Journal articles: 'Hidden objects'

1

Fahle, Manfred. "Detecting hidden objects." Nature 444, no. 7119 (November 2006): 549–50. http://dx.doi.org/10.1038/444549b.

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Zhang, Qing, Gelin Yin, Yongwei Nie, and Wei-Shi Zheng. "Deep Camouflage Images." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12845–52. http://dx.doi.org/10.1609/aaai.v34i07.6981.

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This paper addresses the problem of creating camouflage images. Such images typically contain one or more hidden objects embedded into a background image, so that viewers are required to consciously focus to discover them. Previous methods basically rely on hand-crafted features and texture synthesis to create camouflage images. However, due to lack of reliable understanding of what essentially makes an object recognizable, they typically result in either complete standout or complete invisible hidden objects. Moreover, they may fail to produce seamless and natural images because of the sensitivity to appearance differences. To overcome these limitations, we present a novel neural style transfer approach that adopts the visual perception mechanism to create camouflage images, which allows us to hide objects more effectively while producing natural-looking results. In particular, we design an attention-aware camouflage loss to adaptively mask out information that make the hidden objects visually standout, and also leave subtle yet enough feature clues for viewers to perceive the hidden objects. To remove the appearance discontinuities between the hidden objects and the background, we formulate a naturalness regularization to constrain the hidden objects to maintain the manifold structure of the covered background. Extensive experiments show the advantages of our approach over existing camouflage methods and state-of-the-art neural style transfer algorithms.

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Guo, Bin, Satoru Satake, and Michita Imai. "Home-Explorer: Ontology-Based Physical Artifact Search and Hidden Object Detection System." Mobile Information Systems 4, no. 2 (2008): 81–103. http://dx.doi.org/10.1155/2008/463787.

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A new system named Home-Explorer that searches and finds physical artifacts in a smart indoor environment is proposed. The view on which it is based is artifact-centered and uses sensors attached to the everyday artifacts (called smart objects) in the real world. This paper makes two main contributions: First, it addresses, the robustness of the embedded sensors, which is seldom discussed in previous smart artifact research. Because sensors may sometimes be broken or fail to work under certain conditions, smart objects become hidden ones. However, current systems provide no mechanism to detect and manage objects when this problem occurs. Second, there is no common context infrastructure for building smart artifact systems, which makes it difficult for separately developed applications to interact with each other and uneasy for them to share and reuse knowledge. Unlike previous systems, Home-Explorer builds on an ontology-based knowledge infrastructure named Sixth-Sense, which makes it easy for the system to interact with other applications or agents also based on this ontology. The hidden object problem is also reflected in our ontology, which enables Home-Explorer to deal with both smart objects and hidden objects. A set of rules for deducing an object's status or location information and for locating hidden objects are described and evaluated.

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Haddad, Jeffrey M., Yuping Chen, and Rachel Keen. "Preschoolers search for hidden objects." Journal of Experimental Child Psychology 109, no. 1 (May 2011): 123–31. http://dx.doi.org/10.1016/j.jecp.2010.12.004.

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Drozdek, Adam. "Object-Oriented Programming and Representation of Objects." Studies in Logic, Grammar and Rhetoric 40, no. 1 (March 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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Nawroth, C., M. Ebersbach, and E. von Borell. "A note on pigs’ knowledge of hidden objects." Archives Animal Breeding 56, no. 1 (October 10, 2013): 861–72. http://dx.doi.org/10.7482/0003-9438-56-086.

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Abstract. Object permanence is the notion that objects continue to exist even when they are out of observer´s sight. This ability is adaptive not only for free ranging animals who have to cope with a dangerous and highly changeable environment, allowing them to be aware of predators sneaking in their proximity or to keep track of conspecifics or food sources, even when out of sight. Farm animals, too, might profit from object permanence as the ability to follow the trajectory of hidden food or objects may lead to a higher predictability of subjects' environment, which in turn might affect the level of stress under husbandry conditions. We conducted two experiments to examine the ability of object permanence in young domestic pigs (Sus scrofa domestica). For this purpose we used a test setup that was formerly developed for primates and adopted it to the behavioural constraints of pigs. A rewarded object was hidden in one of three hiding locations with an increasing complexity of the objects movement through successive test sessions. Subjects were confronted with visible and invisible displacement tasks as well as with transpositions of hidden objects in different contextual settings. Pigs solved visible, but not invisible displacements or transpositions, indicating that they have difficulties to keep track of once hidden and then moved objects. This should be taken into account when designing husbandry environments or study designs.

