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Journal articles on the topic 'Human visual system'

To see the other types of publications on this topic, follow the link: Human visual system.

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Dong, Rui, and Dao Shun Wang. "A Visual Hiding Algorithm Based on Human Visual System." Advanced Materials Research 989-994 (July 2014): 2393–97. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2393.

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Image information hiding is a way of concealing secret information within another image or file. The previous researches of image information hiding extract secret by calculating. An algorithm based on HVS (Human Visual System) is proposed in this paper. We first decide the embedding region and embedding strength of secret image based on edge and texture information. Then, a pair of invert images is chosen as the cover images, and the stego images are constructed by modifying the pixels of the cover images through the proposed criterion. Correspondingly, the secret image can be recovered through overlaying the two stego images.

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2

Zhao, Bo, Hong Wei Zhao, Ping Ping Liu, and Gui He Qin. "A New Mobile Visual Search System Based on the Human Visual System." Applied Mechanics and Materials 461 (November 2013): 792–800. http://dx.doi.org/10.4028/www.scientific.net/amm.461.792.

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We describe a novel mobile visual search system based on the saliencymechanism and sparse coding principle of the human visual system (HVS). In the featureextraction step, we first divide an image into different regions using thesaliency extraction algorithm. Then scale-invariant feature transform (SIFT)descriptors in all regions are extracted while regional identities arepreserved based on their various saliency levels. According to the sparsecoding principle in the HVS, we adopt a local neighbor preserving Hash functionto establish the binary sparse expression of the SIFT features. In the searchingstep, the nearest neighbors matched to the hashing codes are processed accordingto different saliency levels. Matching scores of images in the database arederived from the matching of hashing codes. Subsequently, the matching scoresof all levels are weighed by degrees of saliency to obtain the initial set of results. In order to further ensure matching accuracy, we propose an optimized retrieval scheme based on global texture information. We conduct extensive experiments on an actual mobile platform in large-scale datasets by using Corel-1000. The resultsshow that the proposed method outperforms the state-of-the-art algorithms on accuracyrate, and no significant increase in the running time of the feature extractionand retrieval can be observed.

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3

Artal, Pablo, Ann E. Elsner, and Marilyn Schneck. "Aging of the Human Visual System." Journal of the Optical Society of America A 19, no. 1 (January 1, 2002): 134. http://dx.doi.org/10.1364/josaa.19.000134.

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4

Bocci, T., N. Francini, M. Caleo, S. Tognazzi, L. Maffei, S. Rossi, A. Priori, and F. Sartucci. "Homeostatic plasticity and human visual system." Journal of the Neurological Sciences 333 (October 2013): e584. http://dx.doi.org/10.1016/j.jns.2013.07.2040.

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5

Banks, Martin S., Jenny C. A. Read, Robert S. Allison, and Simon J. Watt. "Stereoscopy and the Human Visual System." SMPTE Motion Imaging Journal 121, no. 4 (May 2012): 24–43. http://dx.doi.org/10.5594/j18173.

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6

Mashtakov, A. P. "Sub-Riemannian Geometry in Image Processing and Modeling of the Human Visual System." Nelineinaya Dinamika 15, no. 4 (2019): 561–68. http://dx.doi.org/10.20537/nd190415.

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7

Lyapunov, S. I. "Visual acuity and contrast sensitivity of the human visual system." Journal of Optical Technology 84, no. 9 (September 1, 2017): 613. http://dx.doi.org/10.1364/jot.84.000613.

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8

Orban, G. A., P. Dupont, R. Vogels, B. De Bruyn, G. Bormans, and L. Mortelmans. "Task dependency of visual processing in the human visual system." Behavioural Brain Research 76, no. 1-2 (April 1996): 215–23. http://dx.doi.org/10.1016/0166-4328(95)00195-6.

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9

Idesawa, Masanori. "Information Processing in Visual System - Optical Illusion and Visual Mechanism." Journal of Robotics and Mechatronics 13, no. 6 (December 20, 2001): 569–74. http://dx.doi.org/10.20965/jrm.2001.p0569.

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Human beings obtain big amount of information from the external world through their visual system. Automated system such as robot must provide the visual functions for their flexible operations in 3-D circumstances. In order to realize the visual function artificially, we would be better to learn from the human visual mechanism. Optical illusions would be a pure reflection of the human visual mechanism; they can be used for investigating human visual mechanism. New types of optical illusion with binocular viewing are introduced and investigated.

