Relevant bibliographies by topics / Robust Object Model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Robust Object Model'

Author: Grafiati
Published: 11 March 2023

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Journal articles on the topic "Robust Object Model"

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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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Dong, Qiujie, Xuedong He, Haiyan Ge, Qin Liu, Aifu Han, and Shengzong Zhou. "Improving model drift for robust object tracking." Multimedia Tools and Applications 79, no. 35-36 (July 7, 2020): 25801–15. http://dx.doi.org/10.1007/s11042-020-09032-z.

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Wang, Yong, Xian Wei, Hao Shen, Xuan Tang, and Hui Yu. "Adaptive model updating for robust object tracking." Signal Processing: Image Communication 80 (February 2020): 115656. http://dx.doi.org/10.1016/j.image.2019.115656.

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Lee, Hyungtak, Seongju Kang, and Kwangsue Chung. "Robust Data Augmentation Generative Adversarial Network for Object Detection." Sensors 23, no. 1 (December 23, 2022): 157. http://dx.doi.org/10.3390/s23010157.

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Generative adversarial network (GAN)-based data augmentation is used to enhance the performance of object detection models. It comprises two stages: training the GAN generator to learn the distribution of a small target dataset, and sampling data from the trained generator to enhance model performance. In this paper, we propose a pipelined model, called robust data augmentation GAN (RDAGAN), that aims to augment small datasets used for object detection. First, clean images and a small datasets containing images from various domains are input into the RDAGAN, which then generates images that are similar to those in the input dataset. Thereafter, it divides the image generation task into two networks: an object generation network and image translation network. The object generation network generates images of the objects located within the bounding boxes of the input dataset and the image translation network merges these images with clean images. A quantitative experiment confirmed that the generated images improve the YOLOv5 model’s fire detection performance. A comparative evaluation showed that RDAGAN can maintain the background information of input images and localize the object generation location. Moreover, ablation studies demonstrated that all components and objects included in the RDAGAN play pivotal roles.
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ABDELLAOUI, Mehrez, and Ali DOUIK. "Robust Object Tracker in Video via Discriminative Model." Studies in Informatics and Control 28, no. 3 (October 9, 2019): 337–46. http://dx.doi.org/10.24846/v28i3y201910.

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Medley, Daniela O., Carlos Santiago, and Jacinto C. Nascimento. "Deep Active Shape Model for Robust Object Fitting." IEEE Transactions on Image Processing 29 (2020): 2380–94. http://dx.doi.org/10.1109/tip.2019.2948728.

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Wei Zhong, Huchuan Lu, and Ming-Hsuan Yang. "Robust Object Tracking via Sparse Collaborative Appearance Model." IEEE Transactions on Image Processing 23, no. 5 (May 2014): 2356–68. http://dx.doi.org/10.1109/tip.2014.2313227.

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Nai, Ke, Zhiyong Li, Guiji Li, and Shanquan Wang. "Robust Object Tracking via Local Sparse Appearance Model." IEEE Transactions on Image Processing 27, no. 10 (October 2018): 4958–70. http://dx.doi.org/10.1109/tip.2018.2848465.

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Wang, Chong, and Kai-Qi Huang. "VFM: Visual Feedback Model for Robust Object Recognition." Journal of Computer Science and Technology 30, no. 2 (March 2015): 325–39. http://dx.doi.org/10.1007/s11390-015-1526-1.

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Vajda, Peter, Ivan Ivanov, Lutz Goldmann, Jong-Seok Lee, and Touradj Ebrahimi. "Robust Duplicate Detection of 2D and 3D Objects." International Journal of Multimedia Data Engineering and Management 1, no. 3 (July 2010): 19–40. http://dx.doi.org/10.4018/jmdem.2010070102.