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Kibbe, Melissa M., and Alan M. Leslie. "Conceptually Rich, Perceptually Sparse: Object Representations in 6-Month-Old Infants’ Working Memory." Psychological Science 30, no. 3 (January 22, 2019): 362–75. http://dx.doi.org/10.1177/0956797618817754.

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Six-month-old infants can store representations of multiple objects in working memory but do not always remember the objects’ features (e.g., shape). Here, we asked whether infants’ object representations (a) may contain conceptual content and (b) may contain this content even if perceptual features are forgotten. We hid two conceptually distinct objects (a humanlike doll and a nonhuman ball) one at a time in two separate locations and then tested infants’ memory for the first-hidden object by revealing either the original hidden object or an unexpected other object. Using looking time, we found that infants remembered the categorical identity of the hidden object but failed to remember its perceptual identity. Our results suggest that young infants may encode conceptual category in a representation of an occluded object, even when perceptual features are lost.

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Yan, Hui, Zhiguo Gong, Nan Zhang, Tao Huang, Hua Zhong, and Jun Wei. "Crawling Hidden Objects with kNN Queries." IEEE Transactions on Knowledge and Data Engineering 28, no. 4 (April 1, 2016): 912–24. http://dx.doi.org/10.1109/tkde.2015.2502947.

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Lidbetter, Thomas. "Search Games with Multiple Hidden Objects." SIAM Journal on Control and Optimization 51, no. 4 (January 2013): 3056–74. http://dx.doi.org/10.1137/120893938.

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Appleby, Roger, and Colin Cameron. "Seeing hidden objects with millimetre waves." Physics World 25, no. 01 (January 2012): 35–39. http://dx.doi.org/10.1088/2058-7058/25/01/34.

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Ruffman, Ted, Lance Slade, and Jessica Redman. "Young infants' expectations about hidden objects." Cognition 97, no. 2 (September 2005): B35—B43. http://dx.doi.org/10.1016/j.cognition.2005.01.007.

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Akhlaghi, Milad I., and Aristide Dogariu. "Tracking hidden objects using stochastic probing." Optica 4, no. 4 (April 20, 2017): 447. http://dx.doi.org/10.1364/optica.4.000447.

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Groot, Barbara, and Tineke Abma. "Boundary Objects: Engaging and Bridging Needs of People in Participatory Research by Arts-Based Methods." International Journal of Environmental Research and Public Health 18, no. 15 (July 26, 2021): 7903. http://dx.doi.org/10.3390/ijerph18157903.

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Background: Participatory health research (PHR) is a research approach in which people, including hidden populations, share lived experiences about health inequities to improve their situation through collective action. Boundary objects are produced, using arts-based methods, to be heard by stakeholders. These can bring about dialogue, connection, and involvement in a mission for social justice. This study aims to gain insight into the value and ethical issues of boundary objects that address health inequalities. A qualitative evaluation is conducted on three different boundary objects, created in different participatory studies with marginalized populations (mothers in poverty, psychiatric patients, and unemployed people). A successful boundary object evokes emotions among those who created the objects and those encountering these objects. Such objects move people and create an impulse for change. The more provocative the object, the more people feel triggered to foster change. Boundary objects may cross personal boundaries and could provoke feelings of discomfort and ignorance. Therefore, it is necessary to pay attention to ethics work. Boundary objects that are made by people from hidden populations may spur actions and create influence by improving the understanding of the needs of hidden populations. A dialogue about these needs is an essential step towards social justice.

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Tran, Thang Dai, Tuan Hong Do, and Ha Manh Hoang. "Development of system for detecting hidden objects based on UWB pulse radar." Science and Technology Development Journal 18, no. 3 (August 30, 2015): 111–21. http://dx.doi.org/10.32508/stdj.v18i3.892.