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10

Jenkins, Bill. "Orientational anisotropy in the human visual system." Perception & Psychophysics 37, no. 2 (March 1985): 125–34. http://dx.doi.org/10.3758/bf03202846.

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11

Gulina, Y. S., V. Ya Koliuchkin, and N. E. Trofimov. "Mathematical Model of the Human Visual System." Optical Memory and Neural Networks 27, no. 4 (October 2018): 219–34. http://dx.doi.org/10.3103/s1060992x1804001x.

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12

Grigorian, Anahit, Larissa McKetton, and Keith Schneider. "Abnormal Visual System Connectivity in Human Albinism." Journal of Vision 16, no. 12 (September 1, 2016): 772. http://dx.doi.org/10.1167/16.12.772.

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13

Lee, Yun Gu. "Video stabilization based on human visual system." Journal of Electronic Imaging 23, no. 05 (September 19, 2014): 1. http://dx.doi.org/10.1117/1.jei.23.5.053009.

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14

Rudd, Michael E. "Lightness computation by the human visual system." Journal of Electronic Imaging 26, no. 3 (June 27, 2017): 031209. http://dx.doi.org/10.1117/1.jei.26.3.031209.

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15

Watson, John D. G. "Functional imaging of the human visual system." Seminars in Neuroscience 7, no. 3 (June 1995): 149–56. http://dx.doi.org/10.1006/smns.1995.0017.

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16

Watamaniuk, Scott N. J., and Andrew Duchon. "The human visual system averages speed information." Vision Research 32, no. 5 (May 1992): 931–41. http://dx.doi.org/10.1016/0042-6989(92)90036-i.

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17

Hammett, S. T., and A. T. Smith. "Temporal beats in the human visual system." Vision Research 34, no. 21 (November 1994): 2833–40. http://dx.doi.org/10.1016/0042-6989(94)90052-3.

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18

Vanston, John E., and Lars Strother. "Sex differences in the human visual system." Journal of Neuroscience Research 95, no. 1-2 (November 7, 2016): 617–25. http://dx.doi.org/10.1002/jnr.23895.

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19

Naselaris, Thomas. "Why is the human visual system generative?" Journal of Vision 23, no. 9 (August 1, 2023): 4664. http://dx.doi.org/10.1167/jov.23.9.4664.

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20

Ke, Ziyi, Ziqiang Chen, Huanlei Wang, and Liang Yin. "A Visual Human-Computer Interaction System Based on Hybrid Visual Model." Security and Communication Networks 2022 (June 30, 2022): 1–13. http://dx.doi.org/10.1155/2022/9562104.

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The traditional human-computer interaction is mainly through the mouse, keyboard, remote control, and other peripheral equipment electromagnetic signal transmission. This paper aims to build a visual human-computer interaction system through a series of deep learning and machine vision models, so that people can achieve complete human-computer interaction only through the camera and screen. The established visual human-computer interaction system mainly includes the function modes of three basic peripherals in human-computer interaction: keyboard, mouse (X-Y position indicator), and remote control. The convex hull method was used to switch between these three modes. After issuing the mode command, Gaussian mixture was used to quickly identify the moving human body to narrow the scope of our image processing. Subsequently, finger detection in human body was realized based on the Faster-RCNN-ResNET50-FPN model structure, and realized the function of moving mouse and keyboard through the relationship between different fingers. At the same time, the recognition of human body posture was done by using MediaPipe BlazePose, and the action classification models were established through the Angle between body movements so as to realize the control function of remote control. In order to ensure the real-time performance of the interactive system, according to the characteristics of different data processing processes, CPU and GPU computing power resources are used to cross-process images to ensure the real-time performance. The recognition accuracy of the human-computer interaction system is above 0.9 for the key feature points of human body, and above 0.87 for the recognition accuracy of four kinds of command actions. It is hoped that vision-based human-computer interaction will become a widely used interaction mode in the future.

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21

Zheng, Bo, Xiao-Dong Wang, Jing-Tao Huang, Jian Wang, and Yang Jiang. "Selective visual attention based clutter metric with human visual system adaptability." Applied Optics 55, no. 27 (September 20, 2016): 7700. http://dx.doi.org/10.1364/ao.55.007700.

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22

Hagura, Nobuhiro, Hiroshi Ban, Ganesh Gowrishankar, Hiroki Yamamoto, and Masahiko Haruno. "Visual representation of hand in human dorsal visual system-fMRI study." Neuroscience Research 65 (January 2009): S103. http://dx.doi.org/10.1016/j.neures.2009.09.456.

