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Journal articles on the topic 'Visual guidance systems'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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1

Young, J. R. C. "Visual Approach Guidance Indicator Systems." Journal of Navigation 47, no. 1 (January 1994): 96–99. http://dx.doi.org/10.1017/s0373463300011164.

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2

Alexander, R., and G. Zelinsky. "Visual Similarity Predicts Categorical Search Guidance." Journal of Vision 10, no. 7 (August 17, 2010): 1316. http://dx.doi.org/10.1167/10.7.1316.

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3

Wolfe, Jeremy M., Ester Reijnen, Michael J. Van Wert, and Yoana Kuzmova. "In visual search, guidance by surface type is different than classic guidance." Vision Research 49, no. 7 (April 2009): 765–73. http://dx.doi.org/10.1016/j.visres.2009.02.007.

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4

Yang, H., and G. J. Zelinsky. "Evidence for guidance in categorical visual search." Journal of Vision 6, no. 6 (March 19, 2010): 449. http://dx.doi.org/10.1167/6.6.449.

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5

Eigentler, Karl. "Experiences with LED-based visual guidance systems in tunnels." Tunnelling and Underground Space Technology 21, no. 3-4 (May 2006): 325. http://dx.doi.org/10.1016/j.tust.2005.12.039.

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6

Fajen, B. R. "Perceptual learning and the visual guidance of braking." Journal of Vision 6, no. 6 (March 18, 2010): 139. http://dx.doi.org/10.1167/6.6.139.

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7

Palmer, E. M., M. J. Van Wert, T. S. Horowitz, and J. M. Wolfe. "Measuring the timecourse of guidance in visual search." Journal of Vision 6, no. 6 (March 19, 2010): 443. http://dx.doi.org/10.1167/6.6.443.

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8

Ogawa, H., and K. Watanabe. "Implicit learning of attentional guidance modulates visual preference." Journal of Vision 8, no. 6 (April 2, 2010): 870. http://dx.doi.org/10.1167/8.6.870.

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9

Cao, Chengyu, Naira Hovakimyan, and Johnny Evers. "Active control of visual sensor for navigation and guidance." Computational Management Science 7, no. 1 (June 7, 2008): 85–110. http://dx.doi.org/10.1007/s10287-008-0077-0.

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10

Clark, B. A. J., and P. Antonenko. "A history of visual approach guidance indicator systems in Australia." Journal of Navigation 46, no. 1 (January 1993): 49–62. http://dx.doi.org/10.1017/s0373463300011309.

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Approach and landing are the phases of flight most associated with accidents and near accidents, especially when external visual cues are sparse. At what is now Australia's DSTO Aeronautical Research Laboratory, research on ways of reducing the hazard incidence resulted in the development and testing of several ground-based visual approach slope indicators. The T-VASIS landing aid was an outcome of this work, T-VASIS, which has been in international use for over twenty years, is now under potential threat of displacement by PAPI which is a more recent UK development. The research which led to the implementation of T-VASIS is described here, along with subsequent events leading to the introduction of PAPI to Australia. A glossary of abbreviations is included at the end of the paper.
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11

Viollet, Stéphane, and Franck Ruffier. "Guest editorial: Visual guidance systems for small Unmanned Aerial Vehicles." Autonomous Robots 27, no. 3 (July 29, 2009): 145–46. http://dx.doi.org/10.1007/s10514-009-9128-9.

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12

List, Alexandra, Lucica Iordanescu, Marcia Grabowecky, and Satoru Suzuki. "Haptic guidance of overt visual attention." Attention, Perception, & Psychophysics 76, no. 8 (June 17, 2014): 2221–28. http://dx.doi.org/10.3758/s13414-014-0696-1.

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13

Motter, Brad C., and Eric J. Belky. "The guidance of eye movements during active visual search." Vision Research 38, no. 12 (June 1998): 1805–15. http://dx.doi.org/10.1016/s0042-6989(97)00349-0.

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14

Schmidt, Joseph, and Gregory J. Zelinsky. "Visual search guidance is best after a short delay." Vision Research 51, no. 6 (March 2011): 535–45. http://dx.doi.org/10.1016/j.visres.2011.01.013.

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15

Maravall, D., M. Mazo, V. Palencia, M. M. Pérez, and C. Torres. "GUIDANCE OF AN AUTONOMOUS VEHICLE BY VISUAL FEEDBACK." Cybernetics and Systems 21, no. 2-3 (March 1990): 257–66. http://dx.doi.org/10.1080/01969729008902239.

