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Journal articles on the topic 'A object'

Author: Grafiati

Published: 22 June 2024

Last updated: 31 July 2025

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1

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

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2

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

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3

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

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4

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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5

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

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6

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 (2008): 137–43. http://dx.doi.org/10.1007/s10339-008-0203-5.

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7

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

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8

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 (2018): 17. http://dx.doi.org/10.1167/18.9.17.

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9

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

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10

Gao, T., and B. Scholl. "Are objects required for object-files?" Journal of Vision 7, no. 9 (2010): 916. http://dx.doi.org/10.1167/7.9.916.

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11

Downey, T. Wayne. "Early Object Relations into New Objects." Psychoanalytic Study of the Child 56, no. 1 (2001): 39–67. http://dx.doi.org/10.1080/00797308.2001.11800664.

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12

Clark, Don. "Object-lessons from self-explanatory objects." Computers & Education 18, no. 1-3 (1992): 11–22. http://dx.doi.org/10.1016/0360-1315(92)90031-y.

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13

Chen, Liang-Chia, Thanh-Hung Nguyen, and Shyh-Tsong Lin. "Viewpoint-independent 3D object segmentation for randomly stacked objects using optical object detection." Measurement Science and Technology 26, no. 10 (2015): 105202. http://dx.doi.org/10.1088/0957-0233/26/10/105202.

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14

Schinkel, Anders. "The Object of History." Essays in Philosophy 7, no. 2 (2006): 212–31. http://dx.doi.org/10.5840/eip2006726.

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The phrase ‘the object of history’ may mean all sorts of things. In this article, a distinction is made between object1, the object of study for historians, and object2, the goal or purpose of the study of history. Within object2, a distinction is made between a goal intrinsic to the study of history (object2in) and an extrinsic goal (object2ex), the latter being what the study of history should contribute to society (or anything else outside itself). The main point of the article, which is illustrated by a discussion of the work of R. G. Collingwood, E. H. Carr, and G. R. Elton, is that in th

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15

Korda, Andrea. "Object Lessons in Victorian Education: Text, Object, Image." Journal of Victorian Culture 25, no. 2 (2020): 200–222. http://dx.doi.org/10.1093/jvcult/vcz064.

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Abstract Lessons on common objects, known as ‘object lessons’, were a customary occurrence in Victorian schoolrooms. This article looks at Victorian object lessons around mid-century as a means of examining the variety of meanings that common objects, and particularly manufactured objects, might have held both inside and outside Victorian schoolrooms. While model scripts for object lessons circulated widely and clarified the meaning of common objects in print, the objects themselves had the potential to complicate and challenge these meanings. Drawing primarily on publications by Elizabeth May

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16

Liu, Xiaoyang, Boran Wen, Xinpeng Liu, et al. "Interacted Object Grounding in Spatio-Temporal Human-Object Interactions." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 5622–30. https://doi.org/10.1609/aaai.v39i6.32599.

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Spatio-temporal Human-Object Interaction (ST-HOI) understanding aims at detecting HOIs from videos, which is crucial for activity understanding. However, existing whole-body-object interaction video benchmarks overlook the truth that open-world objects are diverse, that is, they usually provide limited and predefined object classes. Therefore, we introduce a new open-world benchmark: Grounding Interacted Objects (GIO) including 1,098 interacted objects class and 290K interacted object boxes annotation. Accordingly, an object grounding task is proposed expecting vision systems to discover inter

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17

Gainutdinova, Tatyana, Marina Denisova, and Olga Shirokova. "Current Trends in the Study of Object-Oriented Programming in Higher Education." ARPHA Proceedings 1 (November 5, 2019): 163–69. https://doi.org/10.3897/ap.1.e0152.

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The article examines the current methodological and technological problems of software development, based on an object-oriented approach in the field of programming. The relevance of the topic is determined by the general need to develop a methodology for teaching object-oriented programming based on object-oriented design. In this paper the problems of teaching object-oriented programming to students studying at the Faculty of Mathematics majoring in teacher education are formulated. The methodology for development of object-oriented projects related to the implementation of mathematical abst

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18

Kwon, Keehang. "Acessing Objects Locally in Object-Oriented Languages." Journal of Object Technology 4, no. 2 (2005): 151. http://dx.doi.org/10.5381/jot.2005.4.2.a4.

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19

Kogashi, Kaen, Yang Wu, Shohei Nobuhara, and Ko Nishino. "Human–object interaction detection with missing objects." Image and Vision Computing 113 (September 2021): 104262. http://dx.doi.org/10.1016/j.imavis.2021.104262.