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23

Hu, Zongqi, Andrew U. Meyer, Vance Zemon, and Edward J. Haupt. "Modeling of Human Visual System Dynamics: A Visual Evoked Potential Study." IFAC Proceedings Volumes 27, no. 1 (March 1994): 533–34. http://dx.doi.org/10.1016/s1474-6670(17)46328-2.

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24

Lucero, Ché, Geoffrey Brookshire, Clara Sava-Segal, Roberto Bottini, Susan Goldin-Meadow, Edward K. Vogel, and Daniel Casasanto. "Unconscious Number Discrimination in the Human Visual System." Cerebral Cortex 30, no. 11 (June 12, 2020): 5821–29. http://dx.doi.org/10.1093/cercor/bhaa155.

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Abstract How do humans compute approximate number? According to one influential theory, approximate number representations arise in the intraparietal sulcus and are amodal, meaning that they arise independent of any sensory modality. Alternatively, approximate number may be computed initially within sensory systems. Here we tested for sensitivity to approximate number in the visual system using steady state visual evoked potentials. We recorded electroencephalography from humans while they viewed dotclouds presented at 30 Hz, which alternated in numerosity (ranging from 10 to 20 dots) at 15 Hz. At this rate, each dotcloud backward masked the previous dotcloud, disrupting top-down feedback to visual cortex and preventing conscious awareness of the dotclouds’ numerosities. Spectral amplitude at 15 Hz measured over the occipital lobe (Oz) correlated positively with the numerical ratio of the stimuli, even when nonnumerical stimulus attributes were controlled, indicating that subjects’ visual systems were differentiating dotclouds on the basis of their numerical ratios. Crucially, subjects were unable to discriminate the numerosities of the dotclouds consciously, indicating the backward masking of the stimuli disrupted reentrant feedback to visual cortex. Approximate number appears to be computed within the visual system, independently of higher-order areas, such as the intraparietal sulcus.

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25

Kochina, Marina Leonidovna, O. V. Yavorsky, and N. M. Maslova. "CONCEPT OF INFORMATION PROCESSES ORGANIZATION IN HUMAN VISUAL SYSTEM." International Medical Journal, no. 1 (March 5, 2020): 56–60. http://dx.doi.org/10.37436/2308-5274-2020-1-12.

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Recent studies have shown that both traditional and modern (electronic) visual media significantly affect the processes occurring in the visual system of children and adolescents, iin the development of which the concept of controlled and uncontrolled elements is used. When studying the adaptation of the visual system to a visual load, ensuring the uptake, transmission and processing of visual information, its structural and functional organization is taken into account. The proposed summarized scheme of information processes in the visual system takes into account and significantly supplements the main provisions of the object−oriented model of selective visual attention, based on current methods of intelligent data processing. The results of the study indicate the complexity of the visual information transformation path from a visual stimulus to the creation and awareness of the image, occurring in the higher parts of the brain. The whole apparatus of encoding, transmitting, processing and perceiving visual information is useless if the guidance and focusing unit does not provide a clear and undistorted image of the objects of the outside world on the retina. In case of any malfunctions of the first unit or in the presence of defects in the visual system, the processes of finding compensation for this condition and modes of operation are started, which allows to obtain the most complete and high−quality perception of visual objects. These processes can lead to the formation of a visual system with sufficiently high visual functions or monocular one, if the compensation of existing problems will have a high "price". With the help of the proposed concept of organization of information processes in the visual system it is possible to assess the role of each of its considered blocks not only in the perception of visual information, but also the formation of this system in children and adolescents. Key words: visual system, information processes, visual information.

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26

Pearce, Eiluned, and Robin Dunbar. "Latitudinal variation in light levels drives human visual system size." Biology Letters 8, no. 1 (July 27, 2011): 90–93. http://dx.doi.org/10.1098/rsbl.2011.0570.

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Ambient light levels influence visual system size in birds and primates. Here, we argue that the same is true for humans. Light levels, in terms of both the amount of light hitting the Earth's surface and day length, decrease with increasing latitude. We demonstrate a significant positive relationship between absolute latitude and human orbital volume, an index of eyeball size. Owing to tight scaling between visual system components, this will translate into enlarged visual cortices at higher latitudes. We also show that visual acuity measured under full-daylight conditions is constant across latitudes, indicating that selection for larger visual systems has mitigated the effect of reduced ambient light levels. This provides, to our knowledge, the first support that light levels drive intraspecific variation in visual system size in the human population.

