Relevant bibliographies by topics / Objects

Academic literature on the topic 'Objects'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Objects"

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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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Ju, Ginny, and Irving Biederman. "Tests of a Theory of Human Image Understanding: Part I the Perception of Colored and Partial Objects." Proceedings of the Human Factors Society Annual Meeting 30, no. 3 (September 1986): 297–300. http://dx.doi.org/10.1177/154193128603000322.

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Object recognition can be conceptualized as a process in which the perceptual input is successfully matched with a stored representation of the object. A theory of pattern recognition, Recognition by Components(RBC) assumes that objects are represented as simple volumetric primatives (e.g., bricks, cylinders, etc.) in specifed relations to each other. According to RBC, speeded recognition should be possible from only a few components, as long as those components uniquely identify an object. Neither the full complement of an object's components, nor the object's surface characteristics (e.g., color and texture) need be present for rapid identification. The results from two experiments on the perception of briefly presented objects are offered for supporting the sufficiency of the theory. Line drawings are identified about as rapidly and as accurately as full color slides. Partial objects could be rapidly (though not optimally) identified. Complex objects are more readily identified than simple objects.
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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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Wang, Chao, Xuehe Zhang, Xizhe Zang, Yubin Liu, Guanwen Ding, Wenxin Yin, and Jie Zhao. "Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review." Sensors 20, no. 13 (July 2, 2020): 3707. http://dx.doi.org/10.3390/s20133707.

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As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object’s features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.
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Azaad, Shaheed, and Simon M. Laham. "Pixel asymmetry predicts between-object differences in the object-based compatibility effect." Quarterly Journal of Experimental Psychology 73, no. 12 (August 16, 2020): 2376–88. http://dx.doi.org/10.1177/1747021820947374.

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When participants make left/right responses to unimanually graspable objects, response times (RTs) are faster when the responding hand is aligned with the viewed object’s handle. This object-based compatibility effect (CE) is often attributed to motor activation elicited by the object’s afforded grasp. However, some evidence suggests that the object-based CE is an example of spatial CEs, or Simon effects, elicited by the protruding nature of objects’ handles. Moreover, recent work shows that the way in which objects are centred on-screen might attenuate or reverse CEs, perhaps due to differences in pixel asymmetry (the proportion of pixels either side of fixation) between centralities. In this study, we tested whether pixel asymmetry also contributes to between-object variation in object-based CEs. In experiment 1 ( N = 34), we found that between-object differences in asymmetry predicted object-based CEs, such that objects with a greater proportion of pixels to the handle-congruent side of fixation produced larger CEs. In experiment 2 ( N = 35), we presented participants with mug (low asymmetry) and frying pan (high asymmetry) images and found that between-object and within-object (due to stimulus centrality) differences in pixel asymmetry interact to moderate CEs. Base-centred stimuli (centred according to the width of the object’s base) produced conventional CEs, whereas object-centred (centred according to the object’s total width) stimuli produced negative CEs (NCEs). Furthermore, the effect of centrality was smaller for mugs than pans, indicating an interaction between within-object and between-object differences in pixel asymmetry.
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Harlastputra, Amario Fausta, Hadi Nasbey, and Haris Suhendar. "YOLOv3 Algorithm to Measure Free Fall Time and Gravity Acceleration." Current STEAM and Education Research 1, no. 2 (December 31, 2023): 65–70. http://dx.doi.org/10.58797/cser.010204.

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Computer vision methods as an alternative to sensors in modern measurements are feasible in physics experiments due to their speed, accuracy, and low cost. The You Only Look Once (YOLO) algorithm is widely used in computer vision because it detects object positions quickly and accurately. This research uses YOLO version 3 (YOLOv3) to compute an object’s falling time and gravitational acceleration. Two steps are performed in this study: first, the detection of predefined objects using YOLOv3, and second, the use of trained YOLOv3 to track the object's coordinate. According to the object tracking results, the object's falling time can be measured based on the object tracking results. The gravitational acceleration is calculated using the time data after the fall time of the object is measured. The measurement result of the fall time of the object will be compared with the data from the sensor. The result of the gravitational acceleration calculation is measured for its relative error against the value of 9.78150 m/s2, which is the value of gravitational acceleration in Jakarta city. The results show that YOLOv3 can accurately detect objects and measure free-fall motion, with a time measurement error of only 1.1 milliseconds compared to sensor measurements. The error obtained from the measurement of the Earth's gravity is 0.634%.
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Sapkota, Raju P., Shahina Pardhan, and Ian van der Linde. "Change Detection in Visual Short-Term Memory." Experimental Psychology 62, no. 4 (September 2015): 232–39. http://dx.doi.org/10.1027/1618-3169/a000294.

