Relevant bibliographies by topics / A object

Academic literature on the topic 'A object'

Author: Grafiati
Published: 22 June 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "A object":

1

Calogero, Rachel M. "Objects Don’t Object." Psychological Science 24, no. 3 (January 22, 2013): 312–18. http://dx.doi.org/10.1177/0956797612452574.

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Bergin, Joseph, Richard Kick, Judith Hromcik, and Kathleen Larson. "The object is objects." ACM SIGCSE Bulletin 34, no. 1 (March 2002): 251. http://dx.doi.org/10.1145/563517.563438.

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Carey, Susan, and Fei Xu. "Infants' knowledge of objects: beyond object files and object tracking." Cognition 80, no. 1-2 (June 2001): 179–213. http://dx.doi.org/10.1016/s0010-0277(00)00154-2.

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Remhof, Justin. "Object Constructivism and Unconstructed Objects." Southwest Philosophy Review 30, no. 1 (2014): 177–85. http://dx.doi.org/10.5840/swphilreview201430117.

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Neubauer, Peter B. "Preoedipal Objects and Object Primacy." Psychoanalytic Study of the Child 40, no. 1 (January 1985): 163–82. http://dx.doi.org/10.1080/00797308.1985.11823027.

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Vannucci, Manila, Giuliana Mazzoni, Carlo Chiorri, and Lavinia Cioli. "Object imagery and object identification: object imagers are better at identifying spatially-filtered visual objects." Cognitive Processing 9, no. 2 (January 24, 2008): 137–43. http://dx.doi.org/10.1007/s10339-008-0203-5.

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Kingo, Osman S., and Peter Krøjgaard. "Object manipulation facilitates kind-based object individuation of shape-similar objects." Cognitive Development 26, no. 2 (April 2011): 87–103. http://dx.doi.org/10.1016/j.cogdev.2010.08.009.

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Newell, F. N. "Searching for Objects in the Visual Periphery: Effects of Orientation." Perception 25, no. 1_suppl (August 1996): 110. http://dx.doi.org/10.1068/v96l1111.

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Previous studies have found that the recognition of familiar objects is dependent on the orientation of the object in the picture plane. Here the time taken to locate rotated objects in the periphery was examined. Eye movements were also recorded. In all experiments, familiar objects were arranged in a clock face display. In experiment 1, subjects were instructed to locate a match to a central, upright object from amongst a set of randomly rotated objects. The target object was rotated in the frontoparallel plane. Search performance was dependent on rotation, yielding the classic ‘M’ function found in recognition tasks. When matching a single object in periphery, match times were dependent on the angular deviations between the central and target objects and showed no advantage for the upright (experiment 2). In experiment 3 the central object was shown in either the upright rotation or rotated by 120° from the upright. The target object was similarly rotated given four different match conditions. Distractor objects were aligned with the target object. Search times were faster when the centre and target object were aligned and also when the centre object was rotated and the target was upright. Search times were slower when matching a central upright object to a rotated target object. These results suggest that in simple tasks matching is based on image characteristics. However, in complex search tasks a contribution from the object's representation is made which gives an advantage to the canonical, upright view in peripheral vision.
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Shioiri, Satoshi, Kotaro Hashimoto, Kazumichi Matsumiya, Ichiro Kuriki, and Sheng He. "Extracting the orientation of rotating objects without object identification: Object orientation induction." Journal of Vision 18, no. 9 (September 17, 2018): 17. http://dx.doi.org/10.1167/18.9.17.

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Ciribuco, Andrea, and Anne O’Connor. "Translating the object, objects in translation." Translation and Interpreting Studies 17, no. 1 (July 5, 2022): 1–13. http://dx.doi.org/10.1075/tis.00052.int.

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Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "A object":

1

Platt, Kevin Ronald. "Incomplete Objects and Object Sketches." Thesis, The University of Sydney, 2011. http://hdl.handle.net/2123/9496.