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In this paper, the authors analyze their graph-based approach for 2D and 3D object duplicate detection in still images. A graph model is used to represent the 3D spatial information of the object based on the features extracted from training images to avoid explicit and complex 3D object modeling. Therefore, improved performance can be achieved in comparison to existing methods in terms of both robustness and computational complexity. Different limitations of this approach are analyzed by evaluating performance with respect to the number of training images and calculation of optimal parameters in a number of applications. Furthermore, effectiveness of object duplicate detection algorithm is measured over different object classes. The authors’ method is shown to be robust in detecting the same objects even when images with objects are taken from different viewpoints or distances.
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Dissertations / Theses on the topic "Robust Object Model"

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Bazzi, Louay Mohamad Jamil 1974. "Robust algorithms for model-based object recognition and localization." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/9440.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
Includes bibliographical references (p. 86-87).
We consider the problem of model-based object recognition and localization in the presence of noise, spurious features, and occlusion. We address the case where the model is allowed to be transformed by elements in a given space of allowable transformations. Known algorithms for the problem either treat noise very accurately in an unacceptable worst case running time, or may have unreliable output when noise is allowed. We introduce the idea of tolerance which measures the robustness of a recognition and localization method when noise is allowed. We present a collection of algorithms for the problem, each achieving a different degree of tolerance. The main result is a localization algorithm that achieves any desired tolerance in a relatively low order worst case asymptotic running time. The time constant of the algorithm depends on the ratio of the noise bound over the given tolerance bound. The solution we provide is general enough to handle different cases of allowable transformations, such as planar affine transformations, and scaled rigid motions in arbitrary dimensions.
by Louay Mohamad Jamil Bazzi.
S.M.
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Bax, Ingo. "Hierarchical feed forward models for robust object recognition." [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=984822666.

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Schaich, Rainer Manuel. "Robust model predictive control." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:94e75a62-a801-47e1-8cb8-668e8309d477.

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This thesis deals with the topic of min-max formulations of robust model predictive control problems. The sets involved in guaranteeing robust feasibility of the min-max program in the presence of state constraints are of particular interest, and expanding the applicability of well understood solvers of linearly constrained quadratic min-max programs is the main focus. To this end, a generalisation for the set of uncertainty is considered: instead of fixed bounds on the uncertainty, state- and input-dependent bounds are used. To deal with state- and input dependent constraint sets a framework for a particular class of set-valued maps is utilised, namely parametrically convex set-valued maps. Relevant properties and operations are developed to accommodate parametrically convex set-valued maps in the context of robust model predictive control. A quintessential part of this work is the study of fundamental properties of piecewise polyhedral set-valued maps which are parametrically convex, we show that one particular property is that their combinatorial structure is constant. The study of polytopic maps with a rigid combinatorial structure allows the use of an optimisation based approach of robustifying constrained control problems with probabilistic constraints. Auxiliary polytopic constraint sets, used to replace probabilistic constraints by deterministic ones, can be optimised to minimise the conservatism introduced while guaranteeing constraint satisfaction of the original probabilistic constraint. We furthermore study the behaviour of the maximal robust positive invariant set for the case of scaled uncertainty and show that this set is continuously polytopic up to a critical scaling factor, which we can approximate a-priori with an arbitrary degree of accuracy. Relevant theoretical statements are developed, discussed and illustrated with examples.
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Cheng, Qifeng. "Robust & stochastic model predictive control." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:89da4934-9de7-4142-958e-513065189518.