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Abstract. Numerous kinds of visual event challenge our ability to keep track of the objects that populate our visual environment from moment to moment. These include blinks, occlusion, shifting visual attention, and changes to object’s visual and spatial properties over time. These visual events may lead to objects falling out of our visual awareness, but can also lead to unnoticed changes, such as undetected object replacements and positional exchanges. Current visual memory models do not predict which visual changes are likely to be the most difficult to detect. We examine the accuracy with which switches (where two objects exchange locations) and substitutions (where one or two objects are replaced) are detected. Inferior performance for one-object substitutions versus two-objects switches, along with superior performance for two-object substitutions versus two-object switches was found. Our results are interpreted in terms of object file theory, trade-offs between diffused and localized attention, and net visual change.
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Hui, Yuk. "On the Soul of Technical Objects: Commentary on Simondon’s ‘Technics and Eschatology’ (1972)." Theory, Culture & Society 35, no. 6 (March 8, 2018): 97–111. http://dx.doi.org/10.1177/0263276418757318.

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This article comments on a paper titled ‘Technique et eschatologie: le devenir des objets techniques’ that Gilbert Simondon presented in 1972. For Simondon, eschatology consists of a basic presupposition, which is the duality between the immortal soul and the corruptible body. The eschatology of technical objects can be seen as the object’s becoming against time. Simondon suggests that in the epoch of artisans, the product through its perfection searches for the ‘immortality of his producer’, while in the industrial epoch standardization becomes the key mover, in the sense that different parts of the object can be replaced. This analysis of Simondon on the relation between technics and eschatology allows a speculation on the soul of technical objects by tracing his earlier works. This conception of the soul, as this article tries to show, allows Simondon to address the alienation of technical objects in juxtaposition to a Marxist critique of alienation.
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Dissertations / Theses on the topic "Objects"

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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.
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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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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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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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Ježa, Pavel. "Návrh databázově neutrální objektově-relační vrstvy." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2007. http://www.nusl.cz/ntk/nusl-412788.

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This diploma work deals with design and implementation of the database neutral object-relational (OR) layer in C++ language over inherited database. The goal is to create the layer to encase the access to database from the application layer. Suggested layer will stem from the object-relation mapping technology, which is currently available for many object-programming language, such as C#, Java or Visual Basic. The work consists of three main parts. The forepart is focused on clearing object-relation mapping technology. It briefly overviews differences in capabilities and levels of implementation of various approaches. The next part describes significant properties of databases considered as back-ends for data storage in the project. The aim of this part is to present enough information to support database neutral design of the OR layer. The rest of the document deals with design and implementation of OR layer for the considered environment followed by the summarization of results and overall evaluational.
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Cichocki, Radoslaw. "Classification of objects in images based on various object representations." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5774.

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Object recognition is a hugely researched domain that employs methods derived from mathematics, physics and biology. This thesis combines the approaches for object classification that base on two features – color and shape. Color is represented by color histograms and shape by skeletal graphs. Four hybrids are proposed which combine those approaches in different manners and the hybrids are then tested to find out which of them gives best results.
Mail the author at radoslaw.cichocki(at)gmail.com
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Strukus, Wanda. "Unidentified performing objects : perception, phenomenology, and the object as actor /." Thesis, Connect to Dissertations & Theses @ Tufts University, 2003.

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Thesis (Ph.D.)--Tufts University, 2003.
Adviser: Laurence Senelick. Submitted to the Dept. of Drama and Dance. Includes bibliographical references (leaves 302-306). Access restricted to members of the Tufts University community. Also available via the World Wide Web;
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Books on the topic "Objects"

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Krešimir, Purgar, ed. Objekti =: Objects. Zagreb: Galerija grada Zagreba, 1991.

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Bechtold, Matias. Matias Bechtold: Objekte = objects. Berlin: Fantôme Verlag, 2016.

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Evansville Museum of Arts, History and Science, ed. Object project: Five objects, fifteen artists. Evansville, Ind: Evansville Museum of Arts, History & Science, 2007.

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author, Rupert Joe, ed. Object-oriented programming with ABAP Objects. 2nd ed. Bonn: Rheinwerk Publishing, 2015.

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Leclercq, Bruno, Sebastien Richard, and Denis Seron, eds. Objects and Pseudo-Objects. Berlin, München, Boston: DE GRUYTER, 2015. http://dx.doi.org/10.1515/9781501501371.

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Marquez, Lara. Objects: These are objects. England: Lara Marquez, 2020.

