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Littérature scientifique sur le sujet « Visual regularities »

Auteur : Grafiati

Publié le 11 mars 2023

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Articles de revues sur le sujet "Visual regularities"

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Besle, Julien, Zahra Hussain, Marie-Hélène Giard, and Olivier Bertrand. "The Representation of Audiovisual Regularities in the Human Brain." Journal of Cognitive Neuroscience 25, no. 3 (2013): 365–73. http://dx.doi.org/10.1162/jocn_a_00334.

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Neural representation of auditory regularities can be probed using the MMN, a component of ERPs generated in the auditory cortex by any violation of that regularity. Although several studies have shown that visual information can influence or even trigger an MMN by altering an acoustic regularity, it is not known whether audiovisual regularities are encoded in the auditory representation supporting MMN generation. We compared the MMNs elicited by the auditory violation of (a) an auditory regularity (a succession of identical standard sounds), (b) an audiovisual regularity (a succession of iden

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van der Helm, Peter A., and Emanuel L. J. Leeuwenberg. "Goodness of visual regularities: A nontransformational approach." Psychological Review 103, no. 3 (1996): 429–56. http://dx.doi.org/10.1037/0033-295x.103.3.429.

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Kimura, Motohiro, Erich Schröger, István Czigler, and Hideki Ohira. "Human Visual System Automatically Encodes Sequential Regularities of Discrete Events." Journal of Cognitive Neuroscience 22, no. 6 (2010): 1124–39. http://dx.doi.org/10.1162/jocn.2009.21299.

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For our adaptive behavior in a dynamically changing environment, an essential task of the brain is to automatically encode sequential regularities inherent in the environment into a memory representation. Recent studies in neuroscience have suggested that sequential regularities embedded in discrete sensory events are automatically encoded into a memory representation at the level of the sensory system. This notion is largely supported by evidence from investigations using auditory mismatch negativity (auditory MMN), an event-related brain potential (ERP) correlate of an automatic memory-misma

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Yu, Ru Qi, and Jiaying Zhao. "How do regularities bias attention to visual targets?" Journal of Vision 19, no. 10 (2019): 26c. http://dx.doi.org/10.1167/19.10.26c.

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Ball, Felix, Inga Spuerck, and Toemme Noesselt. "Minimal interplay between explicit knowledge, dynamics of learning and temporal expectations in different, complex uni- and multisensory contexts." Attention, Perception, & Psychophysics 83, no. 6 (2021): 2551–73. http://dx.doi.org/10.3758/s13414-021-02313-1.

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AbstractWhile temporal expectations (TE) generally improve reactions to temporally predictable events, it remains unknown how the learning of temporal regularities (one time point more likely than another time point) and explicit knowledge about temporal regularities contribute to performance improvements; and whether any contributions generalise across modalities. Here, participants discriminated the frequency of diverging auditory, visual or audio-visual targets embedded in auditory, visual or audio-visual distractor sequences. Temporal regularities were manipulated run-wise (early vs. late

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Storrs, Katherine R., and Roland W. Fleming. "Learning About the World by Learning About Images." Current Directions in Psychological Science 30, no. 2 (2021): 120–28. http://dx.doi.org/10.1177/0963721421990334.

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One of the deepest insights in neuroscience is that sensory encoding should take advantage of statistical regularities. Humans’ visual experience contains many redundancies: Scenes mostly stay the same from moment to moment, and nearby image locations usually have similar colors. A visual system that knows which regularities shape natural images can exploit them to encode scenes compactly or guess what will happen next. Although these principles have been appreciated for more than 60 years, until recently it has been possible to convert them into explicit models only for the earliest stages of

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Parkinson, Jean, James Mackay, and Murielle Demecheleer. "Putting yourself into your work: expression of visual meaning in student technical writing." Visual Communication 19, no. 2 (2018): 281–306. http://dx.doi.org/10.1177/1470357218784323.

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Students in technical fields use visual as well as verbal modes to express their meaning, employing ways of expressing meaning that are useful later in their professional lives. This study investigates visual meaning in student Builders’ Diaries, journals that are written by carpentry trainees to provide a record of their learning. In professional carpentry practice, Diaries function as a record of building work and are used in planning, billing and record-keeping. For this study, a corpus of 43 Builders’ Diaries, written by apprentices working in industry and by trainees in an educational ins

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Bettoni, Roberta, Hermann Bulf, Shannon Brady, and Scott P. Johnson. "Infants’ learning of non‐adjacent regularities from visual sequences." Infancy 26, no. 2 (2021): 319–26. http://dx.doi.org/10.1111/infa.12384.

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Lelonkiewicz, Jarosław R., Maria Ktori, and Davide Crepaldi. "Morphemes as letter chunks: Discovering affixes through visual regularities." Journal of Memory and Language 115 (December 2020): 104152. http://dx.doi.org/10.1016/j.jml.2020.104152.