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In the thesis, two different model predictive control (MPC) strategies are investigated for linear systems with uncertainty in the presence of constraints: namely robust MPC and stochastic MPC. Firstly, a Youla Parameter is integrated into an efficient robust MPC algorithm. It is demonstrated that even in the constrained cases, the use of the Youla Parameter can desensitize the costs to the effect of uncertainty while not affecting the nominal performance, and hence it strengthens the robustness of the MPC strategy. Since the controller u = K x + c can offer many advantages and is used across the thesis, the work provides two solutions to the problem when the unconstrained nominal LQ-optimal feedback K cannot stabilise the whole class of system models. The work develops two stochastic tube approaches to account for probabilistic constraints. By using a semi closed-loop paradigm, the nominal and the error dynamics are analyzed separately, and this makes it possible to compute the tube scalings offline. First, ellipsoidal tubes are considered. The evolution for the tube scalings is simplified to be affine and using Markov Chain model, the probabilistic tube scalings can be calculated to tighten the constraints on the nominal. The online algorithm can be formulated into a quadratic programming (QP) problem and the MPC strategy is closed-loop stable. Following that, a direct way to compute the tube scalings is studied. It makes use of the information on the distribution of the uncertainty explicitly. The tubes do not take a particular shape but are defined implicitly by tightened constraints. This stochastic MPC strategy leads to a non-conservative performance in the sense that the probability of constraint violation can be as large as is allowed. It also ensures the recursive feasibility and closed-loop stability, and is extended to the output feedback case.
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Gastebois, Jérémy. "Contribution à la commande temps réel des robots marcheurs. Application aux stratégies d'évitement des chutes." Thesis, Poitiers, 2017. http://www.theses.fr/2017POIT2315/document.

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Les grands robots marcheurs sont des systèmes mécatroniques poly-articulés complexes qui cristallisent la volonté des humains de conférer leurs capacités à des artefacts, l’une d’entre elle étant la locomotion bipède, et plus particulièrement la conservation de l’équilibre face à des perturbations extérieures. Cette thèse propose un stabilisateur postural ainsi que sa mise en œuvre sur le système locomoteur BIP 2000.Ce robot anthropomorphique possède quinze degrés de libertés actionnés par moteurs électriques et a reçu un nouvel automate ainsi que des variateurs industriels lors de la mise à jour réalisée dans le cadre de ces travaux. Un contrôleur a été conçu et implémenté en suivant les principes de la programmation orientée objet afin de fournir une modularité qui s’inspire de la symétrie naturelle des humanoïdes. Cet aspect a conduit à l’élaboration d’un ensemble d’outils mathématiques permettant de calculer l’ensemble des modèles d’un robot composé de sous-robots dont on connaîtrait déjà les modèles. Le contrôleur permet notamment à la machine de suivre des trajectoires calculées hors ligne par des algorithmes de génération de marches dynamiques ainsi que de tester le stabilisateur postural.Ce dernier consiste en un contrôle en position du robot physique par la consigne d’un robot virtuel de modèle dégradé, commandé en effort, soumis à des champs électrostatiques contraignant sa configuration articulaire. Les tests effectués ont permis de montrer la faisabilité de la méthode
Big walking robots are complex multi-joints mechanical systems which crystallize the human will to confer their capabilities on artefacts, one of them being the bipedal locomotion and more especially the balance keeping against external disturbances. This thesis proposes a balance stabilizer under operating conditions displayed on the locomotor system BIP 2000.This anthropomorphic robot has got fifteen electrically actuated degree of freedom and an Industrial controller. A new software has been developed with an object-oriented programming approach in order to propose the modularity required by the emulated and natural human symmetry. This consideration leads to the development of a mathematical tool allowing the computation of every modelling of a serial robot which is the sum of multiple sub robots with already known modelling. The implemented software also enables the robot to run offline generated dynamic walking trajectories and to test the balance stabilizer.We explore in this thesis the feasibility of controlling the center of gravity of a multibody robotic system with electrostatic fields acting on its virtual counterpart in order to guarantee its balance. Experimental results confirm the potential of the proposed approach
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Reynaga, Barba Valeria. "Detecting Changes During the Manipulation of an Object Jointly Held by Humans and RobotsDetektera skillnader under manipulationen av ett objekt som gemensamt hålls av människor och robotar." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174027.