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Arnold, Beate Christiane. Schmuck, Kinetik, Objekte =: Jewellery, kinetics, objects. Edited by Joppien Rüdiger, Chadour-Sampson Anna Beatriz 1953-, and Becker Hildegard. 2nd ed. Stuttgart: Arnoldsche Art Publishers, 2001.

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Parr, Martin. Objects. London: Boot, 2008.

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Vera, Federico De. Objects. New York, NY: De Vera, 2010.

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

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Book chapters on the topic "Objects"

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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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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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Marquis, Hank, and Eric A. Smith. "Objects, Objects, and More Objects." In A Visual Basic 6 Programmer’s Toolkit, 269–87. Berkeley, CA: Apress, 2000. http://dx.doi.org/10.1007/978-1-4302-5125-5_16.

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Penz, Franz, and Luís Carriço. "Objects feeling objects in a multiview object space." In Human Computer Interaction, 25–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57312-7_56.

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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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Žižek, Slavoj. "Afterword: Objects, Objects Everywhere." In Slavoj Žižek and Dialectical Materialism, 177–92. New York: Palgrave Macmillan US, 2016. http://dx.doi.org/10.1057/9781137538611_13.

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Hunt, John. "Objects and Companion Objects." In Beginner's Guide to Kotlin Programming, 157–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80893-8_9.

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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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Conference papers on the topic "Objects"

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Skovira, Robert, Alex Koohang, Frederick Kohun, and Richard Will. "Panel Discussion - From Informing Objects to Learning Objects." In InSITE 2009: Informing Science + IT Education Conference. Informing Science Institute, 2009. http://dx.doi.org/10.28945/3362.

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Harman & Koohang (2005) stated that a learning object “ .. is not merely a chunk of information packaged to be used in instructional settings. A learning object, therefore, can include anything that has pedagogical value - digital or non-digital such as a case study, a film, a simulation, an audio, a video, an animation, a graphic image, a map, a book, or a discussion board so long as the object can be contextualized by individual learners. The learner must be able to make meaningful connections between the learning object and his/her experiences or knowledge he/she previously mastered. “ The above definition asserts that a learning object must have “pedagogical value” and that a learning object is 1) anything digital or non-digital such as a film, a simulation, or a case study, and 2) the ability of the learner to contextualize the object, i.e., the learner is capable of making “meaningful connections” between the object and his/her previous experiences and/or knowledge. Once the contextualization occurs, the object will have “pedagogical value” and it no longer merely an object, it is a learning object. Can all objects be contextualized? Is it necessary for all objects to be contextualized? What shall one call an “object” that is retrieved (and may even have some value other than pedagogical value and/or use) from an open access or commercial learning objects repository labeled as a “learning object” with no pedagogical value? If these objects are not learning objects, but have some sort of value and use, how shall one refer to these objects? Perhaps “informing 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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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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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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Buffo, Mathieu, and Didier Buchs. "Contextual Coordination between Objects." In Simpósio Brasileiro de Engenharia de Software. Sociedade Brasileira de Computação, 1996. http://dx.doi.org/10.5753/sbes.1996.24452.

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Notwithstanding the promises it provides for the future of software engineering, the object paradigm suffers from a lack of development methods focusing on the coordination between objects. The distinction of coordination and computation mechanisms is an important separation principle for increasing the management and understanding of the interactions among objects and the configurations of objects on the target systems. This paper shows that contextual coordination must be introduced during the object oriented analysis in order to early take into account the inevitable constraints of the concrete implementation. This contextual coordination leads to a hierarchical model composed of imbricated execution contexts of objects.
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Klimkov, Yu M., M. I. Shribak, and A. P. Shkadarevich. "A Laser Polarimetric Sensor for Measuring Angular Displacements of Objects." In The European Conference on Lasers and Electro-Optics. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/cleo_europe.1996.cwf53.

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One of the promising applications of laser autocollimating polarimetric sensors is measuring angular displacements of objects. For this purpose a parallel-sided plate, cut out of a birefringent crystal, and a retroreflector are mounted onto the object. The collimating beam of the polarimeter is sighted at the object and reflected back by the retroreflector. By this the beam pass two times through the birefringent plate. Retardance, caused by the plate in the beam, is proportionate to plate's tilt. Having measured the returdance by means of the polarimeter one can define the angle of the object's tilt [1].
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Horváth, Imre, and György Kuczogi. "Physical Modeling of Mechanical Objects of Constrained Movement." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/cie-4271.