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Kimura, Motohiro, Andreas Widmann, and Erich Schröger. "Human visual system automatically represents large-scale sequential regularities." Brain Research 1317 (March 2010): 165–79. http://dx.doi.org/10.1016/j.brainres.2009.12.076.

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Thèses sur le sujet "Visual regularities"

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Santolin, Chiara. "Learning Regularities from the Visual World." Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3424417.

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Patterns of visual objects, streams of sounds, and spatiotemporal events are just a few examples of the structures present in a variety of sensory inputs. Amid such variety, numerous regularities can be found. In order to handle the sensory processing, individuals of each species have to be able to rapidly track these regularities. Statistical learning is one of the principal mechanisms that enable to track patterns from the flow of sensory information, by detecting coherent relations between elements (e.g., A predicts B). Once relevant structures are detected, learners are sometimes required

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Kaiser, Daniel. "Inter-object grouping in visual processing: How the brain uses real-world regularities to carve up the environment." Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/367999.

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In everyday situations humans are continuously confronted with complex and cluttered visual environments that contain a large number of objects. Despite this complexity, performance in real-life tasks is surprisingly efficient. As a novel explanation for this efficiency, we propose that the brain uses typical regularities between objects (e.g., lamps are typically appearing above dining tables) to group these objects to reduce complexity and thereby facilitate behavioral performance. In a series of experiments, we show that object regularities reduce competitive interactions in visual cortex,

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Yu, Ying. "Visual Appearances of the Metric Shapes of Three-Dimensional Objects: Variation and Constancy." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1592254922173432.

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Zhou, Yi. "Exploiting Structural Regularities and Beyond: Vision-based Localization and Mapping in Man-Made Environments." Phd thesis, 2018. http://hdl.handle.net/1885/155255.

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Image-based estimation of camera motion, known as visual odometry (VO), plays a very important role in many robotic applications such as control and navigation of unmanned mobile robots, especially when no external navigation reference signal is available. The core problem of VO is the estimation of the camera’s ego-motion (i.e. tracking) either between successive frames, namely relative pose estimation, or with respect to a global map, namely absolute pose estimation. This thesis aims to develop efficient, accurate and robust VO solutions by taking

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Chapitres de livres sur le sujet "Visual regularities"

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Czigler, István. "4. Representation of regularities in visual stimulation." In Unconscious Memory Representations in Perception. John Benjamins Publishing Company, 2010. http://dx.doi.org/10.1075/aicr.78.06czi.

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Krüger, Norbert, Thomas Jäger, and Christian Perwass. "Extraction of Object Representations from Stereo Image Sequences Utilizing Statistical and Deterministic Regularities in Visual Data." In Biologically Motivated Computer Vision. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36181-2_32.

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Honour Chika, Nwagwu, Ukekwe Emmanuel, Ugwoke Celestine, Ndoumbe Dora, and Okereke George. "Visual Identification of Inconsistency in Pattern." In Pattern Recognition [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.95506.

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The visual identification of inconsistencies in patterns is an area in computing that has been understudied. While pattern visualisation exposes the relationships among identified regularities, it is still very important to identify inconsistencies (irregularities) in identified patterns. The significance of identifying inconsistencies for example in the growth pattern of children of a particular age will enhance early intervention such as dietary modifications for stunted children. It is described in this chapter, the need to have a system that identifies inconsistencies in identified pattern of a dataset. Also, techniques that enable the visual identification of inconsistencies in patterns such as fault tolerance and colour coding are described. Two approaches are presented in this chapter for visualising inconsistencies in patterns namely; visualising inconsistencies in objects with many attribute values and visual comparison of an investigated dataset with a case control dataset. These approaches are associated with tools which were developed by the authors of this chapter: Firstly, ConTra which allows its users to mine and analyse the contradictions in attribute values whose data does not abide by the mutual exclusion rule of the dataset. Secondly, Datax which mines missing data; enables the visualisation of the missingness and the identification of the associated patterns. Finally, WellGrowth which explores Children’s growth dataset by comparing an investigated dataset (data obtained from a Primary Health Centre) with a case control dataset (data from the website of World Health Organisation). Instances of inconsistencies as discovered in the explored datasets are discussed.

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Krüger, Norbert, and Florentin Wörgötter. "Statistical and Deterministic Regularities: Utilization of Motion and Grouping in Biological and Artificial Visual Systems." In Advances in Imaging and Electron Physics. Elsevier, 2004. http://dx.doi.org/10.1016/s1076-5670(04)31003-7.

