Visual encoding of partial unknown shape boundaries

Hannah Nordberg; Michael J Hautus; Ernest Greene; Hannah Nordberg; Michael J Hautus; Ernest Greene

doi:10.3934/Neuroscience.2018.2.132

AIMS Neuroscience

2018, Volume 5, Issue 2: 132-147. doi: 10.3934/Neuroscience.2018.2.132

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Research article

Visual encoding of partial unknown shape boundaries

1.
Department of Psychology, University of Southern California, Los Angeles, California USA
2.
The School of Psychology, University of Auckland, Auckland New Zealand, California USA

Received: 03 April 2018 Accepted: 10 May 2018 Published: 16 May 2018

Prior research has found that known shapes and letters can be recognized from a sparse sampling of dots that mark locations on their boundaries. Further, unknown shapes that are displayed only once can be identified by a matching protocol, and here also, above-chance performance requires very few boundary markers. The present work examines whether partial boundaries can be identified under similar low-information conditions. Several experiments were conducted that used a match-recognition task, with initial display of a target shape followed quickly by a comparison shape. The comparison shape was either derived from the target shape or was based on a different shape, and the respondent was asked for a matching judgment, i.e., did it “match” the target shape. Stimulus treatments included establishing how density affected the probability of a correct decision, followed by assessment of how much positioning of boundary dots affected this probability. Results indicate that correct judgments were possible when partial boundaries were displayed with a sparse sampling of dots. We argue for a process that quickly registers the locations of boundary markers and distills that information into a shape summary that can be used to identify the shape even when only a portion of the boundary is represented.
- shape recognition,
- shape encoding,
- boundary marking
Citation: Hannah Nordberg, Michael J Hautus, Ernest Greene. Visual encoding of partial unknown shape boundaries[J]. AIMS Neuroscience, 2018, 5(2): 132-147. doi: 10.3934/Neuroscience.2018.2.132

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Abstract

Prior research has found that known shapes and letters can be recognized from a sparse sampling of dots that mark locations on their boundaries. Further, unknown shapes that are displayed only once can be identified by a matching protocol, and here also, above-chance performance requires very few boundary markers. The present work examines whether partial boundaries can be identified under similar low-information conditions. Several experiments were conducted that used a match-recognition task, with initial display of a target shape followed quickly by a comparison shape. The comparison shape was either derived from the target shape or was based on a different shape, and the respondent was asked for a matching judgment, i.e., did it “match” the target shape. Stimulus treatments included establishing how density affected the probability of a correct decision, followed by assessment of how much positioning of boundary dots affected this probability. Results indicate that correct judgments were possible when partial boundaries were displayed with a sparse sampling of dots. We argue for a process that quickly registers the locations of boundary markers and distills that information into a shape summary that can be used to identify the shape even when only a portion of the boundary is represented.