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In the last decades research and development in the field of robotics has grown rapidly. This growth has resulted in the emergence of service robots that need to be able to physically interact with humans for different applications. One of these applications involves robots and humans cooperating in handling an object together. In such cases, there is usually an initial arrangement of how the robot and the humans hold the object and the arrangement stays the same throughout the manipulation task. Real-world scenarios often require that the initial arrangement changes throughout the task, therefore, it is important that the robot is able to recognize these changes and act accordingly. We consider a setting where a robot holds a large flat object with one or two humans. The aim of this research project is to detect the change in the number of agents grasping the object using only force and torque information measured at the robot's wrist. The proposed solution involves defining a transition sequence of four steps that the humans should perform to go from the initial scenario to the final one. The force and torque information is used to estimate the grasping point of the agents with a Kalman filter. While the humans are going from one scenario to the other, the estimated point changes according to the step of the transition the humans are in. These changes are used to track the steps in the sequence using a hidden Markov model (HMM). Tracking the steps in the sequence means knowing how many agents are grasping the object. To evaluate the method, humans that were not involved in the training of the HMM were asked to perform two tasks: a) perform the previously defined sequence as is, and b) perform a deviation of the sequence. The results of the method show that it is possible to detect the change between one human and two humans holding the object using only force and torque information.
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Munoz, Carpintero Diego Alejandro. "Strategies in robust and stochastic model predictive control." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:2f6bce71-f91f-4d5a-998f-295eff5b089a.

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The presence of uncertainty in model predictive control (MPC) has been accounted for using two types of approaches: robust MPC (RMPC) and stochastic MPC (SMPC). Ideal RMPC and SMPC formulations consider closed-loop optimal control problems whose exact solution, via dynamic programming, is intractable for most systems. Much effort then has been devoted to find good compromises between the degree of optimality and computational tractability. This thesis expands on this effort and presents robust and stochastic MPC strategies with reduced online computational requirements where the conservativeness incurred is made as small as conveniently possible. Two RMPC strategies are proposed for linear systems under additive uncertainty. They are based on a recently proposed approach which uses a triangular prediction structure and a non-linear control policy. One strategy considers a transference of part of the computation of the control policy to an offline stage. The other strategy considers a modification of the prediction structure so that it has a striped structure and the disturbance compensation extends throughout an infinite horizon. An RMPC strategy for linear systems with additive and multiplicative uncertainty is also presented. It considers polytopic dynamics that are designed so as to maximize the volume of an invariant ellipsoid, and are used in a dual-mode prediction scheme where constraint satisfaction is ensured by an approach based on a variation of Farkas' Lemma. Finally, two SMPC strategies for linear systems with additive uncertainty are presented, which use an affine-in-the-disturbances control policy with a striped structure. One strategy considers an offline sequential design of the gains of the control policy, while these are variables in the online optimization in the other. Control theoretic properties, such as recursive feasibility and stability, are studied for all the proposed strategies. Numerical comparisons show that the proposed algorithms can provide a convenient compromise in terms of computational demands and control authority.
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Spoida, Peter. "Robust pricing and hedging beyond one marginal." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:0315824b-52f7-4e44-9ac6-0a688c49762c.

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The robust pricing and hedging approach in Mathematical Finance, pioneered by Hobson (1998), makes statements about non-traded derivative contracts by imposing very little assumptions about the underlying financial model but directly using information contained in traded options, typically call or put option prices. These prices are informative about marginal distributions of the asset. Mathematically, the theory of Skorokhod embeddings provides one possibility to approach robust problems. In this thesis we consider mostly robust pricing and hedging problems of Lookback options (options written on the terminal maximum of an asset) and Convex Vanilla Options (options written on the terminal value of an asset) and extend the analysis which is predominately found in the literature on robust problems by two features: Firstly, options with multiple maturities are available for trading (mathematically this corresponds to multiple marginal constraints) and secondly, restrictions on the total realized variance of asset trajectories are imposed. Probabilistically, in both cases, we develop new optimal solutions to the Skorokhod embedding problem. More precisely, in Part I we start by constructing an iterated Azema-Yor type embedding (a solution to the n-marginal Skorokhod embedding problem, see Chapter 2). Subsequently, its implications are presented in Chapter 3. From a Mathematical Finance perspective we obtain explicitly the optimal superhedging strategy for Barrier/Lookback options. From a probability theory perspective, we find the maximum maximum of a martingale which is constrained by finitely many intermediate marginal laws. Further, as a by-product, we discover a new class of martingale inequalities for the terminal maximum of a cadlag submartingale, see Chapter 4. These inequalities enable us to re-derive the sharp versions of Doob's inequalities. In Chapter 5 a different problem is solved. Motivated by the fact that in some markets both Vanilla and Barrier options with multiple maturities are traded, we characterize the set of market models in this case. In Part II we incorporate the restriction that the total realized variance of every asset trajectory is bounded by a constant. This has been previously suggested by Mykland (2000). We further assume that finitely many put options with one fixed maturity are traded. After introducing the general framework in Chapter 6, we analyse the associated robust pricing and hedging problem for convex Vanilla and Lookback options in Chapters 7 and 8. Robust pricing is achieved through construction of appropriate Root solutions to the Skorokhod embedding problem. Robust hedging and pathwise duality are obtained by a careful development of dynamic pathwise superhedging strategies. Further, we characterize existence of market models with a suitable notion of arbitrage.
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Lee, Sharen Woon Yee. "Bayesian methods for the construction of robust chronologies." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:49c30401-9442-441f-b6b5-1539817e2c95.