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Ciaunica, Anna, and Aikaterini Fotopoulou. "The Touched Self: Psychological and Philosophical Perspectives on Proximal Intersubjectivity and the Self." In Embodiment, Enaction, and Culture. The MIT Press, 2017. http://dx.doi.org/10.7551/mitpress/9780262035552.003.0009.

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Is minimal selfhood a build-in feature of our experiential life (Gallagher 2005; Zahavi 2005, 2014; Legrand 2006) or a later socio-culturally determined acquisition, emerging in the process of social exchanges and mutual interactions (Fonagy et al. 2004; Prinz 2012; Schmid 2014)? This chapter, building mainly on empirical research on affective touch and interoception, argues in favor of a reconceptualization of minimal selfhood that surpasses such debates, and their tacitly “detached,” visuo-spatial models of selfhood and otherness. Instead, the relational origins of the self are traced on fundamental principles and regularities of the human embodied condition, such as the amodal properties that govern the organization of sensorimotor signals into distinct perceptual experiences. Interactive experiences with effects on “within” and “on” the physical boundaries of the body (e.g., skin-to-skin touch) are necessary for such organization in early infancy when the motor system is not as yet developed. Therefore, an experiencing subject is not primarily understood as facing another subject “there.” Instead, the minimal self is by necessity co-constituted by other bodies in physical contact and proximal interaction.

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Actes de conférences sur le sujet "Visual regularities"

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Li, Xin, Yijia He, Jinlong Lin, and Xiao Liu. "Leveraging Planar Regularities for Point Line Visual-Inertial Odometry." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9341278.

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Rosinol, Antoni, Torsten Sattler, Marc Pollefeys, and Luca Carlone. "Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities." In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8794456.

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Myung Hwangbo and Takeo Kanade. "Visual-inertial UAV attitude estimation using urban scene regularities." In 2011 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2011. http://dx.doi.org/10.1109/icra.2011.5979542.

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Romberg, Alexa, Yayun Zhang, Benjamin Newman, Jochen Triesch, and Chen Yu. "Global and local statistical regularities control visual attention to object sequences." In 2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE, 2016. http://dx.doi.org/10.1109/devlrn.2016.7846829.

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Kim, Pyojin, Brian Coltin, and Hyounjin Kim. "Visual Odometry with Drift-Free Rotation Estimation Using Indoor Scene Regularities." In British Machine Vision Conference 2017. British Machine Vision Association, 2017. http://dx.doi.org/10.5244/c.31.62.

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Heeger, David J., and Alexander P. Pentland. "Seeing structure through chaos." In OSA Annual Meeting. Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wd5.

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Research in motion perception has concentrated on recognizing regularities in images due to rigid motion. However, rigid motion is only one type of motion that occurs in our world. There is also elastic motion, fluid motion, and turbulent flow. Examples of turbulent flow include clouds, waves, boiling water, rustling leaves, or flags fluttering in the wind. Fully developed turbulent flow is completely chaotic and incoherent motion. Yet, it has a regular statistical structure underlying the apparent chaos. These regularities are due to the coherence of the physical process which generates turbu

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O'Doherty, Cliona, and Rhodri Cusack. "Objects or Context? Learning From Temporal Regularities in Continuous Visual Experience With an Infant-inspired DNN." In 2022 Conference on Cognitive Computational Neuroscience. Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1093-0.

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Sargent, Gabriel, Pierre Hanna, Henri Nicolas, and Frederic Bimbot. "Exploring the Complementarity of Audio-Visual Structural Regularities for the Classification of Videos into TV-Program Collections." In 2015 IEEE International Symposium on Multimedia (ISM). IEEE, 2015. http://dx.doi.org/10.1109/ism.2015.133.

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Buchsbaum, Gershon. "Optimal coding of spatiochromatic information in the retina." In OSA Annual Meeting. Optica Publishing Group, 1986. http://dx.doi.org/10.1364/oam.1986.tuf2.

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Natural visual stimuli are multidimensional signals encompassing parameters of space, time, and color. Information in these signal dimensions is redundant because natural images exhibit some spatial, temporal, and chromatic regularities. Further, these signal dimensions are not independent. The incoming visual stimulus is processed and coded in the retina by spatially organized center-surround chromatically antagonistic receptive fields with complex temporal characteristics. The basic hypothesis is that the purpose of retinal signal processing is to reduce signal redundancy and to efficiently

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Kersten, Daniel. "The ideal observer: from images to objects." In OSA Annual Meeting. Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.mc2.

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The human visual system is remarkably good at making accurate and reliable interpretations of the world from incomplete or noisy retinal image data. It does this by exploiting implicit knowledge of the statistical regularities of both objects and images. By quantifying the statistical uncertainty inherent in a visual task, ideal observer models specify a limit on the best performance for that task. Comparisons of human and ideal performance, often made by measures of efficiency, can be used to quantify information utilization. Historically, this technique has been developed most fully in the c

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