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This paper present a solution for detecting hidden objects based on UWB pulse radar. In this paper, we introduce an overview of UWB systems, the theory related and used in the process to implement the project. We present the steps to collect and process data through object identification algorithm, improve algorithm for detecting hidden objects in some kind of environments. We evaluate the obtained results, conclude for applying orientation and development of the subject.

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GOGUEN, JOSEPH A., and GRANT MALCOLM. "Hidden coinduction: behavioural correctness proofs for objects." Mathematical Structures in Computer Science 9, no. 3 (June 1999): 287–319. http://dx.doi.org/10.1017/s0960129599002777.

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This paper unveils and motivates an ambitious programme of hidden algebraic research in software engineering. We begin with an outline of our general goals, continue with an overview of results, and conclude with a discussion of some future plans. The main contribution is powerful hidden coinduction techniques for proving behavioural correctness of concurrent systems, and several mechanical proofs are given using OBJ3. We also show how modularization, bisimulation, transition systems, concurrency and combinations of the functional, constraint, logic and object paradigms fit into hidden algebra.

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Kim, Jongwan, and Yang-Hyun Cho. "An Advanced Scheme for Searching Spatial Objects and Identifying Hidden Objects." Journal of the Korea Institute of Information and Communication Engineering 18, no. 7 (July 31, 2014): 1518–24. http://dx.doi.org/10.6109/jkiice.2014.18.7.1518.

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Kim, Hoyoung, and Vijay Harid. "Numerical Modeling of Nondestructive Testing of Various Conductive Objects inside Metal Enclosures Using ELF/VLF Magnetic Fields." Applied Sciences 11, no. 8 (April 19, 2021): 3665. http://dx.doi.org/10.3390/app11083665.

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Nondestructive evaluation of various conductive objects through metal enclosures is investigated by using ELF/VLF magnetic induction fields in detailed simulations. ELF/VLF magnetic fields (<30 kHz) have a unique ability to penetrate highly conductive or permeable shields. Using a magnetic dipole source antenna, objects hidden inside a metal enclosure are imaged via examining distortions to the field outside the enclosure. The field distortion is parametrically studied by varying the size, conductivity, and permeability of the hidden objects. Furthermore, the importance of the conductivity of the enclosure itself is investigated using both low (106 S/m) and high (108 S/m) conductivity metallic shields. It is shown that the responses are quite sensitive to the object and shield parameters; both qualitative and quantitative properties of the field distortions are described in detail. The simulation results suggest that properties of hidden conductive or permeable objects, over a relatively wide range of parameters (both geometry and material), can be inferred nondestructively using ELF/VLF magnetic induction fields.

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MAHJOUB, MOHAMED ALI, and MALEK ABBASSI. "3D MESH OBJECT RETRIEVAL BY DISCRETE AND CONTINUOUS HIDDEN MARKOV MODELS." International Journal of Image and Graphics 12, no. 04 (October 2012): 1250022. http://dx.doi.org/10.1142/s0219467812500222.

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Research in databases of three-dimensional (3D) objects is a special area of research in our community. This paper is in this context and more generally in information retrieval by content. In this context, the authors are particularly interested in the indexing of 3D objects, which aims to characterize the shape of objects, to allow comparison between models. The 3D shape descriptor is presented and, based on the distribution of cords. To make the descriptor invariant to geometric transformations of space, a normalization step is applied to the object, as a preprocessing step. The work is, therefore, to develop a system for indexing and searching databases of 3D objects. The hidden Markov models are well suited to for this indexing system. Using these tools, a system for learning and recognizing of 3D objects is built.

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Marusic, Dragan. "Obvious and Hidden Symmetries of Mathematical Objects." Proceedings 2, no. 1 (January 9, 2018): 84. http://dx.doi.org/10.3390/proceedings2010084.

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Smith, K. A., E. Dechter, J. B. Tenenbaum, and E. Vul. "Tracking hidden objects with efficient physical prediction." Journal of Vision 14, no. 10 (August 22, 2014): 846. http://dx.doi.org/10.1167/14.10.846.

