of our algorithm when confronting appearance changes and background clutters.
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HORNEGGER, J., and H. NIEMANN. "A NOVEL PROBABILISTIC MODEL FOR OBJECT RECOGNITION AND POSE ESTIMATION." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 02 (March 2001): 241–53. http://dx.doi.org/10.1142/s0218001401000903.
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In this paper we consider the problem of object recognition and localization in a probabilistic framework. An object is represented by a parametric probability density, and the computation of pose parameters is implemented as a nonlinear parameter estimation problem. The presence of a probabilistic model allows for recognition according to Bayes rule. The introduced probabilistic model requires no prior segmentation but characterizes the statistical properties of observed intensity values in the image plane. A detailed discussion of the applied theoretical framework is followed by a concise experimental evaluation which demonstrates the benefit of the proposed approach.
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Liu, Tao, Anja Klotzsche, Mukund Pondkule, Harry Vereecken, Yi Su, and Jan van der Kruk. "Radius estimation of subsurface cylindrical objects from ground-penetrating-radar data using full-waveform inversion." GEOPHYSICS 83, no. 6 (November 1, 2018): H43—H54. http://dx.doi.org/10.1190/geo2017-0815.1.
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Ray-based radius estimations of subsurface cylindrical objects such as rebars and pipes from ground-penetrating-radar (GPR) measurements are not accurate because of their approximations. We have developed a novel full-waveform inversion (FWI) approach that uses a full-waveform 3D finite-difference time-domain (FDTD) forward-modeling program to estimate the radius including other object parameters. By using the full waveform of the common-offset GPR data, the shuffled complex evolution (SCE) approach is able to reliably extract the radius of the subsurface cylindrical objects. A combined optimization of radius, medium properties, and the effective source wavelet is necessary. Synthetic and experimental data inversion returns an accurate reconstruction of the cylinder properties, medium properties, and the effective source wavelet. Combining FWI of GPR data using SCE and a 3D FDTD forward model makes the approach easily adaptable for a wide range of other GPR FWI approaches.
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Wolfe, Noah E., Salvatore Vitale, and Colm Talbot. "Too small to fail: characterizing sub-solar mass black hole mergers with gravitational waves." Journal of Cosmology and Astroparticle Physics 2023, no. 11 (November 1, 2023): 039. http://dx.doi.org/10.1088/1475-7516/2023/11/039.
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Abstract The detection of a sub-solar mass black hole could yield dramatic new insights into the nature of dark matter and early-Universe physics, as such objects lack a traditional astrophysical formation mechanism. Gravitational waves allow for the direct measurement of compact object masses during binary mergers, and we expect the gravitational-wave signal from a low-mass coalescence to remain within the LIGO frequency band for thousands of seconds. However, it is unclear whether one can confidently measure the properties of a sub-solar mass compact object and distinguish between a sub-solar mass black hole or other exotic objects. To this end, we perform Bayesian parameter estimation on simulated gravitational-wave signals from sub-solar mass black hole mergers to explore the measurability of their source properties. We find that the LIGO/Virgo detectors during the O4 observing run would be able to confidently identify sub-solar component masses at the threshold of detectability; these events would also be well-localized on the sky and may reveal some information on their binary spin geometry. Further, next-generation detectors such as Cosmic Explorer and the Einstein Telescope will allow for precision measurement of the properties of sub-solar mass mergers and tighter constraints on their compact-object nature.
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Sandykbayeva, Danissa, Zhanat Kappassov, and Bakhtiyar Orazbayev. "VibroTouch: Active Tactile Sensor for Contact Detection and Force Sensing via Vibrations." Sensors 22, no. 17 (August 27, 2022): 6456. http://dx.doi.org/10.3390/s22176456.
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Accurate and fast contact detection between a robot manipulator and objects is crucial for safe robot–object and human–robot interactions. Traditional collision detection techniques relied on force–torque sensors and Columb friction cone estimation. However, the strain gauges used in the conventional force sensors require low-noise and high-precision electronics to deliver the signal to the final user. The Signal-to-Noise Ratio (SNR) in these devices is still an issue in light contact detection. On the other hand, the Eccentric Rotating Mass (ERM) motors are very sensitive to subtle touch as their vibrating resonant state loses immediately. The vibration, in this case, plays a core role in triggering the tactile event. This project’s primary goal is to use generated and received vibrations to establish the scope of object properties that can be obtained through low-frequency generation on one end and Fourier analysis of the accelerometer data on the other end. The main idea behind the system is the phenomenon of change in vibration propagation patterns depending on the grip properties. Moreover, the project’s original aim is to gather enough information on vibration feedback on objects of various properties and compare them. These data sets are further analyzed in terms of frequency and applied grip force correlations in order to prepare the ground for pattern extraction and recognition based on the physical properties of an object.
