References
- Adelson, E. H. (2000). Lightness perception and lightness illusions. In M. Gazzaniga (Ed.), The new cognitive neurosciences (pp. 339-351). Cambridge, MA: MIT Press.
- Agostini, T., & Galmonte, A. (1999). Spatial articulation affects lightness. Perception & Psychophysics, 61, 1345-1355. doi: 10.3758/bf03206185
- Agostini, T., & Galmonte, A. (2002). Perceptual organization overcomes the effects of local surround in determining simultaneous lightness contrast. Psychological Science, 13, 89-93. doi: 10.1111/1467-9280.00417
- Awasthi, P., Gagrani, A., & Ravindran, B. (2007). Image modeling using tree structured conditional random fields. In R. Sangal, H. Mehta, & R. K. Bagga (Eds.), Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (pp. 2060-2065). San Francisco, CA: Morgan Kaufmann.
- Besag, J. E. (1986). On the statistical analysis of dirty pictures (with discussion). Journal of the Royal Statistical Society, Series B, 48, 259-302. doi: 10.1111/j.2517-6161.1986.tb01412.x
- Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.
- Blake, A., & Kohli, P. (2011). Introduction to Markov random fields. In A. Blake, P. Kohli, & C. Rother (Eds.), Markov random fields for vision and image processing (pp. 1-28). Cambridge, MA: MIT Press. doi: 10.7551/mitpress/8579.003.0001
- Boykov, Y., & Kolmogorov, V. (2004). An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 1124-1137. doi: 10.1109/TPAMI.2004.60
- Boykov, Y., Veksler, O., & Zabih, R. (2001). Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 1222-1239. doi: 10.1109/34.969114
- Bozzi, P. (1969). Direzionalità e organizzazione interna della figura. Memorie della Accademia Patavina di Scienze Lettere ed Arti, 81, 135-170.
- Bozzi, P. (2019). A new factor of perceptual grouping: demonstration in terms of pure experimental phenomenology. In I. Bianchi, & R. Davies (Eds.), Paolo Bozzi’s experimental phenomenology (pp. 246-266). New York: Routledge. [English translation of Bozzi, 1969.]
- Burigana, L., & Vicovaro, M. (2016). Inflections of the Bayesian paradigm in perceptual psychology. Perception, 45, 1412-1425. doi: 10.1177/0301006616669959
- Chen, L. (2005). The topological approach to perceptual organization. Visual Cognition, 12, 553-637. doi: 10.1080/13506280444000256
- Cross, G. R., & Jain, A. K. (1983). Markov random field texture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5, 25-39. doi: 10.1109/TPAMI.1983.4767341
- Crouzil, A., Descombes, X., & Durou, J. D. (2003). A multiresolution approach for shape from shading coupling deterministic and stochastic optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence,25, 1416-1421. doi: 10.1109/TPAMI.2003.1240116
- Darwiche, A. (2009). Modeling and reasoning with Bayesian networks. Cambridge, UK: Cambridge University Press.
- Dass, S. C., Jain, A. K., & Lu, X. (2002). Face detection and synthesis using Markov random field models. Proceedings of the Sixteenth International Conference on Pattern Recognition (pp. 201-204). New York: IEEE. doi: 10.1109/ICPR.2002.1047432
- De Campos, C. P., Tong, Y., & Ji, Q. (2008). Constrained maximum likelihood learning of Bayesian networks for facial action recognition. In D. Forsyth, P. Torr, & A. Zisserman (Eds.), Proceedings of the 2008 European Conference on Computer Vision, Lecture Notes in Computer Science, Volume 5304 (pp. 168-181). Berlin: Springer. doi: 10.1007/978-3-540-88690-7_13
- Ehrenstein, W. H., Spillmann, L., & Sarris, V. (2003). Gestalt issues in modern neuroscience. Axiomathes, 13, 433-458. doi: 10.1023/B:AXIO.0000007203.44686.aa
- Epstein, W. (1988). Has the time come to rehabilitate Gestalt theory? Psychological Research, 50, 2-6. doi: 10.1007/BF00309403
- Fantoni, C., & Gerbino, W. (2003). Contour interpolation by vector-field combination. Journal of Vision, 3, 281-303. doi: 10.1167/3.4.4
- Fechner, G. T. (1966). Elements of psychophysics. New York: Holt, Rinehart and Winston
- Freeman, W. T., & Liu, C. (2011). MRFs for superresolution and texture synthesis. In A. Blake, P. Kohli, & C. Rother (Eds.), Markov random fields for vision and image processing (pp. 155-165). Cambridge, MA: MIT Press. doi: 10.7551/mitpress/8579.003.0012
- Geiger, D., & Girosi, F. (1991). Parallel and deterministic algorithms from MRF’s: Surface reconstruction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 401-412. doi: 10.1109/34.134040
- Geisler, W. S. (2008). Visual perception and the statistical properties of natural scenes. Annual Review of Psychology,59, 167-192. doi: 10.1146/annurev.psych.58.110405.085632
- Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741. doi: 10.1109/TPAMI.1984.4767596
- Geman, S., & Graffigne, C. (1987). Markov random field image models and their applications to computer vision. In A.M. Gleason (Ed.), Proceedings of the 1986 International Congress of Mathematicians, Volume 1 (pp. 1496-1517). Providence, RI: American Mathematical Society.