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Ben-Aderet, Yossi, Er’el Granot, Shmuel Sternklar, Tzachi Tal, and Natan S. Kopeika. "Optical imaging of hidden objects behind clothing." Applied Optics 49, no. 20 (July 6, 2010): 3926. http://dx.doi.org/10.1364/ao.49.003926.

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Watts, Siobhan, Dave Abbott, David Crombie, Angus Gunn, and Annemarie La Pensée. "SCIENCE REVEALED: THE HIDDEN STORY OF OBJECTS." Studies in Conservation 53, sup1 (January 2008): 146–50. http://dx.doi.org/10.1179/sic.2008.53.supplement-1.146.

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23

McCall, Daniel D., and Rachel K. Clifton. "Searching for hidden objects without visual feedback." Infant Behavior and Development 19 (April 1996): 608. http://dx.doi.org/10.1016/s0163-6383(96)90662-1.

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Biliris, A., S. Dar, and N. H. Gehani. "Making C++ objects persistent: The hidden pointers." Software: Practice and Experience 23, no. 12 (December 1993): 1285–303. http://dx.doi.org/10.1002/spe.4380231202.

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Boryssenko, Anatoliy. "Preventing Damage by Hidden Objects in Vegetation." IEEE Aerospace and Electronic Systems Magazine 21, no. 12 (December 2006): 12–18. http://dx.doi.org/10.1109/maes.2006.284378.

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Yakubov, V. P., S. É. Shipilov, and D. Ya Sukhanov. "Radio and ultrasound tomography of hidden objects." Russian Physics Journal 55, no. 8 (January 2013): 878–83. http://dx.doi.org/10.1007/s11182-013-9895-9.

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Miller, Holly C., Rebecca Rayburn-Reeves, and Thomas R. Zentall. "What do dogs know about hidden objects?" Behavioural Processes 81, no. 3 (July 2009): 439–46. http://dx.doi.org/10.1016/j.beproc.2009.03.018.

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Luchkina, Elena, and Sandra R. Waxman. "Semantic priming supports infants’ ability to learn names of unseen objects." PLOS ONE 16, no. 1 (January 7, 2021): e0244968. http://dx.doi.org/10.1371/journal.pone.0244968.

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Human language permits us to call to mind objects, events, and ideas that we cannot witness directly. This capacity rests upon abstract verbal reference: the appreciation that words are linked to mental representations that can be established, retrieved and modified, even when the entities to which a word refers is perceptually unavailable. Although establishing verbal reference is a pivotal achievement, questions concerning its developmental origins remain. To address this gap, we investigate infants’ ability to establish a representation of an object, hidden from view, from language input alone. In two experiments, 15-month-olds (N = 72) and 12-month-olds (N = 72) watch as an actor names three familiar, visible objects; she then provides a novel name for a fourth, hidden fully from infants’ view. In the Semantic Priming condition, the visible familiar objects all belong to the same semantic neighborhood (e.g., apple, banana, orange). In the No Priming condition, the objects are drawn from different semantic neighborhoods (e.g., apple, shoe, car). At test infants view two objects. If infants can use the naming information alone to identify the likely referent, then infants in the Semantic Priming, but not in the No Priming condition, will successfully infer the referent of the fourth (hidden) object. Brief summary of results here. Implications for the development of abstract verbal reference will be discussed.

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Luchkina, Elena, and Sandra R. Waxman. "Semantic priming supports infants’ ability to learn names of unseen objects." PLOS ONE 16, no. 1 (January 7, 2021): e0244968. http://dx.doi.org/10.1371/journal.pone.0244968.

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Human language permits us to call to mind objects, events, and ideas that we cannot witness directly. This capacity rests upon abstract verbal reference: the appreciation that words are linked to mental representations that can be established, retrieved and modified, even when the entities to which a word refers is perceptually unavailable. Although establishing verbal reference is a pivotal achievement, questions concerning its developmental origins remain. To address this gap, we investigate infants’ ability to establish a representation of an object, hidden from view, from language input alone. In two experiments, 15-month-olds (N = 72) and 12-month-olds (N = 72) watch as an actor names three familiar, visible objects; she then provides a novel name for a fourth, hidden fully from infants’ view. In the Semantic Priming condition, the visible familiar objects all belong to the same semantic neighborhood (e.g., apple, banana, orange). In the No Priming condition, the objects are drawn from different semantic neighborhoods (e.g., apple, shoe, car). At test infants view two objects. If infants can use the naming information alone to identify the likely referent, then infants in the Semantic Priming, but not in the No Priming condition, will successfully infer the referent of the fourth (hidden) object. Brief summary of results here. Implications for the development of abstract verbal reference will be discussed.