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The research and the work that motivates it, herein discussed in this thesis, hinge upon the need to communicate with the viewer, a sense of desire that remains suspended. Throughout both thesis and practice a defining concept is incompleteness. This suggestion is made manifest in the practical element of my work, through the use of the iconography of the frame and construction images, that suggest what could yet be built upon, or added to the work, that is present in the gallery. The implications of this iconography are diverse and accordingly are explored through a series of art-historical discussions. The physical appearance, or the signature aesthetic of my practice, is explored through the pressing immediacy that ruled and defined the sketch aesthetic of Impressionism. The concept of a desire continually entertained is given exemplary treatment in the seminal Large Glass of Marcel Duchamp. As such this is used as a major study with which to compare the motivating factors of my practice. Engaging with the text Kant After Duchamp, by Thierry de Duve facilitates an exploration of two belief systems that arose and defined and impacted art discourse through the middle and latter half of the last century. This study does not seek to align my work either but rather finds the conviction to follow neither and instead identifies my practice with work that does not take a heavy authorial hand looking for a determined outcome. As such, what is made apparent through this investigation is an enduring interest in that which cannot be attained.
2

Swan, J. Edward. "Object-order rendering of discrete objects /." The Ohio State University, 1997. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487945320760218.

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Bumbaugh, Vottero Lisa Faye. "Object to object." The Ohio State University, 1990. http://rave.ohiolink.edu/etdc/view?acc_num=osu1303929624.

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Aboutalib, Sarah. "Multiple-Cue Object Recognition for Interactionable Objects." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/19.

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Category-level object recognition is a fundamental capability for the potential use of robots in the assistance of humans in useful tasks. There have been numerous vision-based object recognition systems yielding fast and accurate results in constrained environments. However, by depending on visual cues, these techniques are susceptible to object variations in size, lighting, rotation, and pose, all of which cannot be avoided in real video data. Thus, the task of object recognition still remains very challenging. My thesis work builds upon the fact that robots can observe humans interacting with the objects in their environment. We refer to the set of objects, which can be involved in the interaction as `interactionable' objects. The interaction of humans with the `interactionable' objects provides numerous nonvisual cues to the identity of objects. In this thesis, I will introduce a flexible object recognition approach called Multiple-Cue Object Recognition (MCOR) that can use multiple cues of any predefined type, whether they are cues intrinsic to the object or provided by observation of a human. In pursuit of this goal, the thesis will provide several contributions: A representation for the multiple cues including an object definition that allows for the flexible addition of these cues; Weights that reflect the various strength of association between a particular cue and a particular object using a probabilistic relational model, as well as object displacement values for localizing the information in an image; Tools for defining visual features, segmentation, tracking, and the values for the non-visual cues; Lastly, an object recognition algorithm for the incremental discrimination of potential object categories. We evaluate these contributions through a number of methods including simulation to demonstrate the learning of weights and recognition based on an analytical model, an analytical model that demonstrates the robustness of the MCOR framework, and recognition results on real video data using a number of datasets including video taken from a humanoid robot (Sony QRIO), video captured from a meeting setting, scripted scenarios from outside universities, and unscripted TV cooking data. Using the datasets, we demonstrate the basic features of the MCOR algorithm including its ability to use multiple cues of different types. We demonstrate the applicability of MCOR to an outside dataset. We show that MCOR has better recognition results over vision-only recognition systems, and show that performance only improves with the addition of more cue types.
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Sa, Ting. "Object Similarity through Correlated Third-Party Objects." Wright State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=wright1219284798.

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Clark, Donald M. S. "Self-explanatory objects : investigation of object-based help." Thesis, Open University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386745.

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Mello, Catherine. "The business end of objects monitoring object orientation /." Oxford, Ohio : Miami University, 2009. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=miami1247589191.

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Ibrahim, Enas. "L'évolution de la notion d'objet dans les romans d'Alain Robbe-Grillet : Les Gommes, le Voyeur, La Jalousie, Dans le Labyrinthe, La Maison de Rendez-vous et Djinn." Thesis, Tours, 2011. http://www.theses.fr/2011TOUR2020.