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Bayesian modelling is a widely used, powerful approach for reducing absolute dating uncertainties in archaeological research. It is important that the methods used in chronology building are robust and reflect substantial prior knowledge. This thesis focuses on the development and evaluation of two novel, prior models: the trapezoidal phase model; and the Poisson process deposition model. Firstly, the limitations of the trapezoidal phase model were investigated by testing the model assumptions using simulations. It was found that a simple trapezoidal phase model does not reflect substantial prior knowledge and the addition of a non-informative element to the prior was proposed. An alternative parameterisation was also presented, to extend its use to a contiguous phase scenario. This method transforms the commonly-used abrupt transition model to allow for gradual changes. The second phase of this research evaluates the use of Bayesian model averaging in the Poisson process deposition model. The use of model averaging extends the application of the Poisson process model to remove the subjectivity involved in model selection. The last part of this thesis applies these models to different case studies, including attempts at resolving the Iron Age chronological debate in Israel, at determining the age of an important Quaternary tephra, at refining a cave chronology, and at more accurately modelling the mid-Holocene elm decline in the British Isles. The Bayesian methods discussed in this thesis are widely applicable in modelling situations where the associated prior assumptions are appropriate. Therefore, they are not limited to the case studies addressed in this thesis, nor are they limited to analysing radiocarbon chronologies.
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Fleming, James. "Robust and stochastic MPC of uncertain-parameter systems." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:c19ff07c-0756-45f6-977b-9d54a5214310.

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Constraint handling is difficult in model predictive control (MPC) of linear differential inclusions (LDIs) and linear parameter varying (LPV) systems. The designer is faced with a choice of using conservative bounds that may give poor performance, or accurate ones that require heavy online computation. This thesis presents a framework to achieve a more flexible trade-off between these two extremes by using a state tube, a sequence of parametrised polyhedra that is guaranteed to contain the future state. To define controllers using a tube, one must ensure that the polyhedra are a sub-set of the region defined by constraints. Necessary and sufficient conditions for these subset relations follow from duality theory, and it is possible to apply these conditions to constrain predicted system states and inputs with only a little conservatism. This leads to a general method of MPC design for uncertain-parameter systems. The resulting controllers have strong theoretical properties, can be implemented using standard algorithms and outperform existing techniques. Crucially, the online optimisation used in the controller is a convex problem with a number of constraints and variables that increases only linearly with the length of the prediction horizon. This holds true for both LDI and LPV systems. For the latter it is possible to optimise over a class of gain-scheduled control policies to improve performance, with a similar linear increase in problem size. The framework extends to stochastic LDIs with chance constraints, for which there are efficient suboptimal methods using online sampling. Sample approximations of chance constraint-admissible sets are generally not positively invariant, which motivates the novel concept of ‘sample-admissible' sets with this property to ensure recursive feasibility when using sampling methods. The thesis concludes by introducing a simple, convex alternative to chance-constrained MPC that applies a robust bound to the time average of constraint violations in closed-loop.
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Books on the topic "Robust Object Model"

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Odincov, Boris. Models and intelligent systems. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1060845.