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Yagel, Roni, and William Ray. "Visibility Computation for Efficient Walkthrough of Complex Environments." Presence: Teleoperators and Virtual Environments 5, no. 1 (January 1996): 45–60. http://dx.doi.org/10.1162/pres.1996.5.1.45.

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In many virtual reality applications as well as general computer graphics we need to consider large numbers of objects to render one image. In many cases rendering can be preceded by a culling phase that employs simple mechanisms to reject most of the objects. As a result, only a very small portion of the model has to go through the time-consuming process of hidden object removal. We report on such a culling mechanism that is based on regular space subdivision into cells followed by cell classification into interior, exterior, and wall cells. A special cell-to-cell visibility algorithm is then activated between every two nonexterior cells. Only the objects in the potentially visible set of cells are actually submitted to the hidden object removal algorithm. We report on the implementation of the algorithm and its performance for walkthrough of various environments.

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31

Call, Josep. "Apes know that hidden objects can affect the orientation of other objects." Cognition 105, no. 1 (October 2007): 1–25. http://dx.doi.org/10.1016/j.cognition.2006.08.004.

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Törő, Olivér, Tamás Bécsi, and Péter Gáspár. "PHD Filter for Object Tracking in Road Traffic Applications Considering Varying Detectability." Sensors 21, no. 2 (January 11, 2021): 472. http://dx.doi.org/10.3390/s21020472.

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This paper considers the object detection and tracking problem in a road traffic situation from a traffic participant’s perspective. The information source is an automotive radar which is attached to the ego vehicle. The scenario characteristics are varying object visibility due to occlusion and multiple detections of a vehicle during a scanning interval. The goal is to maintain and report the state of undetected though possibly present objects. The proposed algorithm is based on the multi-object Probability Hypothesis Density filter. Because the PHD filter has no memory, the estimate of the number of objects present can change abruptly due to erroneous detections. To reduce this effect, we model the occlusion of the object to calculate the state-dependent detection probability. Thus, the filter can maintain unnoticed but probably valid hypotheses for a more extended period. We use the sequential Monte Carlo method with clustering for implementing the filter. We distinguish between detected, undetected, and hidden particles within our framework, whose purpose is to track hidden but likely present objects. The performance of the algorithm is demonstrated using highway radar measurements.

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Törő, Olivér, Tamás Bécsi, and Péter Gáspár. "PHD Filter for Object Tracking in Road Traffic Applications Considering Varying Detectability." Sensors 21, no. 2 (January 11, 2021): 472. http://dx.doi.org/10.3390/s21020472.

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This paper considers the object detection and tracking problem in a road traffic situation from a traffic participant’s perspective. The information source is an automotive radar which is attached to the ego vehicle. The scenario characteristics are varying object visibility due to occlusion and multiple detections of a vehicle during a scanning interval. The goal is to maintain and report the state of undetected though possibly present objects. The proposed algorithm is based on the multi-object Probability Hypothesis Density filter. Because the PHD filter has no memory, the estimate of the number of objects present can change abruptly due to erroneous detections. To reduce this effect, we model the occlusion of the object to calculate the state-dependent detection probability. Thus, the filter can maintain unnoticed but probably valid hypotheses for a more extended period. We use the sequential Monte Carlo method with clustering for implementing the filter. We distinguish between detected, undetected, and hidden particles within our framework, whose purpose is to track hidden but likely present objects. The performance of the algorithm is demonstrated using highway radar measurements.

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Kibbe, Melissa M., and Alan M. Leslie. "What Do Infants Remember When They Forget? Location and Identity in 6-Month-Olds’ Memory for Objects." Psychological Science 22, no. 12 (November 17, 2011): 1500–1505. http://dx.doi.org/10.1177/0956797611420165.