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Cette thèse a pour objectif d’étudier la présence et l'évolution des objets dans plusieurs romans d’Alain Robbe-Grillet : Les Gommes, Le Voyeur, La Jalousie, Dans Le Labyrinthe, La Maison de Rendez-vous et Djinn. Bien qu’ils soient partout dans l’œuvre, les objets de Robbe-Grillet n’ont encore jamais été étudiés de manière spécifique, détaillée, et à partir d’un large corpus. L’approche de notre travail est littéraire mais aussi sociologique. Il convient en effet de redonner aux objets la place qu’ils occupent dans les relations humaines. C'est à la lumière des recherches d’Abraham A. Moles, Jean Baudrillard, Bernard Blandin, entre autres, que nous avons choisi de nous pencher sur les rapports qui lient l'homme aux objets. Au terme de cette exploration, nous arriverons à la conclusion suivante : les objets robbe-grillétiens sont tantôt muets et opaques, tantôt parlants. Cet effet est pleinement manifesté dans les techniques descriptives qu'adopte le romancier
This thesis aims at investigating the presence and the evolution of objects in several novels by Alain Robbe-Grillet: Les Gommes, Le Voyeur, La Jalousie, Dans le Labyrinthe, La Maison de Rendez-vous and Djinn. Although that objects have an evident existence in Robbe-Grillet’s work, they have never been studied specifically, in details, and from a large corpus. The approach of our work is literary but it is also sociological. It is suitable to give objects the statute they occupy in human relations. In the light of the research of Abraham A. Moles, Jean Baudrillard, Bernard Blandin, among others, we have chosen to dwell on the relations that link humans to objects. After this “exploration”, we reach the following conclusion: the objects are sometimes mute and opaque, sometimes talking. These characteristics are obvious in the description techniques which the novelist uses
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Fisher, Robert B. "From surfaces to objects : recognizing objects using surface information and object models." Thesis, University of Edinburgh, 1986. http://hdl.handle.net/1842/4898.

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This thesis describes research on recognizing partially obscured objects using surface information like Marr's 2D sketch ([MAR82]) and surface-based geometrical object models. The goal of the recognition process is to produce a fully instantiated object hypotheses, with either image evidence for each feature or explanations for their absence, in terms of self or external occlusion. The central point of the thesis is that using surface information should be an important part of the image understanding process. This is because surfaces are the features that directly link perception to the objects perceived (for normal "camera-like" sensing) and because surfaces make explicit information needed to understand and cope with some visual problems (e.g. obscured features). Further, because surfaces are both the data and model primitive, detailed recognition can be made both simpler and more complete. Recognition input is a surface image, which represents surface orientation and absolute depth. Segmentation criteria are proposed for forming surface patches with constant curvature character, based on surface shape discontinuities which become labeled segmentation- boundaries. Partially obscured object surfaces are reconstructed using stronger surface based constraints. Surfaces are grouped to form surface clusters, which are 3D identity-independent solids that often correspond to model primitives. These are used here as a context within which to select models and find all object features. True three-dimensional properties of image boundaries, surfaces and surface clusters are directly estimated using the surface data. Models are invoked using a network formulation, where individual nodes represent potential identities for image structures. The links between nodes are defined by generic and structural relationships. They define indirect evidence relationships for an identity. Direct evidence for the identities comes from the data properties. A plausibility computation is defined according to the constraints inherent in the evidence types. When a node acquires sufficient plausibility, the model is invoked for the corresponding image structure.Objects are primarily represented using a surface-based geometrical model. Assemblies are formed from subassemblies and surface primitives, which are defined using surface shape and boundaries. Variable affixments between assemblies allow flexibly connected objects. The initial object reference frame is estimated from model-data surface relationships, using correspondences suggested by invocation. With the reference frame, back-facing, tangential, partially self-obscured, totally self-obscured and fully visible image features are deduced. From these, the oriented model is used for finding evidence for missing visible model features. IT no evidence is found, the program attempts to find evidence to justify the features obscured by an unrelated object. Structured objects are constructed using a hierarchical synthesis process. Fully completed hypotheses are verified using both existence and identity constraints based on surface evidence. Each of these processes is defined by its computational constraints and are demonstrated on two test images. These test scenes are interesting because they contain partially and fully obscured object features, a variety of surface and solid types and flexibly connected objects. All modeled objects were fully identified and analyzed to the level represented in their models and were also acceptably spatially located. Portions of this work have been reported elsewhere ([FIS83], [FIS85a], [FIS85b], [FIS86]) by the author.
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Lakher, Alexander. "Object comprehension translation for object oriented databases." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0021/MQ47733.pdf.