References

[1]	Fuchs W (1938) Untersuchung uber dae Sehen der Hemianopiker und Hemiamblyopiker [English title: Completion phenomena in hemianopic vision], In: Ellis W.D. Translator, A Source Book of Gestalt Psychology, London: Routledge & Kegan Paul, 352.
[2]	Rosin P, Pantović J, Žunić J (2016) Measuring linearity of curves in 2D and 3D. Pattern Recognit 49: 65–78. doi: 10.1016/j.patcog.2015.07.011
[3]	Jomma HD, Hussein AI (2016) Circle views signature: A novel shape representation for shape recognition and retrieval. Can J Electr Comput Eng 39: 274–282. doi: 10.1109/CJECE.2016.2574745
[4]	Žunić J, Žunić D (2016) Shape interpretation of second-order moment invariants. J Math Imaging Vis 56: 125–136. doi: 10.1007/s10851-016-0638-8
[5]	Sharma S, Dubey S, Singh S, et al. (2015) Identity verification using shape and geometry of human hands. Expert Syst Appl Int J 42: 821–832. doi: 10.1016/j.eswa.2014.08.052
[6]	Tang K, Song P, Chen X (2017) 3D object recognition in cluttered scenes with robust shape description and correspondence selection. IEEE Access 5: 1833–1845. doi: 10.1109/ACCESS.2017.2658681
[7]	Sidram M, Bhajantri N (2015) An exploration with novel shape signature of GMSC distance function to track the object. Int J Imag Graph 15: 1550014. doi: 10.1142/S021946781550014X
[8]	Proenca H, Neves J, Barra S, et al. (2016) Joint head pose/soft label estimation for human recognition in-the-wild. IEEE Trans Pattern Anal Mach Intell 38: 2444–2456. doi: 10.1109/TPAMI.2016.2522441
[9]	Tsai CY, Liao HC, Feng YC (2016) A novel translation, rotation, and scale-invariant shape description method for real-time speed-limit sign recognition. Int Conf Adv Mater Sci Eng 2017: 486–488.
[10]	Greene E (2007) Retinal encoding of ultrabrief shape recognition cues. PloS One 2: e871. doi: 10.1371/journal.pone.0000871
[11]	Greene E (2016) How do we know whether three dots form an equilateral triangle? JSM Brain Sci 1: 1002.
[12]	Greene E (2016) Retinal encoding of shape boundaries. JSM Anat Physiol 1: 1002.
[13]	Greene E, Hautus MJ (2017) Demonstrating invariant encoding of shapes using a similarity judgment protocol. AIMS Neurosci 4: 120–146. doi: 10.3934/Neuroscience.2017.3.120
[14]	Green DM, Swets JA (1966) Signal detection theory and psychophysics. New York: Wiley Press, 25: 1478–1481.
[15]	Hautus MJ (1995) Corrections for extreme proportions and their biasing effects on estimated values of d′. Behav Res Methods Instrum Comput 27: 46–51. doi: 10.3758/BF03203619
[16]	Miller J (1996) The sampling distribution of d'. Percept Psychophys 58: 65–72. doi: 10.3758/BF03205476
[17]	Hautus M (1997) Calculating estimates of sensitivity from group data: Pooled versus averaged estimators. Behav Res Methods Instrum Comput 29: 556–562. doi: 10.3758/BF03210608
[18]	Macmillan NA, Creelman CD (2005) Detection Theory: A User's Guide, 2 Eds., New Jersey: Lawrence Erlbaum.
[19]	Hautus MJ, Hout DV, Lee HS (2009) Variants of a not-A and 2AFC tests: Signal detection theory models. Food Qual Prefer 20: 222–229. doi: 10.1016/j.foodqual.2008.10.002
[20]	Hautus J (2012) SDT Assistant (version 1.0) [Software]. Available from: http://hautus.org.
[21]	Greene E (2007) Recognition of objects displayed with incomplete sets of discrete boundary dots. Percept Mot Skills 104: 1043–1059. doi: 10.2466/pms.104.4.1043-1059
[22]	Sceniak MP, Hawken JJ, Shapley R (2001) Visual spatial characterization of macaque V1 neurons. J Neurophysiol 85: 1873–1887. doi: 10.1152/jn.2001.85.5.1873
[23]	Greene E (2008) Additional evidence that contour attributes are not essential cues for object recognition. Behav Brain Funct 4: e26. doi: 10.1186/1744-9081-4-26
[24]	Greene E, Ogden R (2012) Evaluating the contribution of shape attributes to recognition using the minimal transient discrete cue protocol. Behav Brain Funct 8: e53. doi: 10.1186/1744-9081-8-53
[25]	Fukushima K (1980) Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36: 193–202. doi: 10.1007/BF00344251
[26]	Rolls E, Cowey A, Bruce V (1992) Neurophysiological mechanisms underlying face processing within and beyond the temporal cortical visual areas. Phil Trans R Soc B 335: 11–21. doi: 10.1098/rstb.1992.0002
[27]	Wallis G, Rolls E (1997) Invariant face and object recognition in the visual system. Prog Neurobiol 51: 167–194. doi: 10.1016/S0301-0082(96)00054-8
[28]	Riesenhuber M, Poggio T (2000) Models of object recognition. Nat Neurosci 3: 1199–1204. doi: 10.1038/81479
[29]	Suzuki N, Hashimoto N, Kashimori Y, et al. (2004) A neural model of predictive recognition in form pathway of visual cortex. Bio Syst 76: 33–42.
[30]	Pinto N, Cox D, Dicarlo J (2008) Why is real-world visual object recognition hard? PLoS Comput Biol 4: e27. doi: 10.1371/journal.pcbi.0040027
[31]	Rodríguez-Sánchez A, Tsotsos J (2012) The roles of endstopped and curvature tuned computations in a hierarchical representation of 2D shape. PLoS One 7: e42058. doi: 10.1371/journal.pone.0042058
[32]	Hopfield J (1995) Pattern recognition computation using action potential timing for stimulus representation. Nature 376: 33–36. doi: 10.1038/376033a0
[33]	Thorpe S, Fize D, Marlot C (1996) Speed of processing in the human visual system. Nature 381: 520–522. doi: 10.1038/381520a0
[34]	Thorpe S, Delorme A, VanRullen R (2001) Spike-based strategies for rapid processing. Neural Netw 14: 715–725. doi: 10.1016/S0893-6080(01)00083-1
[35]	VanRullen R, Thorpe S (2001) Is it a bird? Is it a plane? Ultra-rapid visual categorisation of natural and artifactual objects. Perception 30: 655–668.
[36]	VanRullen R, Thorpe S (2002) Surfing a spike wave down the ventral stream. Vision Res 42: 2593–2615. doi: 10.1016/S0042-6989(02)00298-5
[37]	Greene E, Patel Y (2018) Scan transcription of two-dimensional shapes as an alternative neuromorphic concept. Trends Artif Intell 1: 27–33.

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