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What does an infant remember about a forgotten object? Although at age 6 months, infants can keep track of up to three hidden objects, they can remember the featural identity of only one. When infants forget the identity of an object, do they forget the object entirely, or do they retain an inkling of it? In a looking-time study, we familiarized 6-month-olds with a disk and a triangle placed on opposite sides of a stage. During test trials, we hid the objects one at a time behind different screens, and after hiding the second object, we removed the screen where the first object had been hidden. Infants then saw the expected object, the unexpected other object, or the empty stage. Bayes factor analysis showed that although the infants did not notice when the object changed shape, they were surprised when it vanished. This finding indicates that infants can represent an object without its features.

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Shinskey, Jeanne L., and Yuko Munakata. "Are infants in the dark about hidden objects?" Developmental Science 6, no. 3 (June 2003): 273–82. http://dx.doi.org/10.1111/1467-7687.00283.

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Eremeev, A. I., S. E. Shipilov, R. N. Satarov, I. S. Fedyanin, V. L. Khmelev, and V. P. Yakubov. "Non-contact detection of hidden objects under clothes." IOP Conference Series: Materials Science and Engineering 516 (April 26, 2019): 012014. http://dx.doi.org/10.1088/1757-899x/516/1/012014.

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Hernández-Pérez, Manuel. "Jane Jensen: Gabriel Knight, adventure games, hidden objects." Journal of Gender Studies 28, no. 4 (March 21, 2019): 492–94. http://dx.doi.org/10.1080/09589236.2019.1594111.

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Kushner, Sarah, Risa Ulinski, Karan Singh, David I. W. Levin, and Alec Jacobson. "Levitating Rigid Objects with Hidden Rods and Wires." Computer Graphics Forum 40, no. 2 (May 2021): 221–30. http://dx.doi.org/10.1111/cgf.142627.

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Goldfield, Beverly A. "Maternal Talk About Disappearance Events." Applied Psycholinguistics 16, no. 3 (1995): 309–24. http://dx.doi.org/10.1017/s0142716400065930.

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AbstractThis study examines maternal talk about events in which persons or objects are hidden, missing, or absent, and the relationship of maternal language to the child's acquisition of words for disappearance. Twelve mother-child pairs were videotaped in the home during play when the children were 12, 15, and 18 months of age. The mothers kept a diary of the children 's words. Maternal language was coded during four types of disappearance events: peekaboo, hidden object games, missing objects, and absent entities. Children who had acquiredgoneand similar terms experienced more disappearance events and heard more maternal talk about disappearance than children who had not acquired a word for disappearance.

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Brown, Michael E., and Jennie J. Gallimore. "Visualization of Cad Objects Using a Stereoscopic Display." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 38, no. 19 (October 1994): 1295–99. http://dx.doi.org/10.1177/154193129403801910.

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Subjects memorized the shape of a static 3-D object displayed on a stereoscopic CRT. In each of a series of trials that followed, single static objects were presented. The angular orientation of each trial object was one of six 36-degree increments relative to the angle of the memorized stimulus. The subject's task was to determine, as quickly and accurately as possible, whether the trial object was the same shape as the memorized object or its mirrored image. One of the two cases was always true. Disparity and interposition were manipulated in a within-subject manner during the initial memorization period and the trials that followed. Subject response time and error rate were evaluated. The experimental objective was to determine the extent to which stereopsis and hidden surface affect subjects' ability to 1) transfer to and retrieve from long-term memory spatial information about a 3-D object, and 2) visualize spatial characteristics in a quick and direct manner. Improved performance due to hidden surface is the most convincing experimental finding. The study also found a significant but limited stereopsis effect.

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Chieng, Wei-Hua, and D. A. Hoeltzel. "Polygon-to-Object Boundary Clipping in Object Space for Hidden Surface Removal in Computer-Aided Design." Journal of Mechanical Design 117, no. 3 (September 1, 1995): 374–89. http://dx.doi.org/10.1115/1.2826690.