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Books on the topic "A object":

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Doherty, M. Stephen, and John W. Streetman. Object project: Five objects, fifteen artists. Evansville, Ind: Evansville Museum of Arts, History & Science, 2007.

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Wood, James. Object-oriented programming with ABAP Objects. 2nd ed. Bonn: Rheinwerk Publishing, 2015.

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C, Tsichritzis Dionysios, ed. Object oriented development =: Developpement orienté objet. Geneva: Université de Genève, Centre universitaire d'informatique, 1989.

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Sadr, Babak. Unified objects: Object-oriented programming using C++. Los Alamitos, Calif: IEEE Computer Society, 1998.

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Dietrich, Suzanne Wagner. Fundamentals of object databases: Object-oriented and object-relational design. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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Sessions, Roger. Object persistence: Beyond object-oriented databases. Upper Saddle River, N.J: Prentice Hall PTR, 1996.

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Gossain, Sanjiv. Object strategies: Tips and techniques for successful objects. New York: SIGS Books, 1997.

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Booch, Grady. Object solutions: Managing the object-oriented project. Menlo Park, Ca: Addison-Wesley Pub. Co., 1996.

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Quindlen, Anna. Object lessons. New York: Ivy Books, 1992.

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Norrie, Moira C., and Michael Grossniklaus, eds. Object Databases. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14681-7.

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Book chapters on the topic "A object":

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Ehrich, H. D., A. Sernadas, and C. Sernadas. "Objects, object types, and object identification." In Categorical Methods in Computer Science With Aspects from Topology, 142–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/3-540-51722-7_9.

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Schlie, Heike. "Object Links – Objects Link." In Object Links, 9–16. Wien: Böhlau Verlag, 2019. http://dx.doi.org/10.7767/9783205209591.9.

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Sharan, Kishori. "Object and Objects Classes." In Beginning Java 9 Fundamentals, 395–437. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2902-6_11.

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Pilgrim, Mark. "Objects and Object-Orientation." In Dive Into Python, 71–95. Berkeley, CA: Apress, 2004. http://dx.doi.org/10.1007/978-1-4302-0700-9_5.

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Sharan, Kishori, and Adam L. Davis. "Object and Objects Classes." In Beginning Java 17 Fundamentals, 395–434. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7307-4_11.

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Sharan, Kishori. "The Object and Objects Classes." In Beginning Java 8 Fundamentals, 281–316. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6653-2_7.

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Pardoe, John, and Melv King. "Object Lifetime and Dynamic Objects." In Object Oriented Programming Using C++, 143–60. London: Macmillan Education UK, 1997. http://dx.doi.org/10.1007/978-1-349-14449-5_14.

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Alban, Hedy. "Distributed Objects and Object Wrapping." In Data Management, 535–49. 3rd ed. Boca Raton: Auerbach Publications, 2021. http://dx.doi.org/10.1201/9780429114878-49.

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Longhofer, Jeffrey. "Object." In A-Z of Psychodynamic Practice, 125–27. London: Macmillan Education UK, 2015. http://dx.doi.org/10.1007/978-1-137-03387-1_47.

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Blockeel, Hendrik, Geoffrey I. Webb, Peter Auer, and Geoffrey I. Webb. "Object." In Encyclopedia of Machine Learning, 733. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_605.

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Conference papers on the topic "A object":

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Ayman, Shehab Eldeen, Walid Hussein, and Omar H. Karam. "Depth-Based Region Proposal: Multi-Stage Real-Time Object Detection." In 12th International Conference on Digital Image Processing and Vision. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131305.