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TANAKA, Norihiro, Jae-Yong WOO, Tomohiro SARASHINA, and Kosuke MOCHIZUKI. "ESTIMATION OF OBJECT SURFACE REFLECTION PROPERTIES BASED ON MEASURING OF SPECTRAL REFLECTION DATA." Transactions of Japan Society of Kansei Engineering 8, no. 3 (2009): 943–50. http://dx.doi.org/10.5057/jjske.8.943.
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Niechwiej-Szwedo, Ewa, Michael Cao, and Michael Barnett-Cowan. "Binocular Viewing Facilitates Size Constancy for Grasping and Manual Estimation." Vision 6, no. 2 (April 20, 2022): 23. http://dx.doi.org/10.3390/vision6020023.
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A prerequisite for efficient prehension is the ability to estimate an object’s distance and size. While most studies demonstrate that binocular viewing is associated with a more efficient grasp programming and execution compared to monocular viewing, the factors contributing to this advantage are not fully understood. Here, we examined how binocular vision facilitates grasp scaling using two tasks: prehension and manual size estimation. Participants (n = 30) were asked to either reach and grasp an object or to provide an estimate of an object’s size using their thumb and index finger. The objects were cylinders with a diameter of 0.5, 1.0, or 1.5 cm placed at three distances along the midline (40, 42, or 44 cm). Results from a linear regression analysis relating grip aperture to object size revealed that grip scaling during monocular viewing was reduced similarly for both grasping and estimation tasks. Additional analysis revealed that participants adopted a larger safety margin for grasping during monocular compared to binocular viewing, suggesting that monocular depth cues do not provide sufficient information about an object’s properties, which consequently leads to a less efficient grasp execution.
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Kozák, Viktor, Roman Sushkov, Miroslav Kulich, and Libor Přeučil. "Data-Driven Object Pose Estimation in a Practical Bin-Picking Application." Sensors 21, no. 18 (September 11, 2021): 6093. http://dx.doi.org/10.3390/s21186093.
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This paper addresses the problem of pose estimation from 2D images for textureless industrial metallic parts for a semistructured bin-picking task. The appearance of metallic reflective parts is highly dependent on the camera viewing direction, as well as the distribution of light on the object, making conventional vision-based methods unsuitable for the task. We propose a solution using direct light at a fixed position to the camera, mounted directly on the robot’s gripper, that allows us to take advantage of the reflective properties of the manipulated object. We propose a data-driven approach based on convolutional neural networks (CNN), without the need for a hard-coded geometry of the manipulated object. The solution was modified for an industrial application and extensively tested in a real factory. Our solution uses a cheap 2D camera and allows for a semi-automatic data-gathering process on-site.
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Yeom, Seokwon. "Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association." Applied Sciences 11, no. 23 (November 26, 2021): 11234. http://dx.doi.org/10.3390/app112311234.
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The multirotor has the capability to capture distant objects. Because the computing resources of the multirotor are limited, efficiency is an important factor to consider. In this paper, multiple target tracking with a multirotor at a long distance (~400 m) is addressed; the interacting multiple model (IMM) estimator combined with the directional track-to-track association (abbreviated as track association) is proposed. The previous work of the Kalman estimator with the track association approach is extended to the IMM estimator with the directional track association. The IMM estimator can handle multiple targets with various maneuvers. The track association scheme is modified in consideration of the direction of the target movement. The overall system is composed of moving object detection for measurement generation and multiple target tracking for state estimation. The moving object detection consists of frame-to-frame subtraction of three-color layers and thresholding, morphological operation, and false alarm removing based on the object size and shape properties. The centroid of the detected object is input into the next tracking stage. The track is initialized using the difference between two nearest points measured in consecutive frames. The measurement nearest to the state prediction is used to update the state of the target for measurement-to-track association. The directional track association tests both the hypothesis and the maximum deviation between the displacement and directions of two tracks followed by track selection, fusion, and termination. In the experiment, a multirotor flying at an altitude of 400 m captured 55 moving vehicles around a highway interchange for about 20 s. The tracking performance is evaluated for the IMMs using constant velocity (CV) and constant acceleration (CA) motion models. The IMM-CA with the directional track association scheme outperforms other methods with an average total track life of 91.7% and an average mean track life of 84.2%.