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The monograph consists of three chapters, the first of which outlines the theoretical foundations of intelligent information systems. Special attention is paid to the disclosure of the term "model" as the intended meaning depends on the understanding of the material. Introduces and examines the new concepts such as the associative and intuitive knowledge while in the creation of intellectual information systems are not used. The second Chapter contains the analysis of problems of development of artificial intelligence (AI), developed in two directions: classical and statistical. Discusses difficulties in the development of the classical approach, associated with identifying the meaning of words, phrases, text, and formulating thoughts. The analysis of problems arising in the play of imagination and insight, machine understanding of natural language texts, play, verbalization and reflection. The third Chapter contains examples of the development of intelligent information systems and technologies in practice of management of economic objects. Theoretical bases of construction of information robots designed to support the task hierarchy of the knowledge base and generating control regulations. The technology of their creation and application in the management of the business efficiency of enterprise business processes and its investment activities. Focused on researchers and developers, AI and intelligent information systems, as well as graduate students and faculty in related academic disciplines.
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Koslicki, Kathrin. Hylomorphic Relations. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198823803.003.0005.

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This chapter takes up the question of how hylomorphists should conceive of the relations between a matter–form compound, its matter, and its form. It responds to the challenge issued to hylomorphists by the Grounding Problem, which asks what (if anything) explains the apparent differences between an object and its matter. Chapter 4 argues that hylomorphists should opt for a “robust” construal of form according to which forms do not simultaneously bear the same relation to a matter–form compound (essentially) and to the matter composing it (accidentally). Armed with this conception of forms, the differences between numerically distinct spatiotemporally coincident objects can then be explained by appeal to a non-modal conception of essence and a mereological approach to the relation between a matter–form compound, its matter, and its form.
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Farina, Lara. Get a Grip? The Tactile Object of Handlyng Synne. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198802648.003.0007.

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This chapter contributes to a history of the human handling of books by considering the instructive and affective qualities of an early fourteenth-century confessor’s manual, Robert Mannyng’s Handlyng Synne. It discusses both Mannyng’s directions for the user and his use of exempla, comparing the latter to ‘experiment anecdotes’ in present-day writings in affect theory, such as those by Brian Massumi and Erin Manning. It argues that the medieval carole, a dance featured in Mannyng’s most memorable exemplum (known as ‘The Dancers of Colbek’) provides a model of the readerly movements summoned by both Mannyng’s book and current work in affect studies. These movements are circular, but contradictory and in some ways ‘nonsensical’, unlike the progressive mobility imagined in medieval monastic and mystical writings.
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Petersen, Christina. “The Most Assassinated Woman in the World”. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037689.003.0005.

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This chapter examines the evocation of Pearl White's star persona by avant-garde theorists and filmmakers ranging from Sergei Eisenstein to surrealist Robert Desnos. The avant-garde's reactions to White fall largely along movement lines. At once the embodiment of a low-culture narrative mode and a spectacular star object who transcended any particular plot line, White's pejorative status as “the most assassinated woman in the world” may have been more revealing than Jean Epstein originally intended. Whereas Epstein decried White's constant near-death experiences and numerous last-minute escapes as unrealistic pulp fiction, surrealists celebrated her as a “marvelous” apparition. This chapter compares the reactions of adherents of surrealism and impressionism to White and considers how Desnos and the surrealists attempted to transpose the “love and poetry” of her films into their own filmmaking practice. It suggests that White's legacy aside from international stardom exerted an influence upon the avant-garde movement, and that her influence had revolutionary potential for challenging the status quo.
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Klein, Julie Thompson. Beyond Interdisciplinarity. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197571149.001.0001.

