Image curvature assessment in the presence of distractors

Bondarko, V.M., Solnushkin, S.D., Chikhman, V.N.

For Russian citation (Opticheskii Zhurnal):

Бондарко В.М., Солнушкин С.Д., Чихман В.Н. Оценка кривизны изображений в присутствии дистракторов // Оптический журнал. 2024. Т. 91. № 10. С. 94–105. http://doi.org/10.17586/1023-5086-2024-91-10-94-105

Bondarko V.M., Solnushkin S.D., Chikhman V.N. Image curvature assessment in the presence of distractors [in Russian] // Opticheskii Zhurnal. 2024. V. 91. № 10. P. 94–105. http://doi.org/10.17586/1023-5086-2024-91-10-94-105

For citation (Journal of Optical Technology):

Abstract:

Subject of study. The mechanisms of visual perception of image curvature in the presence of additional images, so-called distractors, were studied. The purpose of the work is to describe and consider the mechanisms of images curvature perception. Methods. A number of psychophysical experiments were carried out, images were analyzed and the data obtained were compared with the results of studies of the perception of other images. Main results. Curvature estimates were obtained for real and interpolated images. Distortions (illusions) in the perception of curvature in the presence of distractors were revealed. Straight lines are perceived as curved, and estimates for other stimuli are also erroneous. The illusions are stronger for interpolated images. The data obtained are consistent with the tilt illusion resulting from the interaction between spatial frequency channels. It can be assumed that the illusions of curvature can also be explained by this interaction. But the question arises about the ambiguity of the mechanisms for estimating curvature due to contradictory data. Curvature estimates depend on image shape and surroundings. Practical significance. The results can be implemented in artificial neural networks used to recognize faces and other images close to those used in the study, since during the primary processing of objects in such networks, filtering is often carried out, similar to filtering by the receptive fields of the visual cortex.

Keywords:

curvature, optical illusions, tilt illusion, interaction between spatial-frequency channels

Acknowledgements:

the study was supported by the State funding allocated to the Pavlov Institute of Physiology Russian Academy of Sciences (№ 1021062411653-4-3.1.8).

OCIS codes: 330.7326, 330.4060, 330.5510

References:

1. Baker N., Elder J.H. Deep learning models fail to capture the configural nature of human shape perception // Iscience. 2022. V. 25(9). P. 1–17. https://doi.org/10.1016/j.isci.2022.104913
2. He W., Jiang Z., Zhang C. et al. CurvaNet: Geometric deep learning based on directional curvature for 3D shape analysis // Proc. 26th ACM SIGKDD Int. Conf. Knowledge Discovery. 2020. P. 2214–2224. https://doi.org/10.1145/3394486.3403272
3. Srinivas S., Matoba K., Lakkaraju H. et al. Efficient training of low-curvature neural networks // Advances in Neural Information Processing Systems. 2022. V. 35. P. 25951–25964.
4. Tavana P., Akraminia M., Koochari A. et al. Classification of spinal curvature types using radiography images: deep learning versus classical methods // Artificial Intelligence Review. 2023. P. 1–33. https://doi.org/10.1007/s10462-023-10480-w
5. Attneave F. Some informational aspects of visual perception // Psychol. Rev. 1954. V. 61. P. 183–197. https://psycnet.apa.org/doi/10.1037/h0054663
6. Wilson H.R. Discrimination of contour curvature: data and theory // J. Opt. Soc. Am. 1985. V. 2. P. 1191–1199. https://doi.org/10.1364/JOSAA.2. 001191
7. Wilson H.R., Richards W.A. Mechanisms of contour curvature discrimination // J. Opt. Soc. Am. 1989. V. 6. P. 106–115. https://doi.org/10.1364/JOSAA.6.000106
8. Habak C., Wilkinson F., Zakher B., Wilson H.R. Curvature population coding for complex shapes in human vision // Vision Res. 2004. V. 44. P. 2815–2826. https://doi.org/10.1016/j.visres.2004.06.019
9. Foster D.H., Simmons D.R., Cook M.J. The cue for contour curvature discrimination // Vision Res. 1993. V. 33. P. 329–338. https://doi.org/10.1016/0042-6989(93)90089-F