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Many real-time object recognition systems operate on two-dimensional images, degrading the influence of the involved objects' third-dimensional (i.e., depth) information. The depth information of a captured scene provides a thorough understanding of an object in fulldimensional space. During the last decade, several region proposal techniques have been integrated into object detection. scenes’ objects are then localized and classified but only in a two-dimensional space. Such techniques exist under the umbrella of two-dimensional object detection models such as YOLO and SSD. However, these techniques have the issue of being uncertain that an object's boundaries are properly specified in the scene. This paper proposes a unique region proposal and object detection strategy based on retrieving depth information for localization and segmentation of the scenes’ objects in a real-time manner. The obtained results on different datasets show superior accuracy in comparison to the commonly implemented techniques with regards to not only detection but also a pixel-by-pixel accurate localization of objects.
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Bergin, Joseph, Richard Kick, Judith Hromcik, and Kathleen Larson. "The object is objects." In the 33rd SIGCSE technical symposium. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/563340.563438.

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Zeng, Zhen, Adrian Röfer, and Odest Chadwicke Jenkins. "Semantic Linking Maps for Active Visual Object Search (Extended Abstract)." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/667.

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We aim for mobile robots to function in a variety of common human environments, which requires them to efficiently search previously unseen target objects. We can exploit background knowledge about common spatial relations between landmark objects and target objects to narrow down search space. In this paper, we propose an active visual object search strategy method through our introduction of the Semantic Linking Maps (SLiM) model. SLiM simultaneously maintains the belief over a target object's location as well as landmark objects' locations, while accounting for probabilistic inter-object spatial relations. Based on SLiM, we describe a hybrid search strategy that selects the next best view pose for searching for the target object based on the maintained belief. We demonstrate the efficiency of our SLiM-based search strategy through comparative experiments in simulated environments. We further demonstrate the real-world applicability of SLiM-based search in scenarios with a Fetch mobile manipulation robot.
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Mohan, Permanand. "Learning Object Repositories." In InSITE 2005: Informing Science + IT Education Conference. Informing Science Institute, 2005. http://dx.doi.org/10.28945/2908.

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In order to reuse learning objects created by others, they must be made available to potential users on the Web, and services must be provided to allow users to discover, obtain rights to, and use these learning objects in their own instructional scenarios. In the learning object economy, these services are typically provided by learning object repositories, which are collections of learning objects that are accessible to users via a network without prior knowledge of the structure of the collections. This chapter discusses the important role played by learning object repositories in the learning object economy. The success of the learning objects' approach depends on users worldwide (such as instructors, learners, and software agents) being able to access and search for learning objects in different repositories in a uniform manner. The first part of the chapter explains how this can be achieved using a standardized approach for accessing and describing learning objects in a repository. Standardized access and retrieval is facilitated by implementing a specification from the IMS known as the Digital Repositories Interoperability (DRI) specification, while standardized search and discovery is facilitated by implementing a metadata standard such as the IEEE Learning Object Metadata (LOM) standard, described earlier in the book. There are different architectural approaches and business models that can be employed when designing a learning object repository and these are discussed next in the chapter. Typical architectural choices include using a centralized repository based on the client/server approach versus using several local repositories connected in a peer-to-peer fashion. Typical choices for business models include using an online broker for advertising and receiving payment for learning objects versus making the learning objects freely available. The advantages and disadvantages of the different approaches and models are carefully examined, and concrete examples of research prototypes and real-world deployments are provided wherever appropriate.
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Anderson, R. J., E. J. Sharp, G. L. Wood, W. W. Clark, G. J. Salamo, and R. R. Neurgaonkar. "Moving object correlator." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.thc2.

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Cross talk is observed in a double-phase conjugator and utilized to construct a moving object correlator. Separate objects are placed in each arm of a double-phase bridge phase conjugator by using a strontium barium niobate cerium doped crystal. During the transient time of grating or bridge formation, cross talk is observed in each arm of the double-phase phase conjugator. After a time corresponding to the characteristic photorefractive response time of the crystal, the cross talk is observed to disappear. If one of the objects in either arms is suddenly moved, however, a signal representing the correlation between the object in each arm can be observed in the correlation plane of the other arm. Consequently, as long as the object in one arm of the double-phase phase conjugator is moving, the signal representing the correlation between the two objects can be observed in the others arm's correlation plane. If the moving object becomes stationary for a time longer than the photorefractive response time, however, the correlation signal disappears. In this way, only the correlation signal between objects moving in one arm and stationary reference objects in the second arm are observed.
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Carvalho, Wilka, Anthony Liang, Kimin Lee, Sungryull Sohn, Honglak Lee, Richard Lewis, and Satinder Singh. "Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/306.