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Beyond Interdisciplinarity examines the broadening meaning, heterogeneity, and boundary work of interdisciplinarity. It includes both crossdisciplinary work (encompassing multi-, inter-, and trans-disciplinary forms) as well as cross-sector work (spanning disciplines, fields, professions, government and industry, and communities in the North and South). Part I defines boundary work, discourses of interdisciplinarity, and the nature of interdisciplinary fields and interdisciplines. Part II examines dynamics of working across boundaries, including communicating, collaborating, and learning in research projects and programs, with a closing chapter on failing and succeeding along with gateways to literature and other resources. The conceptual framework is based on an ecology of spatializing practices in transaction spaces, including trading zones and communities of practice. Boundary objects, boundary agents, and boundary organizations play a vital role in brokering differences for platforming change in contexts ranging from small projects to new fields to international initiatives. Translation, interlanguage, and a communication boundary space are vital to achieving intersubjectivity and collective identity, fostering not only pragmatics of negotiation and integration but also reflexivity, transactivity, and co-production of knowledge with stakeholders beyond the academy. Rhetorics of holism and synthesis compete with instrumentalities of problem solving and innovation as well as transgressive critique. Yet typical warrants today include complexity, contextualization, collaboration, and socially robust knowledge. The book also emphasizes the roles of contextualization and historical change while accounting for the shifting relationship of disciplinarity and interdisciplinarity, the ascendancy of transdisciplinarity, and intersections with other constructs, including Mode 2 knowledge production, convergence, team science, and postdisciplinarity.
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Book chapters on the topic "Robust Object Model"

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Wang, Liqun, Xuenan Shi, Sunyi Han, and Jinchi. "Robust Object Tracking via Improved Mean-Shift Model." In Mobile and Wireless Technologies 2017, 86–93. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5281-1_10.

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Wang, Weijun, and Ramakant Nevatia. "Robust Object Tracking Using Constellation Model with Superpixel." In Computer Vision – ACCV 2012, 191–204. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37431-9_15.

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Jurie, Frederic. "Robust hypothesis verification for model based object recognition using Gaussian error model." In Computer Vision — ACCV'98, 440–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63931-4_247.

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Liu, Guoqi, Haifeng Li, and Chenjing Li. "Robust Edge-Based Model with Sparsity Representation for Object Segmentation." In Neural Information Processing, 445–56. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70090-8_46.

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Choi, Seokeon, Junhyun Lee, Yunsung Lee, and Alexander Hauptmann. "Robust Long-Term Object Tracking via Improved Discriminative Model Prediction." In Computer Vision – ECCV 2020 Workshops, 602–17. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-68238-5_40.

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Qiao, Xiaokang, Kaile Su, Zhonglong Zheng, Huawen Liu, and Xiaowei He. "Robust Object Tracking Based on Collaborative Model via L2-Norm Minimization." In Communications in Computer and Information Science, 486–500. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3002-4_41.

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Zografos, Vasileios, and Bernard F. Buxton. "Affine Invariant, Model-Based Object Recognition Using Robust Metrics and Bayesian Statistics." In Lecture Notes in Computer Science, 407–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11559573_51.

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Pareek, Anshul, Vsudha Arora, and Nidhi Arora. "A Robust Surf-Based Online Human Tracking Algorithm Using Adaptive Object Model." In Proceedings of International Conference on Artificial Intelligence and Applications, 543–51. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4992-2_51.

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Seo, Byung-Kuk, and Harald Wuest. "A Direct Method for Robust Model-Based 3D Object Tracking from a Monocular RGB Image." In Lecture Notes in Computer Science, 551–62. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49409-8_48.

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Singer, David, Dorian Rohner, and Dominik Henrich. "Robot-Based Creation of Complete 3D Workpiece Models." In Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021, 289–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-74032-0_24.

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AbstractA complete object database containing a model (representing geometric and texture information) of every possible workpiece is a common necessity e.g. for different object recognition or task planning approaches. The generation of these models is often a tedious process. In this paper we present a fully automated approach to tackle this problem by generating complete workpiece models using a robotic manipulator. A workpiece is recorded by a depth sensor from multiple views for one side, then turned, and captured from the other side. The resulting point clouds are merged into one complete model. Additionally, we represent the information provided by the object’s texture using keypoints. We present a proof of concept and evaluate the precision of the final models. In the end we conclude the usefulness of our approach showing a precision of around 1 mm for the resulting models.
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Conference papers on the topic "Robust Object Model"

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Zhou, Zhi, Yue Wang, and Eam Khwang Teoh. "Robust object tracking using Bi-model." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738639.