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20

Erlikhman, Gennady, Taissa Lytchenko, Nathan H. Heller, Marvin R. Maechler, and Gideon P. Caplovitz. "Object-based attention generalizes to multisurface objects." Attention, Perception, & Psychophysics 82, no. 4 (2020): 1599–612. http://dx.doi.org/10.3758/s13414-019-01964-5.

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21

Sun, Zekun, and Chaz Firestone. "Curious objects: Preattentive processing of object complexity." Journal of Vision 18, no. 10 (2018): 1055. http://dx.doi.org/10.1167/18.10.1055.

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22

Wong, J. H., A. P. Hillstrom, and Y. C. Chai. "What changes to objects disrupt object constancy?" Journal of Vision 5, no. 8 (2005): 1042. http://dx.doi.org/10.1167/5.8.1042.

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23

Reiland, Hans. "The object beyond objects and the sacred." Scandinavian Psychoanalytic Review 27, no. 2 (2004): 78–86. http://dx.doi.org/10.1080/01062301.2004.10592945.

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24

Li, M., and Z. Yang. "Statistics of natural objects and object recognition." Journal of Vision 10, no. 7 (2010): 993. http://dx.doi.org/10.1167/10.7.993.

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25

HARRISON, VICTORIA S. "Mathematical objects and the object of theology." Religious Studies 53, no. 4 (2016): 479–96. http://dx.doi.org/10.1017/s0034412516000238.

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AbstractThis article brings mathematical realism and theological realism into conversation. It outlines a realist ontology that characterizes abstract mathematical objects as inaccessible to the senses, non-spatiotemporal, and acausal. Mathematical realists are challenged to explain how we can know such objects. The article reviews some promising responses to this challenge before considering the view that the object of theology also possesses the three characteristic features of abstract objects, and consequently may be known through the same methods that yield knowledge of mathematical objec

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26

Takahashi, Kohske, and Katsumi Watanabe. "Seeing Objects as Faces Enhances Object Detection." i-Perception 6, no. 5 (2015): 204166951560600. http://dx.doi.org/10.1177/2041669515606007.

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27

Drozdek, Adam. "Object-Oriented Programming and Representation of Objects." Studies in Logic, Grammar and Rhetoric 40, no. 1 (2015): 293–302. http://dx.doi.org/10.1515/slgr-2015-0014.

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Abstract In this paper, a lesson is drawn from the way class definitions are provided in object-oriented programming. The distinction is introduced between the visible structure given in a class definition and the hidden structure, and then possible connections are indicated between these two structures and the structure of an entity modeled by the class definition.

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28

Albert, Elvira, Puri Arenas, Jesús Correas, et al. "Object-sensitive cost analysis for concurrent objects." Software Testing, Verification and Reliability 25, no. 3 (2015): 218–71. http://dx.doi.org/10.1002/stvr.1569.

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29

Chow, Jason, Thomas Palmeri, and Isabel Gauthier. "Tactile object recognition performance on graspable objects, but not texture-like objects, relates to visual object recognition ability." Journal of Vision 20, no. 11 (2020): 188. http://dx.doi.org/10.1167/jov.20.11.188.

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30

Brickman, Arthur S. ""Object"? I object." Psychoanalytic Psychology 26, no. 4 (2009): 402–14. http://dx.doi.org/10.1037/a0017715.

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31

Liao, Xiaolian, Jun Li, Leyi Li, Caoxi Shangguan, and Shaoyan Huang. "RGBD Salient Object Detection, Based on Specific Object Imaging." Sensors 22, no. 22 (2022): 8973. http://dx.doi.org/10.3390/s22228973.

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RGBD salient object detection, based on the convolutional neural network, has achieved rapid development in recent years. However, existing models often focus on detecting salient object edges, instead of objects. Importantly, detecting objects can more intuitively display the complete information of the detection target. To take care of this issue, we propose a RGBD salient object detection method, based on specific object imaging, which can quickly capture and process important information on object features, and effectively screen out the salient objects in the scene. The screened target ob

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32

Zhang, Jun, Feiteng Han, Yutong Chun, Kangwei Liu, and Wang Chen. "Detecting Objects from No-Object Regions: A Context-Based Data Augmentation for Object Detection." International Journal of Computational Intelligence Systems 14, no. 1 (2021): 1871. http://dx.doi.org/10.2991/ijcis.d.210622.003.

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33

Li, Yongdong, Liang Qu, Guiyan Cai, et al. "Video Object Counting With Scene-Aware Multi-Object Tracking." Journal of Database Management 34, no. 3 (2023): 1–13. http://dx.doi.org/10.4018/jdm.321553.