- Gibbs, J.W. (1902). Elementary principles in statistical mechanics: developed with especial reference to the rational foundations of thermodynamics. New York: C. Scribner’s sons.
- Gibson, J. J. (1950). The perception of the visual world. Cambridge, MA: The Riverside Press.
- Gibson, J. J. (1979). The ecological approach to visual perception. Boston, MA: Houghton Mifflin.
- Gogel, W. C. (1978). The adjacency principle in visual perception. Scientific American, 238, 126-139. doi: 10.1038/scientificamerican0578-126
- Gordon, I. E. (1989). Theories of visual perception. New York: Wiley.
- Hammond, K. R., & Stewart, T. R. (Eds.) (2001). The essential Brunswik: Beginnings, explications, applications. New York: Oxford University Press.
- Hartline, H. K. (1940). The receptive fields of optic nerve fibers. American Journal of Physiology, 130, 690-699. doi: 10.1152/AJPLEGACY.1940.130.4.690
- Hatfield, G. C., & Epstein, W. (1985). The status of the minimum principle in the theoretical analysis of visual perception. Psychological Bulletin, 97, 155-186. doi: 10.1037/0033-2909.97.2.155
- Hochbaum, D. S. (2013). Multi-label Markov random fields as an efficient and effective tool for image segmentation, total variations and regularization. Numerical Mathematics: Theory, Methods, and Applications, 6, 169-198. doi: 10.4208/nmtma.2013.mssvm09
- Hochberg, J. E. (1957). Effects of the Gestalt revolution: The Cornell symposium on perception. Psychological Review, 64, 73-84. doi: 10.1037/h0043738
- Hua, Y., & Tian, H. (2016). Depth estimation with convolutional conditional random field network. Neurocomputing, 214, 546-554. doi: 10.1016/j.neucom.2016.06.029
- Ising, E. (1925). Beitrag zur Theorie des Ferromagnetismus. Zeitschrift für Physik, 31, 253-258. doi: 10.1007/BF02980577
- Kadar, E. E., & Shaw, R. E. (2000). Toward an ecological field theory of perceptual control of locomotion. Ecological Psychology, 12, 141-180. doi: 10.1207/S15326969ECO1202_02
- Kanizsa, G. (1994). Gestalt theory has been misinterpreted, but has also had some real conceptual difficulties. Philosophical Psychology, 7, 149-162. doi: 10.1080/09515089408573117
- Kanizsa, G., & Gerbino, W. (1982). Amodal completion: seeing or thinking? In J. Beck (Ed.), Organization and representation in perception (pp. 167-190). Hillsdale, NJ: Erlbaum.
- Kanizsa, G., & Luccio, R. (1986). Die Doppeldeutigkeiten der Prägnanz. Gestalt Theory, 8, 99-135.
- Kasrai, R., & Kingdom, F. A. A. (2002). Achromatic transparency and the role of local contours. Perception, 31, 775-790. doi: 10.1068/p3357
- Kersten, D. (1991). Transparency and the cooperative computation of scene attributes. In M. S. Landy, & J. A. Movshon (Eds.), Computational models of visual processing (pp. 209-228). Cambridge, MA: MIT Press. doi: 10.7551/mitpress/2002.003.0022
- Kersten, D., Mamassian, P., & Yuille, A. L. (2004). Object perception as Bayesian inference. Annual Review of Psychology, 55, 271-304. doi: 10.1146/annurev.psych.55.090902.142005
- Kham, K., & Blake, R. (2000). Depth capture by kinetic depth and by stereopsis. Perception, 29, 211-220. doi: 10.1068/p3011
- Khang, B. G., & Zaidi, Q. (2002). Cues and strategies for color constancy: perceptual scission, image junctions and transformational color matching. Vision Research, 42, 211-226. doi: 10.1016/S0042-6989(01)00252-8
- Kienker, P. K., Sejnowski, T. J., Hinton, G. E., & Schumacher, L. E. (1986). Separating figure from ground with a parallel network. Perception, 15, 197-216. doi: 10.1068/p150197
- Kim, I. Y., & Yang, H. S. (1994). A systematic way for region-based image segmentation based on Markov random field model. Pattern Recognition Letters, 15, 969-976. doi: 10.1016/0167-8655(94)90028-0
- Kindermann, R., & Snell, J. L. (1980). Markov random fields and their applications. Providence, RI: American Mathematical Society.
- Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671-680. doi: 10.1126/science.220.4598.671
- Koenderink, J. J. (1986). Optic flow. Vision Research, 26, 161-179. doi: 10.1016/0042-6989(86)90078-7
- Koenderink, J. J., van Doorn, A., & Wagemans, J. (2015). The nature of the visual field, a phenomenological analysis. Pattern Recognition Letters, 64, 71-79. doi: 10.1016/j.patrec.2015.02.003
- Koffka, K. (1935). Principles of Gestalt psychology. New York: Harcourt, Brace and Company.
- Köhler, W. (1920). Die physischen Gestalten in Ruhe und im stationären Zustand. Eine naturphilosophische Untersuchung. Braunschweig: Friedrich Vieweg und Sohn. doi: 10.1007/978-3-663-02204-6
- Köhler, W. (1940). Dynamics in psychology. New York: Liveright.
- Köhler, W. (1950). Physical Gestalten. In W. D. Ellis (Ed.), A source book of Gestalt psychology (pp. 17-54). New York: The Humanities Press. [Translated excerpts from Köhler, 1920.]
- Kohli, P., Ladický, L., & Torr, P. H. S. (2011). Enforcing label consistency using higher-order potential. In A. Blake, P. Kohli, & C. Rother (Eds.), Markov random fields for vision and image processing (pp. 311-328). Cambridge, MA: MIT Press. doi: 10.7551/mitpress/8579.003.0024
- Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. Cambridge, MA: MIT Press.
- Kolmogorov, V., & Zabih, R. (2004). What energy functions can be minimized via graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 147-159. doi: 10.1109/TPAMI.2004.1262177
- Kopfermann, H. (1930). Psychologische Untersuchungen über die Wirkung zweidimensionaler Darstellungen körperlicher Gebilde. Psychologische Forschung, 13, 293-364. doi: 10.1007/BF00406771
- Köster, U., Lindgren, J. T., & Hyvärinen, A. (2009). Estimating Markov random field potentials for natural images. In T. Adali, C. Jutten, J. M. T. Romano, & A. K. Barros (Eds.), Proceedings of the Eighth International Conference on Independent Component Analysis and Signal Separation, Lecture Notes in Computer Science, Volume 5441 (pp. 515-522). Berlin: Springer. doi: 10.1007/978-3-642-00599-2_65
- Kubovy, M., & van den Berg, M. (2008). The whole is equal to the sum of its parts: A probabilistic model of grouping by proximity and similarity in regular patterns. Psychological Review, 115, 131-154. doi: 10.1037/0033-295X.115.1.131
- Kumar, S., & Hebert, M. (2006). Discriminative random fields. International Journal of Computer Vision, 68, 179-201. doi: 10.1007/s11263-006-7007-9
- Lafferty, J. D., McCallum, A., & Pereira, F. C. N. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In C. E. Brodley, & A. P. Danyluk (Eds.), Proceedings of the Eighteenth International Conference on Machine Learning (pp. 282-289). San Francisco, CA: Morgan Kaufmann.
- Lehmann, G. (1981). Figurale Wechselwirkungen im Gesichtsfeld. Experimentelle Analysen stochastischer Feldtheorien der subjektiven Figurbildung. Göttingen: Hogrefe.
- Luccio, R. (2019). Perceptual simplicity: The true role of Prägnanz and Occam. Gestalt Theory, 41, 263-276. doi: 10.2478/gth-2019-0024
- Malfait, M., & Roose, D. (1997). Wavelet-based image denoising using a Markov random field a priori model. IEEE Transactions on Image Processing, 6, 549-565. doi: 10.1109/83.563320
- Maxwell, J. C. (1873). A treatise on electricity and magnetism. Oxford, UK: Clarendon Press.
- Metelli, F. (1974). The perception of transparency. Scientific American, 230, 90-98. doi: 10.1038/scientificam erican0474-90
- Metzger, W. (1975). Die Entdeckung der Prägnanztendenz. Die Anfänge einer nicht-atomistischen Wahrnehmungslehre. In G. B. Flores D’Arcais (Ed.), Studies in perception. Festschrift for Fabio Metelli (pp. 3-47). Milano: Martello-Giunti.