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27

Rikani, Azadeh A., Zia Choudhry, Adnan Maqsood Choudhry, Nasir Rizvi, and Huma Ikram. "Spatial information processing by the human visual system." El Mednifico Journal 2, no. 3 (July 25, 2014): 309. http://dx.doi.org/10.18035/emj.v2i3.156.

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28

Buch, John, and Billy Hammond. "Photobiomodulation of the Visual System and Human Health." International Journal of Molecular Sciences 21, no. 21 (October 28, 2020): 8020. http://dx.doi.org/10.3390/ijms21218020.

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Humans express an expansive and detailed response to wavelength differences within the electromagnetic (EM) spectrum. This is most clearly manifest, and most studied, with respect to a relatively small range of electromagnetic radiation that includes the visible wavelengths with abutting ultraviolet and infrared, and mostly with respect to the visual system. Many aspects of our biology, however, respond to wavelength differences over a wide range of the EM spectrum. Further, humans are now exposed to a variety of modern lighting situations that has, effectively, increased our exposure to wavelengths that were once likely minimal (e.g., “blue” light from devices at night). This paper reviews some of those biological effects with a focus on visual function and to a lesser extent, other body systems.

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29

Panetta, Karen, Chen Gao, and Sos Agaian. "Human-Visual-System-Inspired Underwater Image Quality Measures." IEEE Journal of Oceanic Engineering 41, no. 3 (July 2016): 541–51. http://dx.doi.org/10.1109/joe.2015.2469915.

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30

Li, Jingqiang. "Autofocus searching algorithm considering human visual system limitations." Optical Engineering 44, no. 11 (November 1, 2005): 113201. http://dx.doi.org/10.1117/1.2130725.

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31

Park, Sang-Hee. "Spatial deblocking algorithm based on human visual system." Optical Engineering 48, no. 4 (April 1, 2009): 047006. http://dx.doi.org/10.1117/1.3122005.

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32

Thorpe, Simon, Denis Fize, and Catherine Marlot. "Speed of processing in the human visual system." Nature 381, no. 6582 (June 1996): 520–22. http://dx.doi.org/10.1038/381520a0.

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33

Gao, Shaoshu, Yanjiang Wang, Weiqi Jin, and Xiaodong Zhang. "Perceptual sharpness metric based on human visual system." Electronics Letters 50, no. 23 (November 2014): 1695–97. http://dx.doi.org/10.1049/el.2014.2844.

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34

Jenkins, Bill. "Spatiotemporal Isosensitivity Fields in the Human Visual System." Perception 15, no. 4 (August 1986): 467–72. http://dx.doi.org/10.1068/p150467.

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The human visual system is capable of detecting correlations, manifested perceptually as global pattern, in mathematically constrained dynamic textures. This ability has given rise to speculation that correlative mechanisms in the human visual system exist and that they have a neural basis similar to the orientationally selective structures discovered in area 17 of the mammalian visual cortex. The limits to the detection of correlation were mapped, spatially and temporally, by means of a psychophysical technique. Evidence is presented that, at least in the spatial domain, the correlation mechanism may be served by a population of such neural units.

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35

Petit, Laurent, Nathalie Tzourio, Christophe Orssaud, Uwe Pietrzyk, Alain Berthoz, and Bernard Mazoyer. "Functional Neuroanatomy of the Human Visual Fixation System." European Journal of Neuroscience 7, no. 1 (January 1995): 169–74. http://dx.doi.org/10.1111/j.1460-9568.1995.tb01031.x.

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36

Adini, Yael, Dov Sagi, and Misha Tsodyks. "Context-enabled learning in the human visual system." Nature 415, no. 6873 (February 2002): 790–93. http://dx.doi.org/10.1038/415790a.

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37

Purushothaman, Gopathy, Haluk Öğmen, and Harold E. Bedell. "Suprathreshold Intrinsic Dynamics of the Human Visual System." Neural Computation 15, no. 12 (December 1, 2003): 2883–908. http://dx.doi.org/10.1162/089976603322518786.

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Intrinsic high-frequency neural activities have been observed in the visual system of several species, but their functional significance for visual perception remains a fundamental puzzle in cognitive neuroscience. Spatiotemporal integration in the human visual system acts as a low-pass filter and makes the psychophysical observation of high-frequency activities very difficult. A computational model of retino-cortical dynamics (RECOD) is used to derive experimental paradigms that allow psychophysical studies of high-frequency neural activities. A reduced-parameter version of the model is used to quantitatively relate psychophysical data collected in two of these experimental paradigms. Statistical analysis shows that the model's account of the variance in the data is, in general, highly significant. We suggest that psychophysically measured oscillations reflect intrinsic neuronal oscillations observed in the visual cortex.