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16

Yang, H., and G. Zelinsky. "Visual memory or visual features coded verbally? An effect of working memory load on guidance during visual search." Journal of Vision 7, no. 9 (March 23, 2010): 686. http://dx.doi.org/10.1167/7.9.686.

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17

Melcher, David, and Eileen Kowler. "Visual scene memory and the guidance of saccadic eye movements." Vision Research 41, no. 25-26 (November 2001): 3597–611. http://dx.doi.org/10.1016/s0042-6989(01)00203-6.

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18

Chen, Xin, and Gregory J. Zelinsky. "Real-world visual search is dominated by top-down guidance." Vision Research 46, no. 24 (November 2006): 4118–33. http://dx.doi.org/10.1016/j.visres.2006.08.008.

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19

Oh, S. H., and M. S. Kim. "The guidance effect of working memory load on visual search." Journal of Vision 3, no. 9 (March 18, 2010): 629. http://dx.doi.org/10.1167/3.9.629.

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20

Eloka, O., and V. H. Franz. "Effects of object shape on the visual guidance of action." Journal of Vision 10, no. 7 (August 13, 2010): 1089. http://dx.doi.org/10.1167/10.7.1089.

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21

Diamond, J., and L. Tremblay. "Visual-haptic disparity of target will modulate action-guidance strategy." Journal of Vision 9, no. 8 (March 21, 2010): 1112. http://dx.doi.org/10.1167/9.8.1112.

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22

Schmidt, J., and G. Zelinsky. "Visual search guidance is best shortly after target preview offset." Journal of Vision 9, no. 8 (March 21, 2010): 1183. http://dx.doi.org/10.1167/9.8.1183.

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23

Tyrrell, Richard A., Kirsten K. Rudolph, Beth G. Eggers, and Herschel W. Leibowitz. "Evidence for the persistence of visual guidance information." Perception & Psychophysics 54, no. 4 (July 1993): 431–38. http://dx.doi.org/10.3758/bf03211765.

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24

Gielen, CCAM, and JAM van Gisbergen. "The Visual Guidance of Saccades and Fast Aiming Movements." Physiology 5, no. 2 (April 1, 1990): 58–63. http://dx.doi.org/10.1152/physiologyonline.1990.5.2.58.

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Sensory signals are used to create an internal representation of the environment. There is good evidence that the same internal representation of target position in space is shared by different motor systems. A central problem is how the command signal is transformed from a spatial code at the sensory side into a dynamic activation signal appropriately matched to the properties of the effector system.
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25

Ovcharenko, E. A., G. V. Savrasov, and K. U. Klyshnikov. "Visual and Robotic Guidance Systems for Transcatheter Implantation of Heart Value Prostheses." Biomedical Engineering 51, no. 1 (May 2017): 1–5. http://dx.doi.org/10.1007/s10527-017-9672-0.

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26

Bergmann, Nils, Dennis Koch, and Anna Schubö. "Reward expectation facilitates context learning and attentional guidance in visual search." Journal of Vision 19, no. 3 (March 27, 2019): 10. http://dx.doi.org/10.1167/19.3.10.

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27

Adolph, K. E., J. M. Franchak, D. Badaly, M. T. Smith, and J. S. Babcock. "Head-mounted eye-tracking with children: Visual guidance of motor action." Journal of Vision 8, no. 6 (March 19, 2010): 102. http://dx.doi.org/10.1167/8.6.102.

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28

Franchak, J. M., M. T. Smith, and K. E. Adolph. "Visual guidance of locomotion in infants, young adults, and the elderly." Journal of Vision 8, no. 6 (March 20, 2010): 1163. http://dx.doi.org/10.1167/8.6.1163.

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29

Medina, J., S. A. Jax, S. Prasad, and H. B. Coslett. "Role of visual guidance in reaching after right intraparietal sulcus resection." Journal of Vision 10, no. 7 (August 13, 2010): 1086. http://dx.doi.org/10.1167/10.7.1086.

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30

Michod, K. O., J. M. Wolfe, and T. S. Horowitz. "Does guidance take time to develop during a visual search trial?" Journal of Vision 4, no. 8 (August 1, 2004): 340. http://dx.doi.org/10.1167/4.8.340.

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31

Wolfe, Jeremy M., Randall S. Birnkrant, Melina A. Kunar, and Todd S. Horowitz. "Visual search for transparency and opacity: Attentional guidance by cue combination?" Journal of Vision 5, no. 3 (March 30, 2005): 9. http://dx.doi.org/10.1167/5.3.9.