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Liu, Qiankun, Qi Chu, Bin Liu, and Nenghai Yu. "GSM: Graph Similarity Model for Multi-Object Tracking." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/74.

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The popular tracking-by-detection paradigm for multi-object tracking (MOT) focuses on solving data association problem, of which a robust similarity model lies in the heart. Most previous works make effort to improve feature representation for individual object while leaving the relations among objects less explored, which may be problematic in some complex scenarios. In this paper, we focus on leveraging the relations among objects to improve robustness of the similarity model. To this end, we propose a novel graph representation that takes both the feature of individual object and the relations among objects into consideration. Besides, a graph matching module is specially designed for the proposed graph representation to alleviate the impact of unreliable relations. With the help of the graph representation and the graph matching module, the proposed graph similarity model, named GSM, is more robust to the occlusion and the targets sharing similar appearance. We conduct extensive experiments on challenging MOT benchmarks and the experimental results demonstrate the effectiveness of the proposed method.
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"ROBUST OBJECT TRACKING BY SIMULTANEOUS GENERATION OF AN OBJECT MODEL." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0001659803920397.

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Huang, Po-Hao, Yi-Lin Chen, Chia-Ming Cheng, Yu-An Lu, and Shang-Hong Lai. "Robust 3D object model reconstruction from video." In Electronic Imaging 2004, edited by Brian D. Corner, Peng Li, and Roy P. Pargas. SPIE, 2004. http://dx.doi.org/10.1117/12.528965.

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Li, Yi, Xiaohuan Lu, Zhenyu He, Hongpeng Wang, and Wen-Sheng Chen. "A Robust Appearance Model for Object Tracking." In 2016 7th International Conference on Cloud Computing and Big Data (CCBD). IEEE, 2016. http://dx.doi.org/10.1109/ccbd.2016.056.

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Wei Zhong, Huchuan Lu, and Ming-Hsuan Yang. "Robust object tracking via sparsity-based collaborative model." In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6247882.

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Chang, Yongxin, Zhiyong Xu, Jing Zhang, Chengyu Fu, and Chunming Gao. "Robust object recognition based on HMAX model architecture." In Photonics Asia, edited by Tsutomu Shimura, Guangyu Xu, Linmi Tao, and Jesse Zheng. SPIE, 2012. http://dx.doi.org/10.1117/12.999350.

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Yao, Zhijun, Bin Feng, Junwei Wang, and Wenyu Liu. "Building a Robust Appearance Model for Object Tracking." In 2009 International Conference on Artificial Intelligence and Computational Intelligence. IEEE, 2009. http://dx.doi.org/10.1109/aici.2009.165.

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Haifeng Chen, I. Shimshoni, and P. Meer. "Model based object recognition by robust information fusion." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1334468.

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Luo, Wenhan, Xiaoqin Zhang, Yang Liu, Xi Li, Weiming Hu, and Wei Li. "Efficient block-division model for robust multiple object tracking." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946626.

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Reports on the topic "Robust Object Model"

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Christie, Benjamin, Osama Ennasr, and Garry Glaspell. ROS integrated object detection for SLAM in unknown, low-visibility environments. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42385.

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Integrating thermal (or infrared) imagery on a robotics platform allows Unmanned Ground Vehicles (UGV) to function in low-visibility environments, such as pure darkness or low-density smoke. To maximize the effectiveness of this approach we discuss the modifications required to integrate our low-visibility object detection model on a Robot Operating System (ROS). Furthermore, we introduce a method for reporting detected objects while performing Simultaneous Localization and Mapping (SLAM) by generating bounding boxes and their respective transforms in visually challenging environments.
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