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The critical challenge of video object counting is to avoid counting the same object multiple times in different frames. By comparing the appearance and motion feature information of the detection results, the authors use the multi-object tracking method to assign an independent ID number to each object. From the time the ID tag is obtained until the end of the video, each object is counted only once. However, even minor amounts of image noise can cause irreversible changes in feature information, resulting in severe tracking drifts. This paper introduces the concept of scene awareness and add

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34

Landau, Barbara, Linda Smith, and Susan Jones. "Object Shape, Object Function, and Object Name." Journal of Memory and Language 38, no. 1 (1998): 1–27. http://dx.doi.org/10.1006/jmla.1997.2533.

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35

Su, Ching-Liang. "Manufacture Automation: Model and Object Recognition by Using Object Position Auto Locating Algorithm and Object Comparison Model." JALA: Journal of the Association for Laboratory Automation 5, no. 2 (2000): 61–65. http://dx.doi.org/10.1016/s1535-5535-04-00062-0.

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This research uses the geometry matching technique to identify the different objects. The object is extracted from the background. The second moment 6 is used to find the orientation and the center point of the extracted object. Since the second moment can find the orientations and the center point of the object, the perfect object and the test object can be aligned to the same orientation. Furthermore, these two images can be shifted to the same centroid. After this, the perfect object can be subtracted from the test face. By using the subtracted result, the objects can be classified. The tec

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36

Rivas, Javier. "VERB-INDIRECT OBJECT-DIRECT OBJECT and VERB-DIRECT OBJECT-INDIRECT OBJECT." Isogloss. Open Journal of Romance Linguistics 11, no. 5 (2025): 1–20. https://doi.org/10.5565/rev/isogloss.511.

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The purpose of this paper is to contribute to the study of constituent order in ditransitive constructions by means of a variationist analysis based on spoken Spanish from Madrid. We analyze the variable position of objects in ditransitive clauses. We identify two variants: (1) Verb-Direct Object-Indirect Object and (2) Verb-Indirect Object-Direct Object. We include in the envelope of variation ditransitive constructions in which the DO is expressed by a noun phrase (sometimes preceded by the marker a) and the IO is expressed by means of a prepositional phrase preceded by a ‘to’. The percentag

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37

Huan, Ruo Hong, Xiao Mei Tang, Zhe Hu Wang, and Qing Zhang Chen. "Abnormal Motion Detection for Intelligent Video Surveillance." Applied Mechanics and Materials 58-60 (June 2011): 2290–95. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2290.

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A method of abnormal motion detection for intelligent video surveillance is presented, which includes object intrusion detection, object overlong stay detection and object overpopulation detection. Background subtraction algorithm is used to detect moving objects in video streams. Kalman filter is applied for object tracking. By the construction of relation matrix, the tracking process is divided into five statuses for prediction and estimation, which are object disappearing, object separating, new object appearing, object sheltering and object matching. The object parameters and predictive in

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38

Xu, Fei. "Categorization, object individuation, and object manipulation: Commentary on “Object manipulation facilitates kind-based object individuation of shape-similar objects” by O.S. Kingo and P. Krøjgaard (2011)." Cognitive Development 26, no. 2 (2011): 104–5. http://dx.doi.org/10.1016/j.cogdev.2011.03.001.

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39

Kudo, Yasuo, and Tetsuya Murai. "A Theoretical Formulation of Object-Oriented Rough Set Models." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 5 (2006): 612–20. http://dx.doi.org/10.20965/jaciii.2006.p0612.

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We introduce object-oriented paradigm into rough set theory. First, we provide concepts of class, object, and name, respectively. Class structures represent abstract data forms, and abstract structural hierarchy based on is-a relationship and has-a relationship. Object structures illustrate many kinds of objects and actual dependence among objects by is-a relationship and has-a relationship. Name structures provide concrete design of objects, and connect class structures and object structures consistently. Next, combining class, name and object structures, we propose object-oriented informatio

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40

Mamta, A. Baitule* Prof. Mukund R. Joshi. "OBJECT DETECTION AND TRACKING ALGORITHM FOR LOW VISION VIDEO." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 661–65. https://doi.org/10.5281/zenodo.51857.

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We propose a general framework for Object Recognition into regions and objects. In this framework, the detection and recognition of objects proceed simultaneously with image segmentation in a competitive and cooperative manner .Videos are a collection of sequential images with a constant time interval. So video can provide more information about our object when scenarios are changing with respect to time. Therefore, manually handling videos are quite impossible. So we need an automated devise to process these videos. Object tracking is  a process of segmenting a region of interest from a

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41

Xu, Yaoda. "Distinctive Neural Mechanisms Supporting Visual Object Individuation and Identification." Journal of Cognitive Neuroscience 21, no. 3 (2009): 511–18. http://dx.doi.org/10.1162/jocn.2008.21024.