- Michel, M. M., & Jacobs, R. A. (2007). Parameter learning but not structure learning: A Bayesian network model of constraints on early perceptual learning. Journal of Vision, 7(1):4, 1-18. doi: 10.1167/7.1.4
- Ming, Y., & Hu, Z. (2010). Modeling stereopsis via Markov random field. Neural Computation, 22, 2161-2191. doi: 10.1162/NECO_a_00005-Ming
- Modestino, J. W., & Zhang, J. (1992). A Markov random field model-based approach to image interpretation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 606-615. doi: 10.1109/34.141552
- Murray, R. F. (2020). A model of lightness perception guided by probabilistic assumptions about lighting and reflectance. Journal of Vision, 20(7):28, 1-22. doi: 10.1167/jov.20.7.28
- Newell, G. F., & Montroll, E. W. (1953). On the theory of the Ising model of ferromagnetism. Reviews of Modern Physics, 25, 353-389. doi: 10.1103/RevModPhys.25.353
- Nowozin, S., & Lampert, C. H. (2009). Global connectivity potentials for random field models. Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 818-825). New York: IEEE. doi: 10.1109/CVPR.2009.5206567
- Orbison, W. D. (1939). Shape as a function of the vector-field. American Journal of Psychology, 52, 31-45. doi: 10.2307/1416658
- Ouali, S., Courbot, J. B., Pierron, R., & Haeberlé, O. (2024). Bayesian image segmentation under varying blur with triplet Markov random field. Inverse Problems, 40, Article Number 095010. doi: 10.1088/1361-6420/ad6a34
- Panagopoulos, A., Wang, C., Samaras, D., & Paragios, N. (2013). Simultaneous cast shadows, illumination and geometry inference using hypergraphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 437-449. doi: 10.1109/TPAMI.2012.110
- Poggio, T., Torre, V., & Koch, C. (1985). Computational vision and regularization theory. Nature, 317, 314-319. doi: 10.1038/317314a0
- Ramachandran, V. S., & Cavanagh, P. (1985). Subjective contours capture stereopsis. Nature, 317, 527-530. doi: 10.1038/317527a0
- Rausch, E. (1966). Das Eigenschaftsproblem in der Gestalttheorie der Wahrnehmung. In W. Metzger (Ed.), Handbuch der Psychologie. Band 1.1. Allgemeine Psychologie: Der Aufbau des Erkennens (Wahrnehmung und Bewusstsein) (pp. 866-953). Göttingen: Hogrefe.
- Ren, X., Fowlkes, C. C., & Malik, J. (2008). Learning probabilistic models for contour completion in natural images. International Journal of Computer Vision, 77, 47-63. doi: 10.1007/s11263-007-0092-6
- Rescorla, M. (2015). Bayesian perceptual psychology. In M. Matthen (Ed.), The Oxford handbook of philosophy of perception (pp. 694-716). New York. Oxford University Press. doi: 10.1093/oxfordhb/9780199600472.013.010
- Richey, M. (2010). The evolution of Markov Chain Monte Carlo methods. American Mathematical Monthly, 117, 383-413. doi: 10.4169/000298910X485923
- Rock, I. (1983). The logic of perception. Cambridge, MA: MIT Press.
- Rubin, N. (2001). The role of junctions in surface completion and contour matching. Perception, 30, 339-366. doi: 10.1068/p3173
- Saxena, A., Chung, S. H., & Ng, A. Y. (2008). 3-D depth reconstruction from a single still image. International Journal of Computer Vision, 76, 53-69. doi: 10.1007/s11263-007-0071-y
- Saxena, A., Sun, M., & Ng, A. Y. (2009). Make3D: Learning 3D scene structure from a single still image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 824-840. doi: 10.1109/TPAMI.2008.132
- Sedgwick, H. A. (2001). Visual space perception. In E. B. Goldstein (Ed.), Blackwell handbook of perception (pp. 128-167). Malden, MA: Blackwell. doi: 10.1002/9780470753477.ch5
- Shotton, J., Winn, J., Rother, C., & Criminisi, A. (2009). TextonBoost for image understanding: multi-class object recognition and segmentation by jointly modeling texture, layout, and context. International Journal of Computer Vision, 81, 2-23. doi: 10.1007/s11263-007-0109-1
- Spillmann, L. (2012). The current status of Gestalt rules in perceptual research: Psychophysics and neurophysiology. In L. Spillmann (Ed.), On perceived motion and figural organization (pp. 191-251). Cambridge, MA: MIT Press. doi: 10.7551/mitpress/9222.003.0008
- Spitzer, F. (1971). Markov random fields and Gibbs ensembles. American Mathematical Monthly, 78, 142-154. doi: 10.2307/2317621
- Stadler, M., Richter, P. H., Pfaff, S., & Kruse, P. (1991). Attractors and perceptual field dynamics of homogeneous stimulus areas. Psychological Research, 53, 102-112. doi: 10.1007/BF01371818
- Sun, J., & Tappen, M. F. (2013). Separable Markov random field model and its applications in low level vision. IEEE Transactions on Image Processing, 22, 402-408. doi: 10.1109/TIP.2012.2208981
- Sutton, C., & McCallum, A. (2012). An introduction to conditional random fields. Foundations and Trends in Machine Learning, 4, 267-373. doi: 10.1561/2200000013
- Szeliski, R. (2011). Computer vision: algorithms and applications. New York: Springer.
- Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M. F., & Rother C. (2008). A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 1068-1080. doi: 10.1109/TPAMI.2007.70844
- Todorović, D. (1997). Lightness and junctions. Perception, 26, 379-394. doi: 10.1068/p260379
- Wagemans, J. (2014). How much of Gestalt theory has survived a century of neuroscience? In A. Geremek, M. W. Greenlee, & S. Magnussen (Eds.), Perception beyond Gestalt: Progress in vision research (pp. 9-21). New York: Psychology Press.
- Wagemans, J. (2018). Perceptual organization. In J. T. Wixted, & J. Serences (Eds.), Stevens’ handbook of experimental psychology and cognitive neuroscience. Volume 2. Sensation, perception, and attention (pp. 803-872). Hoboken, NJ: Wiley. doi: 10.1002/9781119170174.epcn218
- Wagemans, J., Feldman, J., Gepshtein, S., Kimchi, R., Pomerantz, J. R., van der Helm, P. A., & van Leeuwen, C. (2012). A century of Gestalt psychology in visual perception: II. Conceptual and theoretical foundations. Psychological Bulletin, 138, 1218-1252. doi: 10.1037/a0029334
- Wang, C., Komodakis, N., & Paragios, N. (2013). Markov random field modeling, inference & learning in computer vision & image understanding: A survey. Computer Vision and Image Understanding, 117, 1610-1627. doi: 10.1016/j.cviu.2013.07.004
- Wertheimer, M. (1923). Untersuchungen zur Lehre von der Gestalt. II. Psychologische Forschung, 4, 301-350. doi: 10.1007/BF00410640
- Wertheimer, M. (2012). Investigations on Gestalt principles. In L. Spillmann (Ed.), On perceived motion and figural organization (pp. 127-182). Cambridge, MA: MIT Press. doi: 10.7551/mitpress/9222.003.0006 [English translation of Wertheimer, 1923.]
- Wilson, A. D., & Bobick, A. F. (1999). Parametric hidden Markov models for gesture recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 884-900. doi: 10.1109/34.790429
- Wilson, R., & Li, C. T. (2002). A class of discrete multiresolution random fields and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 42-56. doi: 10.1109/TPAMI.2003.1159945
- Winn, J., & Shotton, J. (2011). Markov random fields for object detection. In A. Blake, P. Kohli, & C. Rother (Eds.), Markov random fields for vision and image processing (pp. 389-404). Cambridge, MA: MIT Press. doi: 10.7551/mitpress/8579.003.0030
- Zhang, L., & Ji, Q. (2010). Image segmentation with a unified graphical model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 1406-1425. doi: 10.1109/TPAMI.2009.145
- Zhang, L., Zeng, Z., & Ji, Q. (2011). Probabilistic image modeling with an extended chain graph for human activity recognition and image segmentation. IEEE Transactions on Image Processing, 20, 2401-2413. doi: 10.1109/TIP.2011.2128332
- Zhou, Z., Zhong, L., & Wang, L. (2014). Locally incremental visual cluster analysis using Markov random field. Neurocomputing, 136, 49-55. doi: 10.1016/j.neucom.2014.01.032
- Zhu, S. C. (1999). Embedding Gestalt laws in Markov random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 1170-1187. doi: 10.1109/34.809110
- Zhu, S. C., & Wu, Y. N. (1999). From local features to global perception – A perspective of Gestalt psychology from Markov random field theory. Neurocomputing, 26-27, 939-945. doi: 10.1016/S0925-2312(99)00089-2
- Zhu, S. C., Wu, Y. N., & Mumford, D. (1998). Filters, random fields and maximum entropy (FRAME). Towards a unified theory for texture modeling. International Journal of Computer Vision, 27, 107-126. doi: 10.1023/A:1007925832420