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Learning how to execute complex tasks involving multiple objects in a 3D world is challenging when there is no ground-truth information about the objects or any demonstration to learn from. When an agent only receives a signal from task-completion, this makes it challenging to learn the object-representations which support learning the correct object-interactions needed to complete the task. In this work, we formulate learning an attentive object dynamics model as a classification problem, using random object-images to define incorrect labels for our object-dynamics model. We show empirically that this enables object-representation learning that captures an object's category (is it a toaster?), its properties (is it on?), and object-relations (is something inside of it?). With this, our core learner (a relational RL agent) receives the dense training signal it needs to rapidly learn object-interaction tasks. We demonstrate results in the 3D AI2Thor simulated kitchen environment with a range of challenging food preparation tasks. We compare our method's performance to several related approaches and against the performance of an oracle: an agent that is supplied with ground-truth information about objects in the scene. We find that our agent achieves performance closest to the oracle in terms of both learning speed and maximum success rate.
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Pentland, Alex. "Part Segmentation for Object Recognition." In Image Understanding and Machine Vision. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/iumv.1989.wa3.

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In order to recognize objects one must be able to compute a stable, canonical representation that can be used to index into memory. The most widely accepted theory on how people recognize objects seems to be that they first segment the object into its component parts and then recognition occurs by using this part description to classify the object. The problem of segmenting objects into parts, however, has remained unsolved despite much research.
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Mindrup, Matthew. "La Réaction Poètique of a Prepared Mind." In LC2015 - Le Corbusier, 50 years later. Valencia: Universitat Politècnica València, 2015. http://dx.doi.org/10.4995/lc2015.2015.677.

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Abstract: This paper explores Le Corbusier’s practice of collecting and studying everyday objects as inspiration for new architectural ideas. An avid collector of ‘objets trouves’ that Le Corbusier referred to specifically as ‘objets à réaction poètique,’ he promoted their use claiming they gave direction to an imagination that alone might not be able to detect. Perhaps the most famous object in Le Corbusier’s collection was a crab shell that he used as inspiration for the design of the roof for his Notre-Dame du Haut chapel in Ronchamp, France. Although Le Corbusier’s use of this shell is well documented in studies on his oeuvre, little attention has been given to the role he intended found objects to play in his design process. In themselves these objects, which have their own identities as shells, pinecones or pieces of bone, they do not immediately lend themselves to any architectural solution. Rather, they are evidence of Le Corbusier’s unique approach to design that relies on a what Louis Pasteur referred to as a ‘prepared mind,’ availed of all relevant data and information pertaining to a task, that can search for solutions in random object or events by spontaneously shift back and forth between analytic and associative modes of thought. Keywords: Architectural model, Ronchamp, Design method, Imagination, Play, Objet trouve. DOI: http://dx.doi.org/10.4995/LC2015.2015.677
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Kou, X. Y., S. T. Tan, and W. S. Sze. "Relation Oriented Modeling for Heterogeneous Object Design." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85589.

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Relation oriented modeling approaches are proposed to design heterogeneous objects. The heterogeneous object modeling process is viewed as representing and manipulating complex geometrical, topological and material variation relations with proper data structures. Linear list structure, hierarchical tree structures and more general graph structures are used to represent complex heterogeneous objects. The powerful non-manifold cellular representation and the hierarchical heterogeneous feature tree representation are combined to model complex objects with simultaneous geometry intricacies and compound material variations. We demonstrate that relations play critical roles in heterogeneous object design and under the relation oriented framework, heterogeneous objects can be modeled with generic, uniform representations. The proposed relation oriented modeling approaches are tested with a prototype heterogeneous CAD modeler and presented with different types of heterogeneous object examples.
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Zhu, Zhuotun, Lingxi Xie, and Alan Yuille. "Object Recognition with and without Objects." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/505.