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Borovskikh, V. E., and A. O. Podvoyskiy. "Method of forecasting of fatigue durability in the conditions of quasimonotonous degradation of physico-mechanical properties of an object." Izvestiya MGTU MAMI 6, no. 2-1 (January 20, 2012): 46–55. http://dx.doi.org/10.17816/2074-0530-68424.
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The authors consider the method of object life estimation in the conditions of quasimonotonous degradation of system quality parameters, invariant relative to a class of generally non-stationary stochastic process of complex structure.
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Jayasudha, Murugan, Muniyandy Elangovan, Miroslav Mahdal, and Jayaraju Priyadarshini. "Accurate Estimation of Tensile Strength of 3D Printed Parts Using Machine Learning Algorithms." Processes 10, no. 6 (June 9, 2022): 1158. http://dx.doi.org/10.3390/pr10061158.
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Manufacturing processes need optimization. Three-dimensional (3D) printing is not an exception. Consequently, 3D printing process parameters must be accurately calibrated to fabricate objects with desired properties irrespective of their field of application. One of the desired properties of a 3D printed object is its tensile strength. Without predictive models, optimizing the 3D printing process for achieving the desired tensile strength can be a tedious and expensive exercise. This study compares the effectiveness of the following five predictive models (i.e., machine learning algorithms) used to estimate the tensile strength of 3D printed objects: (1) linear regression, (2) random forest regression, (3) AdaBoost regression, (4) gradient boosting regression, and (5) XGBoost regression. First, all the machine learning models are tuned for optimal hyperparameters, which control the learning process of the algorithms. Then, the results from each machine learning model are compared using several statistical metrics such as 𝑅2, mean squared error (MSE), mean absolute error (MAE), maximum error, and median error. The XGBoost regression model is the most effective among the tested algorithms. It is observed that the five tested algorithms can be ranked as XG boost > gradient boost > AdaBoost > random forest > linear regression.
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Khlestkova, O. O. "FEATURES OF CRIMINALISTIC ESTIMATION IN FORENSIC SOIL-SCIENCE EXAMINATION." Theory and Practice of Forensic Science and Criminalistics 17 (November 29, 2017): 284–89. http://dx.doi.org/10.32353/khrife.2017.35.
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The paper indicates that a forensic soil-science examination is a multistage comprehensive research, and the evaluation is being carried out after each research phase taking into account the knowledge as in the field ofsoil-science and adjacent sciences (a naturalscience evaluation), and in the field of Criminalistics (criminalistic evaluation). The natural-science evaluation is performedfrom the point of view of classification, taxonomy and other structural units of soil-science, geology, biology and other sciences. The criminalistic evaluation foresees transformation of a natural-science evaluation results in accordance with the special knowledge in the field of the criminalistic identification theory. The features of criminalistic evaluation in the identification researches of forensic soil-science examination are considered, they consist in specific properties of objects of a soil-mineral origination: the absence of data on the structure of a genetic profile of soil in overlayings on object-carrier, multicomponentness of soils, possibility of some indicators of soil to be both as patrimonial and group signs depending on the character of the ground where the crime was committed. The patrimonial belongingness of soil objects corresponds to the broadest plots of terrain and is the initial stage at localization (separation) of accident scene plot under identification. The group belongingness unites objects with certain specific conditions of emergence and existence which, mainly, are conditioned by economic activities of a person in the industry, agriculture, construction. Group signs are namely those allow to separate a local plot on a broad territory with a certain complex of properties and indicators. Each expert research of soils needs the creative approach with taking into account all features of identification objects, including suitability of accident scene plot for localization and identification, the identification importance of signs, the presence of enough quantity of ground overlayings and preservation of initial indicators in them, coincidence ofproperties of all components and impurities in compared soils.
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Sirotskiy, Alexei A. "Application of data analytics methods to assess the prospectivity of planned real estate developments." Stroitel'stvo: nauka i obrazovanie [Construction: Science and Education] 13, no. 2 (June 30, 2023): 144–65. http://dx.doi.org/10.22227/2305-5502.2023.2.10.