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Abstract Presented is a computational method for physical modeling of mechanical objects of constrained movement. In a physical modeling environment, an object’s behavior is modeled as it is implied by its geometry and substance, and the appearing physical (mechanical, thermal, and other) phenomena. The developed software is able to describe various forms of movement of a rigid object, configure obstacles in the space, check for collisions of the moving object with the obstacles, and compute post-collision trajectories of the object. The motivation for the background research is an industrial problem, namely, spatial positioning of moving objects without using extra energy. The novelty of this work is in (a) the interactive definition of constituents of the modeling environment, (b) the integrated simulation of an arbitrary sequence of spatial movements and multiple collisions, and (c) the applicability to preliminary design of mechanical equipment. The process of physical modeling has been implemented in six phases: (a) geometric modeling and determination of the substantial attributes of the object, (b) computing of the pre-defined movements, (c) specification and positioning obstacles in the space, (d) checking for single and multiple collisions, (e) computing of post-collision movements, and (f) controlling of multiple and repeated events. Among others, the developed software is able to compute compound movements in the space and any sequence of eccentric collisions with four types of obstacles.
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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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Acharya, Manoj, Anirban Roy, Kaushik Koneripalli, Susmit Jha, Christopher Kanan, and Ajay Divakaran. "Detecting Out-Of-Context Objects Using Graph Contextual Reasoning Network." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/89.

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This paper presents an approach for detecting out-of-context (OOC) objects in images. Given an image with a set of objects, our goal is to determine if an object is inconsistent with the contextual relations and detect the OOC object with a bounding box. In this work, we consider common contextual relations such as co-occurrence relations, the relative size of an object with respect to other objects, and the position of the object in the scene. We posit that contextual cues are useful to determine object labels for in-context objects and inconsistent context cues are detrimental to determining object labels for out-of-context objects. To realize this hypothesis, we propose a graph contextual reasoning network (GCRN) to detect OOC objects. GCRN consists of two separate graphs to predict object labels based on the contextual cues in the image: 1) a representation graph to learn object features based on the neighboring objects and 2) a context graph to explicitly capture contextual cues from the neighboring objects. GCRN explicitly captures the contextual cues to improve the detection of in-context objects and identify objects that violate contextual relations. In order to evaluate our approach, we create a large-scale dataset by adding OOC object instances to the COCO images. We also evaluate on recent OCD benchmark. Our results show that GCRN outperforms competitive baselines in detecting OOC objects and correctly detecting in-context objects. Code and data: https://nusci.csl.sri.com/project/trinity-ooc
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Van Hove, Patrick. "Reconstruction of Axisymmetric Objects from One Silhouette." In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/srs.1986.thd3.

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Silhouettes of opaque convex objects in orthographic projections were studied in an earlier paper, using a representation on the Gaussian Sphere [1]. A similar argument is applied here to solve for the direct and inverse relations between axisymmetric objects and their silhouettes. Furthermore, a new method for determining object orientation in three dimensions is proposed for such objects, using our analysis of silhouette curvature [1]. Axisymmetric objects are completely determined by a section through their axis of symmetry, which we refer to as the object generator; their silhouettes are symmetric curves in the projection plane. The projection of the object into its silhouette reduces to a transformation between the object generator and the silhouette, which are both planar curves. This paper investigates the conditions under which inversion of this transformation is possible; see Fig. 1. Two cases arise, depending on whether or not the tilt is known between the axis of the object and the projection plane. The derivations summarized here apply to a large class of axisymmetric objects, including non-convex objects. The presentation will be accompanied by a large number of computer generated examples which illustrate various aspects of the theories we developed.
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Reports on the topic "Objects"

1

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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Birman, Kenneth P. Live Information Objects. Fort Belvoir, VA: Defense Technical Information Center, June 2011. http://dx.doi.org/10.21236/ada545398.

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Hoffmann, Christoph M. Modeling Physical Objects. Fort Belvoir, VA: Defense Technical Information Center, July 1990. http://dx.doi.org/10.21236/ada227982.

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Hoffmann, Christoph M. Modeling Physical Objects. Fort Belvoir, VA: Defense Technical Information Center, April 1990. http://dx.doi.org/10.21236/ada227983.

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Canavan, G. H. Radiation from hard objects. Office of Scientific and Technical Information (OSTI), February 1997. http://dx.doi.org/10.2172/469098.

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Herlihy, Maurice P., and Jennette M. Wing. Axioms for Concurrent Objects. Fort Belvoir, VA: Defense Technical Information Center, December 1987. http://dx.doi.org/10.21236/ada200584.

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Erdmann, Michael, and Thomas Lozano-Perez. On Multiple Moving Objects. Fort Belvoir, VA: Defense Technical Information Center, May 1986. http://dx.doi.org/10.21236/ada196213.

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Hoffmann, Christoph M. Interactive Modeling Physical Objects. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada231941.

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