10. Kramer D., Fahle M. A simple mechanism for detecting low curvatures // Vision Res. 1996. V. 36. P. 1411–1423. https://doi.org/10.1016/0042-6989(95)00340-1
11. Ninio J. Geometrical illusions are not always where you think they are: a review of some classical and less classical illusions, and ways to describe them // Frontiers in human neuroscience. 2014. V. 8. A. 856. P. 1–21. https://doi.org/10.3389/fnhum.2014.00856
12. Sweeny T.D., Grabowecky M., Kim Y.J. et al. Internal curvature signal and noise in low-and high-level vision // J. Neurophysiology. 2011. V. 105(3). P. 1236–1257. https://doi.org/10.1152/jn.00061.2010
13. Wang S.M., Liao C.L., Ni Y.Q. A machine vision system based on driving recorder for automatic inspection of rail curvature // IEEE Sensors J. 2021. V. 21(10) P. 11291–11300.
14. Choudhury S.D., Bhattacharyya A. Generalised curvature estimation using geometric measure theory with a feature detection application in computer vision // Multimed. Tools&Appl. 2024. P. 25415–25434. https://doi.org/10.1007/s11042-023-16408-4
15. Nisar I. Curvature сoding in рuman vision: A classical review across psychophysics, neurophysiology and computer vision. What’s missing? // Proc. IEEE. 2023. P. 1–13. https://doi.org/ 10.20944/preprints202310.0349.v2
16. Chellappa R., Wilson C.L., Sirohey S. Human and machine recognition of faces: A survey // Proc. IEEE. 1995. V. 83(5). P. 705–741. DOI: 10.1109/5.381842
17. Бондарко В.М., Чихман В.Н. Искажение формы изображений в оптических иллюзиях // Оптический журнал. 2023. Т. 90. № 10. С. 67–79. http://doi.org/10.17586/1023-5086-2023-90-10-67-79
Bondarko V.M., Chikhman V.N. Shape deformation in optical illusions // Journal of Optical Technol ogy. 2023. V. 90. P. 601–608. https://doi.org/10.1364/ JOT.90.000601
18. Gibson J.J., Radner M. Adaptation, after-effect and contrast in the perception of tilted lines // J. Exp. Psychology. 1937. V. 20. P. 453–467.
19. O’Toole B., Wenderoth P. The tilt illusion: Repulsion and attraction effects in the oblique meridian // Vision Res. 1977. V. 17. P. 367–374. https://doi.org/10.1016/0042-6989(77)90025-6
20. Бондарко В.М., Солнушкин С.Д., Чихман В.Н. Зрительные иллюзии и восприятие классической архитектуры // Эксп. псих. 2023. Т. 16. № 3. C. 68–85. https://doi.org/10.17759/exppsy.2020130
Bondarko V.M., Solnushkin S.D., Chikhman V.N. Visual illusion and perception of classical architecture [In Russian] // Eksperimental’naya psikhologiya =Experimental psychology (Russia). 2023. V. 16(3). Р. 68–85. https:// doi.org/10.17759/exppsy.2020130
21. Бондарко В.М. Иллюзия наклона и ориентационная чувствительность // Физиология человека. 2020. Т. 46. № 3. С. 90–98. https://doi.org/10.31857/S0131164620020034
Bondarko V.M. The tilt illusion and orientation sensitivity // Human Physiology. 2020. V. 46. P. 312–320. https://doi.org/10.1134/S0362119720020036
22. Blakemore C., Carpenter R.H.S., Georgeson M.A. Lateral inhibition between orientation detectors in the human visual system // Nature. 1970. V. 228. № 5266. P. 37–39. https://doi.org/10.1038/228037a0
23. Carpenter R.H.S., Blakemore C. Interaction between orientation in human vision // Exp. Brain Res. 1973. V. 18. P. 287–303. https://doi.org/10.1007/BF00234599