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Introduction. In the process of making decisions, about design and construction there are tasks of assessing the prospects for the planned construction of real estate. The subject of the research is the assessment of the attractiveness of real estate objects from the position of expediency of their construction. Materials and methods. Research methods include analysis of scientific papers, application of systems analysis and systems approach, structural and mathematical modelling of phenomena and processes, theory and practice of digitalization of economic systems, theory and methodology of object-oriented big data processing, theory of forecasting and statistical analysis. Results. Four groups of property parameters that may influence their attractiveness have been identified. The information has been formalized into a form suitable for analytics. It has been shown that the properties of objects can be regarded as their attributes and in this regard, a star data model has been proposed for the information-analytical system. The scheme of interconnection of object characteristics and parameters is proposed, as well as the model of data processing system including the collection of big data from multiple sources and integration with the enterprise platforms. The estimation of attractiveness of objects is carried out by calculating the integral index consisting of integral indexes of separate data sets. The method of ranking the formalized indicators of objects as a preliminary stage of expert determination of their weight values is proposed. On the basis of the integral index of object attractiveness a management decision may be made as to the advisability and prospects of construction or performance of correction of design indices of the projected object. The modelling and reporting process can be carried out in software that implements the Business Intelligence concept. Conclusions. The proposed methodology for assessing prospective properties based on big data analysis can be used in decision-making by both construction companies and participants in the secondary real estate market for efficient parametric selection of properties according to customer requests.
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Dymova, Н. "Dynamic Operator Extraction Method." COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, no. 52 (September 24, 2023): 43–47. http://dx.doi.org/10.36910/6775-2524-0560-2023-52-05.
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A "black box" is used to mean an object whose internal structure is unknown and information about its structure and functioning can only be partially obtained by analyzing the input-output connections of this object. Not only those material, energy and/or informational flows that are necessary for its functioning in accordance with the goals set before it - signals, but also those that actually complicate the realization of the set goal by the system - obstacles come to the input of the system from the external environment. An unregulated facility is being explored here. When studying such an object, it is important that the signals always describe the behavior of the object as a whole and reflect the individual movements of a large number of its microparticles of the same type. The analysis of the structure of the object based on its established signal is insufficient, if only the dynamic dependence on time is taken into account, even the most detailed registration of the single solution of the established dynamic equation does not allow revealing the structure of the operator in real situations. The inadequacy of the usual black box scheme for studying an unregulated object based on a settled signal leads to the need to account for internal fluctuations in the equations of the object signal. Therefore, the article considers autonomous objects, in the dynamic equations of which time t is not explicitly included. The work formulates and to some extent substantiates a fairly general and fairly simple principle of signal description. According to this basic premise, the properties of the signal, which are quantitatively significant and regularly manifest under the given conditions of observation, are connected to each other by some dynamic structure of the object. The role of object movements, which are less important under these conditions, as well as the role of the external environment, is reflected in this description by the time-fluctuating force that disturbs the dynamic system. The study of the statistical properties of the response of the dynamic system to the fluctuating disturbance allows, in a fairly wide range of problems, to evaluate the dynamic characteristics of an unregulated object based on the established signal. The behavior of the signal, which is described by a linearized equation, requires the estimation of the coefficient , so the article considers possible schemes for estimating this coefficient
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Wu, Yan Jun, Ren Long Li, and Xiao Wang. "Detection of Chirp Signal with Time-Varying Amplitude Based on FRFT." Advanced Materials Research 989-994 (July 2014): 4046–49. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4046.
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The general method time-varying amplitude linear FM signal parameter estimation, the proposed parameter fractional Fourier transform for time-varying estimates of the magnitude of the chirp signal, and the related issues of a more in-depth research. Study the time-varying amplitude of the initial phase chirp signal, the initial angular frequency, modulation frequency and amplitude information extraction and estimation methods, and the magnitude of the Gaussian function varies with the magnitude of random variation and chirp signal for the object properties (parameters on parameter estimation estimate the mean square error) were simulated.
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Pawlak, Mirosław, Gurmukh Singh Panesar, and Marcin Korytkowski. "A Novel Method for Invariant Image Reconstruction." Journal of Artificial Intelligence and Soft Computing Research 11, no. 1 (January 1, 2021): 69–80. http://dx.doi.org/10.2478/jaiscr-2021-0005.