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While recent deep neural networks have achieved a promising performance on object recognition, they rely implicitly on the visual contents of the whole image. In this paper, we train deep neural networks on the foreground (object) and background (context) regions of images respectively. Considering human recognition in the same situations, networks trained on the pure background without objects achieves highly reasonable recognition performance that beats humans by a large margin if only given context. However, humans still outperform networks with pure object available, which indicates networks and human beings have different mechanisms in understanding an image. Furthermore, we straightforwardly combine multiple trained networks to explore different visual cues learned by different networks. Experiments show that useful visual hints can be explicitly learned separately and then combined to achieve higher performance, which verifies the advantages of the proposed framework.

Reports on the topic "A object":

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Hardie, T., M. Bowman, D. Hardy, M. Schwartz, and D. Wessels. CIP Index Object Format for SOIF Objects. RFC Editor, August 1999. http://dx.doi.org/10.17487/rfc2655.

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Fitzpatrick, Paul. Object Lesson: Discovering and Learning to Recognize Objects. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada434695.

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Haase, Ken. Soft Objects: A Paradigm for Object Oriented Programming. Fort Belvoir, VA: Defense Technical Information Center, March 1990. http://dx.doi.org/10.21236/ada225513.

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Ross, Andrew, David Johnson, Hai Le, Danny Griffin, Carl Mudd, and David Dawson. USACE Advanced Modeling Object Standard : Release 1.0. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42152.

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The U.S. Army Corps of Engineers (USACE) Advanced Modeling Object Standard (AMOS) has been developed by the CAD/BIM Technology Center for Facilities, Infrastructure, and Environment to establish standards for support of the Advanced Modeling process within the Department of Defense (DoD) and the Federal Government. The critical component of Advanced Modeling is the objects themselves- and either make the modeling process more difficult or more successful. This manual is part of an initiative to develop a nonproprietary Advanced Modeling standard that incorporates both vertical construction and horizontal construction objects that will address the entire life cycle of facilities within the DoD. The material addressed in this USACE Advanced Modeling Object Standard includes a classification organization that is needed to identify models for specific use cases. Compliance with this standard will allow users to know whether the object model they are getting is graphically well developed but data poor or if it does have the data needed for creating contract documents. This capability will greatly reduce the designers’ efforts to either build an object or search/find/edit an object necessary for the development of their project. Considering that an advanced model may contain hundreds of objects this would represent a huge time savings and improve the modeling process.
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Dodson, Nathan, Michael Paquette, and Garry Glaspell. Leveraging MOVEit for object inspection in simulation. Engineer Research and Development Center (U.S.), November 2023. http://dx.doi.org/10.21079/11681/47847.

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Herein we evaluate using a robotic arm with an attached camera to investigate objects of interest in simulation. Specifically, a Husky unmanned ground vehicle with a Panda Powertool was used in the simulation. The code enabled an operator to initiate a preconfigured set of motions when an object of interest was identified. The scan was stored in a database file that was used to generate a 3D mesh of the scanned object. The report describes both setting up the simulation and the code used to scan objects of interest.
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Amphay, Sengvieng, and David Gray. Infrared Stationary Object Acquisition and Moving Object Tracking. Fort Belvoir, VA: Defense Technical Information Center, March 2011. http://dx.doi.org/10.21236/ada538516.

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Bragdon, Sophia, Vuong Truong, and Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/45902.

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The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a dataset that consists of thermal images containing buried objects paired with the corresponding environmental and meteorological conditions, we employ a machine learning approach to determine which environmental conditions are the most impactful on the visibility of the buried objects. We find the key environmental conditions include incoming shortwave solar radiation, soil volumetric water content, and average air temperature. For each image, ROIs are computed using a computer vision approach and these ROIs are coupled with the most important environmental conditions to form the input for the classification algorithm. The environmentally informed classification algorithm produces a decision on whether the ROI contains a buried object by simultaneously learning on the ROIs with a classification neural network and on the environmental data using a tabular neural network. On a given set of ROIs, we have shown that the environmentally informed classification approach improves the detection of buried objects within the ROIs.
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Bormann, C. Concise Binary Object Representation (CBOR) Tags for Object Identifiers. RFC Editor, July 2021. http://dx.doi.org/10.17487/rfc9090.

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Abelson, L. A. Object-Oriented Metrics. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada400493.

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Wells, III, and William M. Statistical Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada270887.

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