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32

Terao, M., H. Ogawa, and A. Yagi. "The repetition of object identities modulates attentional guidance in visual search." Journal of Vision 5, no. 8 (March 16, 2010): 413. http://dx.doi.org/10.1167/5.8.413.

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33

Tan, C., T. Serre, S. Chikkerur, and T. Poggio. "Feature-based and contextual guidance mechanisms in complex natural visual search." Journal of Vision 9, no. 8 (March 21, 2010): 1189. http://dx.doi.org/10.1167/9.8.1189.

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34

Beitner, Julia, Jason Helbing, Dejan Draschkow, and Melissa Le-Hoa Vo. "Scene grammar guidance affects both visual search and incidental object memory." Journal of Vision 21, no. 9 (September 27, 2021): 2150. http://dx.doi.org/10.1167/jov.21.9.2150.

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35

Burnett, Gary, and Sue Joyner. "Route Guidance Systems: Getting it Right from the Driver's Perspective." Journal of Navigation 49, no. 2 (May 1996): 169–77. http://dx.doi.org/10.1017/s0373463300013266.

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This paper presents a brief overview of current human factors research into route guidance systems. A short description of different interface styles is given and a review of two road-based studies of three existing route guidance systems is provided. A general discussion then highlights a range of issues of relevance to driver-system interfaces, drawing on results both from the studies carried out, and other research. Specific aspects addressed include the content and means of presenting route guidance information, and the potential effect of interface design on driver visual behaviour and performance.
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36

Chen, Zhe, and Brian H. Tsou. "Task-based working memory guidance of visual attention." Attention, Perception, & Psychophysics 73, no. 4 (January 20, 2011): 1082–95. http://dx.doi.org/10.3758/s13414-010-0087-1.

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37

Krupa, Alexandre, and François Chaumette. "Guidance of an ultrasound probe by visual servoing." Advanced Robotics 20, no. 11 (January 2006): 1203–18. http://dx.doi.org/10.1163/156855306778792443.

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38

Seelinger, Michael, and John-David Yoder. "Automatic visual guidance of a forklift engaging a pallet." Robotics and Autonomous Systems 54, no. 12 (December 2006): 1026–38. http://dx.doi.org/10.1016/j.robot.2005.10.009.

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39

Fajen, Brett R., and William H. Warren. "Visual Guidance of Intercepting a Moving Target on Foot." Perception 33, no. 6 (June 2004): 689–715. http://dx.doi.org/10.1068/p5236.

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40

Talati, Ardesheer, Francisco J. Valero-Cuevas, and Joy Hirsch. "Visual and Tactile Guidance of Dexterous Manipulation Tasks: An fMRI Study." Perceptual and Motor Skills 101, no. 1 (August 2005): 317–34. http://dx.doi.org/10.2466/pms.101.1.317-334.

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Models of motor guidance that dynamically adjust to the availability and quality of sensory information are based on the observation that dexterous tasks are routinely performed using various combinations of visual and tactile inputs. However, a dynamic neural system that acquires and processes relevant visual and tactile information remains relatively uncharacterized in humans. In this study, whole-brain functional magnetic resonance images were acquired during a dexterous manipulation task, compression of the end caps of a slender spring prone to buckling, to investigate the neural systems associated with motor guidance under four visual and tactile guidance conditions: (1) eyes closed (no visual input), smooth end caps, (2) eyes closed, rough end caps, (3) eyes open and watching hand, smooth end caps, and (4) eyes open and watching hand, rough end caps. Performance of the dexterous task remained constant in all conditions. Variations in the two levels of visual input resulted in modulation of activity in the middle and inferior occipital gyrii and inferior parietal lobule, and variation in the two levels of tactile input during the task resulted in modulation of activity in the precentral (primary motor) gyrus. Although significantly active in all conditions, cingulate gyrus, medial frontal gyrus, postcentral gyrus, and cerebellum activities were not modulated by levels of either visual or somatosensory input, and no interaction effects were observed. Together, these data indicate that a fine-tuned motor task guided by varying visual and tactile information engages a distributed and integrated neural complex consisting of control and executive functions and regions that process dynamic sensory information related to guidance functions.
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41

Schmidt, J., and G. Zelinsky. "Visual search guidance increases with a delay between target cue and search." Journal of Vision 8, no. 6 (March 26, 2010): 317. http://dx.doi.org/10.1167/8.6.317.