24. Dobbins A., Zucker S.W., Cynader M.S. Endstopping and curvature // Vision Res. 1989. V. 29. P. 1371–1385. https://doi.org/10.1016/0042-6989(89)90193-4
25. Глезер В.Д. Зрение и мышление. СПб.: Наука, 1993. 285 с.
Glezer V.D. Vision and thinking [in Russian]. St. Petersburg: Nauka, 1993. 285 p.
26. Schmidtmann G., Ouhnana M., Loffler G. et al. Imagining circles — empirical data and a perceptual model for the arc-size illusion // Vision Res. 2016. V. 121. P. 50–56. https://doi.org/10.1016/j.visres.2015.12.003
27. Matsushita S., Morikawa K., Mitsuzane S. et al. Eye shape illusions induced by eyebrow positions // Perception. 2015. V. 44(5). P. 529–540. https://doi.org/10.1068/p7823
28. Барабанщиков В.А., Маринова М.М. Deepfake как основа цифрового коллажирования «невозможного лица» // Оптический журнал. 2022. Т. 89. № 8. С. 24–32. https://doi.org/10.17586/1023-5086-2022-89-08-24-32
Barabanshchikov V.A., Marinova M.M. Deepfake as the basis for digitally collaging “impossible faces” // Journal of Optical Technology. 2022. V. 89(8). P. 448–453. https://doi.org/10.1364/JOT.89.000448
29. Королькова О.А., Лободинская Е.А. База видеоизображений естественных эмоциональных экспрессий БЕВЭЛ: восприятие эмоций и автоматизированный анализ мимики лица // Оптический журнал. 2022. Т. 89. № 8. С. 97–103. https://doi.org/10.17586/1023-5086-2022-89-08-97-103
Korolkova O.A., Lobodinskaya E.A. Database of video images of natural emotional facial expressions: perception of emotions and automated analysis of facial structure // Journal of Optical Technology. 2022. V. 89(8). P. 498–501. https://doi.org/10.1364/JOT.89. 000498
30. Малахова Е.Ю. Представление категорий посредством прототипов согласованной активности нейронов в свёрточных нейронных сетях // Оптический журнал. 2021. Т. 88. № 12. C. 36–41. https://doi. org/10.17586/1023-5086-2021-88-12-36-41
Malakhova E.Yu. Representation of categories through prototypes formed based on coordinated activity of units in convolutional neural networks // Journal of Optical Technology. 2021. V. 88(12). P. 706–709. https://doi.org/10.1364/JOT.88.000706
31. Явна Д.В., Бабенко В.В., Горбенкова О.А. и др. Категоризация объектов и сцен нейронной сетью, входы которой предварительно обучены декодированию пространственных неоднородностей текстуры // Оптический журнал. 2023. Т. 90. № 1. С. 37–48. https:// doi.org/10.17586/1023-5086-2023-90-01-37-48
Yavna D.V., Babenko V.V., Gorbenkova O.A. et al. Classification of objects and scenes by a neural network with pretrained input modules to decode spatial texture inhomogeneities // Journal of Optical Technology. 2023. V. 90. № 1. P. 20–25. https://doi.org/10.1364/ JOT.90.000020
32. Ермаченкова М.К., Малашин Р.О., Бойко А.А. Обучение нейронных сетей для классификации тепловизионных изображений на основе изображений видимого спектра // Оптический журнал. 2023. Т. 90. № 10. С. 48–66. http://doi.org/10.17586/1023-5086-2023-90-10-48-66
Ermachenkova M.K., Malashin R.O., Bojko A.A. Neural network training for thermal image classification based on visible spectrum images // Journal of Optical Technology. 2023. V. 90. № 10. P. 590–600. https:// doi.org/10.1364/JOT.90.000590
33. Yuille A., Liu C. Deep nets: What have they ever done for vision? // Int. J. Computer Vision. 2021. V. 129. P. 781–798. https://doi.org/10.1007/s11263-020-01405-z