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AbstractIn this paper we propose a novel method for invariant image reconstruction with the properly selected degree of symmetry. We make use of Zernike radial moments to represent an image due to their invariance properties to isometry transformations and the ability to uniquely represent the salient features of the image. The regularized ridge regression estimation strategy under symmetry constraints for estimating Zernike moments is proposed. This extended regularization problem allows us to enforces the bilateral symmetry in the reconstructed object. This is achieved by the proper choice of two regularization parameters controlling the level of reconstruction accuracy and the acceptable degree of symmetry. As a byproduct of our studies we propose an algorithm for estimating an angle of the symmetry axis which in turn is used to determine the possible asymmetry present in the image. The proposed image recovery under the symmetry constraints model is tested in a number of experiments involving image reconstruction and symmetry estimation.
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Deng, Zhen, Yannick Jonetzko, Liwei Zhang, and Jianwei Zhang. "Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization." Sensors 20, no. 4 (February 14, 2020): 1050. http://dx.doi.org/10.3390/s20041050.
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Grasping force control is important for multi-fingered robotic hands to stabilize the grasped object. Humans are able to adjust their grasping force and react quickly to instabilities through tactile sensing. However, grasping force control through tactile sensing with robotic hands is still relatively unexplored. In this paper, we make use of tactile sensing for multi-fingered robot hands to adjust the grasping force to stabilize unknown objects without prior knowledge of their shape or physical properties. In particular, an online detection module based on Deep Neural Network (DNN) is designed to detect contact events and object material simultaneously from tactile data. In addition, a force estimation method based on Gaussian Mixture Model (GMM) is proposed to compute the contact information (i.e., contact force and contact location) from tactile data. According to the results of tactile sensing, an object stabilization controller is then employed for a robotic hand to adjust the contact configuration for object stabilization. The spatio-temporal property of tactile data is exploited during tactile sensing. Finally, the effectiveness of the proposed framework is evaluated in a real-world experiment with a five-fingered Shadow Dexterous Hand equipped with BioTac sensors.
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Tumurbaatar, Tserennadmid, and Taejung Kim. "Comparative Study of Relative-Pose Estimations from a Monocular Image Sequence in Computer Vision and Photogrammetry." Sensors 19, no. 8 (April 22, 2019): 1905. http://dx.doi.org/10.3390/s19081905.
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Techniques for measuring the position and orientation of an object from corresponding images are based on the principles of epipolar geometry in the computer vision and photogrammetric fields. Contributing to their importance, many different approaches have been developed in computer vision, increasing the automation of the pure photogrammetric processes. The aim of this paper is to evaluate the main differences between photogrammetric and computer vision approaches for the pose estimation of an object from image sequences, and how these have to be considered in the choice of processing technique when using a single camera. The use of a single camera in consumer electronics has enormously increased, even though most 3D user interfaces require additional devices to sense 3D motion for their input. In this regard, using a monocular camera to determine 3D motion is unique. However, we argue that relative pose estimations from monocular image sequences have not been studied thoroughly by comparing both photogrammetry and computer vision methods. To estimate motion parameters characterized by 3D rotation and 3D translations, estimation methods developed in the computer vision and photogrammetric fields are implemented. This paper describes a mathematical motion model for the proposed approaches, by differentiating their geometric properties and estimations of the motion parameters. A precision analysis is conducted to investigate the main characteristics of the methods in both fields. The results of the comparison indicate the differences between the estimations in both fields, in terms of accuracy and the test dataset. We show that homography-based approaches are more accurate than essential-matrix or relative orientation–based approaches under noisy conditions.
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Schmidt, Tobias O. B., Ralph Neuhäuser, and Andreas Seifahrt. "Mass Determination of Sub-stellar Companions Around Young Stars - The Example of HR 7329." Proceedings of the International Astronomical Union 7, S282 (July 2011): 189–92. http://dx.doi.org/10.1017/s174392131102730x.
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AbstractLowrance et al. (2000) found a faint companion candidate about 4 arcsec south of the young A0-type star HR 7329. Its spectral type of M7-8 is consistent with a young brown dwarf companion. Here we report spectroscopic J band observations using the integral field spectrograph SINFONI at VLT, enabling a new estimation of effective temperature, extinction and surface gravity of the object and hence its mass. Although the data were reduced carefully, the presence of a spike within the point spread function of the object in each spectral image hampered the precise estimation of the properties of HR 7329. Nevertheless, we will show with the example of this sub-stellar companion how mass estimates independent of evolutionary models of directly imaged sub-stellar companions can be obtained, after removing most of the strong influence of the spike in the present data, and present a new mass estimation of HR 7329 B/b based on the values gained.