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Many everyday activities, such as driving on a busy street, require the encoding of distinctive visual objects from crowded scenes. Given resource limitations of our visual system, one solution to this difficult and challenging task is to first select individual objects from a crowded scene (object individuation) and then encode their details (object identification). Using functional magnetic resonance imaging, two distinctive brain mechanisms were recently identified that support these two stages of visual object processing. While the inferior intraparietal sulcus (IPS) selects a fixed number

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42

Shen, Mowei, Wenjun Yu, Xiaotian Xu, and Zaifeng Gao. "Building Blocks of Visual Working Memory: Objects or Boolean Maps?" Journal of Cognitive Neuroscience 25, no. 5 (2013): 743–53. http://dx.doi.org/10.1162/jocn_a_00348.

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The nature of the building blocks of information in visual working memory (VWM) is a fundamental issue that has not been well resolved. Most researchers take objects as the building blocks, although this perspective has received criticism. The objects could be physically separated ones (strict object hypothesis) or hierarchical objects created from separated individuals (broad object hypothesis). Meanwhile, a newly proposed Boolean map theory for visual attention suggests that Boolean maps may be the building blocks of VWM (Boolean map hypothesis); this perspective could explain many critical

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43

Zafar, Adeel, and Umar Khalid. "Detect-and-describe: Joint learning framework for detection and description of objects." MATEC Web of Conferences 277 (2019): 02028. http://dx.doi.org/10.1051/matecconf/201927702028.

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Traditional object detection answers two questions; “what” (what the object is?) and “where” (where the object is?). “what” part of the object detection can be fine grained further i-e. “what type”, “what shape” and “what material” etc. This results in shifting of object detection task to object description paradigm. Describing object provides additional detail that enables us to understand the characteristics and attributes of the object (“plastic boat” not just boat, “glass bottle” not just bottle). This additional information can implicitly be used to gain insight about unseen objects (e.g.

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44

WON, WOONG-JAE, JIYOUNG YEO, SANG-WOO BAN, and MINHO LEE. "BIOLOGICALLY MOTIVATED INCREMENTAL OBJECT PERCEPTION BASED ON SELECTIVE ATTENTION." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 08 (2007): 1293–305. http://dx.doi.org/10.1142/s021800140700596x.

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In this paper, we propose an object selective attention and perception system, which was implemented by integrating a specific object preferable attention model with an incremental object perception model. The object oriented attention model can selectively pay attention to the candidates of an object in natural scenes based on a bottom-up selective attention model in conjunction with a top-down biased attention mechanism for a specific object. A generative model based on an incremental Bayesian parameter estimation is considered in order to perceive arbitrary objects in the attended areas. Co

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45

Yoo, Yong-Sang, Seong-Ho Lee, and Seung-Hwan Bae. "Effective Multi-Object Tracking via Global Object Models and Object Constraint Learning." Sensors 22, no. 20 (2022): 7943. http://dx.doi.org/10.3390/s22207943.

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Effective multi-object tracking is still challenging due to the trade-off between tracking accuracy and speed. Because the recent multi-object tracking (MOT) methods leverage object appearance and motion models so as to associate detections between consecutive frames, the key for effective multi-object tracking is to reduce the computational complexity of learning both models. To this end, this work proposes global appearance and motion models to discriminate multiple objects instead of learning local object-specific models. In concrete detail, it learns a global appearance model using contras

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46

Dodani, Mahesh H. "The Dark Side of Object Learning: Learning Objects." Journal of Object Technology 1, no. 5 (2002): 37. http://dx.doi.org/10.5381/jot.2002.1.5.c3.

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47

Nagata, Kazuyuki, and Takao Nishi. "Modeling object arrangement patterns and picking arranged objects." Advanced Robotics 35, no. 16 (2021): 981–94. http://dx.doi.org/10.1080/01691864.2021.1948446.

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48

강승만. "Double Object Constructions: Indirect Objects as PP Goals." Studies in English Language & Literature 37, no. 4 (2011): 167–81. http://dx.doi.org/10.21559/aellk.2011.37.4.009.

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49

Hayes, Barry. "Using key object opportunism to collect old objects." ACM SIGPLAN Notices 26, no. 11 (1991): 33–46. http://dx.doi.org/10.1145/118014.117957.

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50

Campos, Joana, and Vasco T. Vasconcelos. "Channels as Objects in Concurrent Object-Oriented Programming." Electronic Proceedings in Theoretical Computer Science 69 (October 18, 2011): 12–28. http://dx.doi.org/10.4204/eptcs.69.2.

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