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Since techniques for both polygon-to-polygon clipping and polygon-to-object boundary (contour) clipping have been developed, it appears that the visibility problem may exhibit potential for improvement in its time complexity. This paper provides some insight and results concerning the performance of an object-space hidden surface removal algorithm based on polygon-to-object boundary (contour) clipping. The applicability of these results to the graphic rendering of partially visible objects in an incremental computer-aided geometric design system, such as that used in mechanical design, is demonstrated. The polygon-to-object boundary clipping algorithm is compared with the more conventional polygon-to-polygon approach to clipping for hidden surface removal. Examples are included which demonstrate the potential for improving the performance of software-based hidden surface removal algorithms used in computer-aided geometric design applications.

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Perez-Cortes, Juan-Carlos, Alberto Perez, Sergio Saez-Barona, Jose-Luis Guardiola, and Ismael Salvador. "A System for In-Line 3D Inspection without Hidden Surfaces." Sensors 18, no. 9 (September 7, 2018): 2993. http://dx.doi.org/10.3390/s18092993.

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This work presents a 3D scanner able to reconstruct a complete object without occlusions, including its surface appearance. The technique presents a number of differences in relation to current scanners: it does not require mechanical handling like robot arms or spinning plates, it is free of occlusions since the scanned part is not resting on any surface and, unlike stereo-based methods, the object does not need to have visual singularities on its surface. This system, among other applications, allows its integration in production lines that require the inspection of a large volume of parts or products, especially if there is an important variability of the objects to be inspected, since there is no mechanical manipulation. The scanner consists of a variable number of industrial quality cameras conveniently distributed so that they can capture all the surfaces of the object without any blind spot. The object is dropped through the common visual field of all the cameras, so no surface or tool occludes the views that are captured simultaneously when the part is in the center of the visible volume. A carving procedure that uses the silhouettes segmented from each image gives rise to a volumetric representation and, by means of isosurface generation techniques, to a 3D model. These techniques have certain limitations on the reconstruction of object regions with particular geometric configurations. Estimating the inherent maximum error in each area is important to bound the precision of the reconstruction. A number of experiments are presented reporting the differences between ideal and reconstructed objects in the system.

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LI, Bo, Tian-Yu WO, Chun-Ming HU, Jian-Xin LI, Ying WANG, and Jin-Peng HUAI. "Hidden OS Objects Correlated Detection Technology Based on VMM." Journal of Software 24, no. 2 (December 27, 2013): 405–20. http://dx.doi.org/10.3724/sp.j.1001.2013.04265.

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Lu, Donna. "AI can spot objects even if they are hidden." New Scientist 246, no. 3282 (May 2020): 17. http://dx.doi.org/10.1016/s0262-4079(20)30920-9.

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Gariepy, Genevieve, Francesco Tonolini, Robert Henderson, Jonathan Leach, and Daniele Faccio. "Detection and tracking of moving objects hidden from view." Nature Photonics 10, no. 1 (December 7, 2015): 23–26. http://dx.doi.org/10.1038/nphoton.2015.234.

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Coats, Karen. "The Hidden Memory of Objects by Danielle Mages Amato." Bulletin of the Center for Children's Books 70, no. 8 (2017): 351. http://dx.doi.org/10.1353/bcc.2017.0272.

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Chan, S., R. E. Warburton, G. Gariepy, Y. Altmann, S. McLaughlin, J. Leach, and D. Faccio. "Fast tracking of hidden objects with single‐pixel detectors." Electronics Letters 53, no. 15 (July 2017): 1005–8. http://dx.doi.org/10.1049/el.2017.0993.

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Baillargeon, Renee. "Young infants' representation of the properties of hidden objects." Infant Behavior and Development 9 (April 1986): 15. http://dx.doi.org/10.1016/s0163-6383(86)80015-7.

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Fiset, Sylvain, Sylvain Gagnon, and Claude Beaulieu. "Spatial encoding of hidden objects in dogs (Canis familiaris)." Journal of Comparative Psychology 114, no. 4 (2000): 315–24. http://dx.doi.org/10.1037/0735-7036.114.4.315.

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Magele, C., W. Renhart, and B. Brandstätter. "Identification of hidden ferrous 3D objects using finite elements." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 20, no. 3 (September 2001): 689–98. http://dx.doi.org/10.1108/03321640110393671.

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Journal articles on the topic 'Hidden objects'

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