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38

Bowskill, Jerry, and John Downie. "Extending the capabilities of the human visual system." ACM SIGGRAPH Computer Graphics 29, no. 2 (May 1995): 61–65. http://dx.doi.org/10.1145/204362.204378.

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39

Thorpe, S., D. Fize, and C. Marlot. "Speed of processing in the human visual system." American Journal of Ophthalmology 122, no. 4 (October 1996): 608–9. http://dx.doi.org/10.1016/s0002-9394(14)72148-8.

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40

Cormack, Lawrence K., Scott B. Stevenson, and Clifton M. Schor. "Disparity-tuned channels of the human visual system." Visual Neuroscience 10, no. 4 (July 1993): 585–96. http://dx.doi.org/10.1017/s0952523800005290.

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AbstractTraditionally, it has been thought that the processing of binocular disparity for the perception of stereoscopic depth is accomplished via three types of disparity-selective channels – “near,” “far,” and “tuned.” More recent evidence challenges this notion. We have derived disparity-tuning functions psychophysically using a subthreshold summation (i.e. low-level masking) technique. We measured correlation-detection thresholds for dynamic random-element stereograms containing either one or two surfaces in depth. The resulting disparity-tuning functions show an opponent-type profile, indicating the presence of inhibition between disparity-tuned units in the visual system. Moreover, there is clear inhibition between disparities of the same sign, obviating a strict adherence to near-far opponency. These results compare favorably with tuning functions derived psychophysically using an adaptation technique, and with the tuning profiles from published single-unit recordings. Our results suggests a continuum of overlapping disparity-tuned channels, which is consistent with recent physiological evidence as well as models based on other psychophysical data.

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41

Kennedy, Lesa M., and Mitra Basu. "Image enhancement using a human visual system model." Pattern Recognition 30, no. 12 (December 1997): 2001–14. http://dx.doi.org/10.1016/s0031-3203(97)00014-9.

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42

Peng, Renbin, and Pramod K. Varshney. "A human visual system-driven image segmentation algorithm." Journal of Visual Communication and Image Representation 26 (January 2015): 66–79. http://dx.doi.org/10.1016/j.jvcir.2014.11.002.

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43

Zanker, J. M., and J. P. Harris. "On temporal hyperacuity in the human visual system." Vision Research 42, no. 22 (October 2002): 2499–508. http://dx.doi.org/10.1016/s0042-6989(02)00301-2.

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44

Ungerleider, L. G., S. M. Courtney, and J. V. Haxby. "A neural system for human visual working memory." Proceedings of the National Academy of Sciences 95, no. 3 (February 3, 1998): 883–90. http://dx.doi.org/10.1073/pnas.95.3.883.

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45

Dilks, D. D., C. I. Baker, Y. Liu, and N. Kanwisher. "Rapid reorganization in the adult human visual system." Journal of Vision 8, no. 6 (March 29, 2010): 476. http://dx.doi.org/10.1167/8.6.476.

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46

Qi, Huiyan, Dong Zheng, and Jiying Zhao. "Human visual system based adaptive digital image watermarking." Signal Processing 88, no. 1 (January 2008): 174–88. http://dx.doi.org/10.1016/j.sigpro.2007.07.020.

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47

Kumar, Sanoj, Sanjeev Kumar, Nagarajan Sukavanam, and Balasubramanian Raman. "Human visual system and segment-based disparity estimation." AEU - International Journal of Electronics and Communications 67, no. 5 (May 2013): 372–81. http://dx.doi.org/10.1016/j.aeue.2012.10.007.

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48

Xiao, Jiang, and Chengke Wu. "JPEG2000 compression coding using human visual system model." Journal of Electronics (China) 22, no. 1 (January 2005): 53–58. http://dx.doi.org/10.1007/bf02687951.

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49

IMADA, TOSHIAKI, and EIJI YODOGAWA. "Feature extraction processing time in human visual system." Japanese Psychological Research 27, no. 1 (1985): 11–20. http://dx.doi.org/10.4992/psycholres1954.27.11.

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50

Alley, D., S. Susstrunk, and J. Herault. "Linear demosaicing inspired by the human visual system." IEEE Transactions on Image Processing 14, no. 4 (April 2005): 439–49. http://dx.doi.org/10.1109/tip.2004.841200.

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