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42

Beck, V., and S. Luck. "Guidance of Attention During Visual Search: Can Multiple Attentional Templates Operate Concurrently?" Journal of Vision 10, no. 7 (August 17, 2010): 1291. http://dx.doi.org/10.1167/10.7.1291.

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43

Peterson, M. S., M. R. Beck, and M. Vomela. "The guidance of attention by retrospective and prospective memory during visual search." Journal of Vision 4, no. 8 (August 1, 2004): 168. http://dx.doi.org/10.1167/4.8.168.

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44

Passingham, Richard E., and Ivan Toni. "Contrasting the Dorsal and Ventral Visual Systems: Guidance of Movement versus Decision Making." NeuroImage 14, no. 1 (July 2001): S125—S131. http://dx.doi.org/10.1006/nimg.2001.0836.

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45

Wolfe, Jeremy M., Matthew S. Cain, and Avigael M. Aizenman. "Guidance and selection history in hybrid foraging visual search." Attention, Perception, & Psychophysics 81, no. 3 (January 2, 2019): 637–53. http://dx.doi.org/10.3758/s13414-018-01649-5.

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46

Wang, DeLiang, Arni Kristjansson, and Ken Nakayama. "Efficient visual search without top-down or bottom-up guidance." Perception & Psychophysics 67, no. 2 (February 2005): 239–53. http://dx.doi.org/10.3758/bf03206488.

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47

TALATI, ARDESHEER. "VISUAL AND TACTILE GUIDANCE OF DEXTEROUS MANIPULATION TASKS: AN fMRI STUDY." Perceptual and Motor Skills 101, no. 5 (2005): 317. http://dx.doi.org/10.2466/pms.101.5.317-334.

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48

Shen, Kelly, Gleb Bezgin, Rajajee Selvam, Anthony R. McIntosh, and Jennifer D. Ryan. "An Anatomical Interface between Memory and Oculomotor Systems." Journal of Cognitive Neuroscience 28, no. 11 (November 2016): 1772–83. http://dx.doi.org/10.1162/jocn_a_01007.

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Visual behavior is guided by memories from prior experience and knowledge of the visual scene. The hippocampal system (HC), in particular, has been implicated in the guidance of saccades: Amnesic patients, following damage to the HC, exhibit selective deficits in their gaze patterns. However, the neural circuitry by which mnemonic representations influence the oculomotor system remains unknown. We used a data-driven, network-based approach on directed anatomical connectivity from the macaque brain to reveal an extensive set of polysnaptic pathways spanning the extrastriate, posterior parietal and prefrontal cortices that potentially mediate the exchange of information between the memory and visuo-oculomotor systems. We additionally show how the potential for directed information flow from the hippocampus to oculomotor control areas is exceptionally high. In particular, the dorsolateral pFC and FEF—regions known to be responsible for the cognitive control of saccades—are topologically well positioned to receive information from the hippocampus. Together with neuropsychological evidence of altered gaze patterns following damage to the hippocampus, our findings suggest that a reconsideration of hippocampal involvement in oculomotor guidance is needed.
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49

Bazeille, Stephane, Emmanuel Battesti, and David Filliat. "A Light Visual Mapping and Navigation Framework for Low-Cost Robots." Journal of Intelligent Systems 24, no. 4 (December 1, 2015): 505–24. http://dx.doi.org/10.1515/jisys-2014-0116.

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AbstractWe address the problems of localization, mapping, and guidance for robots with limited computational resources by combining vision with the metrical information given by the robot odometry. We propose in this article a novel light and robust topometric simultaneous localization and mapping framework using appearance-based visual loop-closure detection enhanced with the odometry. The main advantage of this combination is that the odometry makes the loop-closure detection more accurate and reactive, while the loop-closure detection enables the long-term use of odometry for guidance by correcting the drift. The guidance approach is based on qualitative localization using vision and odometry, and is robust to visual sensor occlusions or changes in the scene. The resulting framework is incremental, real-time, and based on cheap sensors provided on many robots (a camera and odometry encoders). This approach is, moreover, particularly well suited for low-power robots as it is not dependent on the image processing frequency and latency, and thus it can be applied using remote processing. The algorithm has been validated on a Pioneer P3DX mobile robot in indoor environments, and its robustness is demonstrated experimentally for a large range of odometry noise levels.
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50

Edelman, Jay A., Nelson Valenzuela, and Jason J. S. Barton. "Antisaccade velocity, but not latency, results from a lack of saccade visual guidance." Vision Research 46, no. 8-9 (April 2006): 1411–21. http://dx.doi.org/10.1016/j.visres.2005.09.013.

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