Cognitive mechanism related to line drawings and its applications in intelligent process of visual media: a survey
Yongjin LIU, Minjing YU, Qiufang FU, Wenfeng CHEN, Ye LIU, Lexing XIE
Cognitive mechanism related to line drawings and its applications in intelligent process of visual media: a survey
Line drawings, as a concise form, can be recognized by infants and even chimpanzees. Recently, how the visual system processes line-drawings attracts more and more attention from psychology, cognitive science and computer science. The neuroscientific studies revealed that line drawings generate similar neural actions as color photographs, which give insights on how to efficiently process big media data. In this paper, we present a comprehensive survey on line drawing studies, including cognitive mechanism of visual perception, computational models in computer vision and intelligent process in diverse media applications. Major debates, challenges and solutions that have been addressed over the years are discussed. Finally some of the ensuing challenges in line drawing studies are outlined.
line drawings / cognitive computation / visual media / intelligent process
[1] |
Fischetti M. Computers vs. brains. Scientific American, 2011, 305(5):104–104
CrossRef
Google scholar
|
[2] |
FuX L, Cai L H, Liu Y, Jia J, Chen WF, Zhang Y, Zhao GZ, Liu YJ, Wu C X. A computational cognition model of perception, memory andjudgment. Science China Information Sciences, 2014, 57(3): 1–15
|
[3] |
Ullman S. High-level vision: Object recognition and visual cognition. Cambridge: MIT press, 2000
|
[4] |
Liu Y J, Fu Q F, Liu Y, Fu X L. A distributed computational cognitivemodel for object recognition. Science China Information Sciences, 2013, 56(9): 1–13
CrossRef
Google scholar
|
[5] |
Riddoch M, Humphreys G. Object Recognition. In: Rapp B, ed. Handbookof Cognitive Neuropsychology.Hove: Psychology Press
|
[6] |
Canny J. A computational approach to edge detection. IEEE Transactionson Pattern Analysis and Machine Intelligence, 1986 (6): 679–698
|
[7] |
Cole F, Golovinskiy A, Limpaecher A, Barros H S, Finkelstein A, Funkhouser T, Rusinkiewicz S. Where do people draw lines? ACMTransactions on Graphics, 2008, 27(3): 88
CrossRef
Google scholar
|
[8] |
Sayim B, Cavanagh P. What Line Drawings Reveal About the VisualBrain. Frontiers in Human Neuroscience, 2011, 5
|
[9] |
Yonas A, Arterberry M E. Infants perceive spatial structure specifiedby line junctions. Perception. 1994, 23(12): 1427–1435
CrossRef
Google scholar
|
[10] |
Itakura S. Recognition of line-drawing representations by a chimpanzee(Pan troglodytes). The Journal of general psychology, 1994,121(3): 189–197
CrossRef
Google scholar
|
[11] |
Cole F, Sanik K, DeCarlo D, Finkelstein A, Funkhouser T, Rusinkiewicz S, Singh M. How well do line drawings depict shape? ACM Transactions on Graphics, 2009, 28(3): 28
CrossRef
Google scholar
|
[12] |
Koenderink J J, Van Doorn A J, Kappers A M L. Surface perception inpictures. Perception & Psychophysics, 1992, 52(5): 487–496
CrossRef
Google scholar
|
[13] |
Koenderink J J, van Doorn A J, Kappers A M L, Todd J T. Ambiguityand the “mental eye” in pictorial relief. Perception, 2001, 30(4):431–448
CrossRef
Google scholar
|
[14] |
Marr D, Vision A. A Computational Investigation into the Human Representationand Processing of Visual Information. Cambridge: MITPress, 2010
CrossRef
Google scholar
|
[15] |
Watt R J, Rogers B J. Human vision and cognitive science. Research Directions in Cognitive Science: A European Perspective, 1989, 1: 9–22
|
[16] |
Sonka M, Hlavac V, Boyle R. Image processing, analysis, and machinevision. Cengage Learning, 2014
|
[17] |
Biederman I. Recognition-by-components: a theory of human imageunderstanding. Psychological review, 1987, 94(2): 115
CrossRef
Google scholar
|
[18] |
Biederman I, Ju G. Surface versus edge-based determinants of visual recognition. Cognitive Psychology, 1988, 20(1): 38–64
CrossRef
Google scholar
|
[19] |
Fu Q, Liu Y J, Chen W, Fu X. The time course of natural scene categorization in human brain: simple line-drawings vs. color photographs. Journal of Vision, 2013, 13(9): 1060
CrossRef
Google scholar
|
[20] |
Del Viva M M, Punzi G, Benedetti D. Information and Perception of Meaningful Patterns. PLoS One, 2013, 8(7): e69154
CrossRef
Google scholar
|
[21] |
Morgan M J. Features and the ‘primal sketch’. Vision Research, 2011, 51(7): 738–753
CrossRef
Google scholar
|
[22] |
Delorme A, Richard G, Fabre-Thorpe M. Key visual features for rapidcategorization of animals in natural scenes. Frontiers in Psychology, 2010, 1: 21
|
[23] |
Naber M, Hilger M, Einhäuser W. Animal detection and identificationin natural scenes: image statistics and emotional valence. Journal of Vision, 2012, 12(1): 25
CrossRef
Google scholar
|
[24] |
Derrington A M, Krauskopf J, Lennie P. Chromatic mechanisms in lateralgeniculate nucleus of macaque. The Journal of Physiology, 1984, 357(1): 241–265
CrossRef
Google scholar
|
[25] |
Campbell F W, Robson J G. Application of Fourier analysis to the visibilityof gratings. The Journal of physiology, 1968, 197(3): 551
CrossRef
Google scholar
|
[26] |
Joubert O R, Rousselet G A, Fabre-Thorpe M, Fize D. Rapid visualcategorization of natural scene contexts with equalized amplitude spectrumand increasing phase noise. Journal of Vision, 2009, 9(1): 2
CrossRef
Google scholar
|
[27] |
Jolicoeur P. The time to name disoriented natural objects. Memory &Cognition, 1985, 13(4): 289–303
CrossRef
Google scholar
|
[28] |
Farah M J, Hammond K M. Mental rotation and orientation-invariantobject recognition: dissociable processes. Cognition, 1988, 29(1): 29–46
CrossRef
Google scholar
|
[29] |
Westheimer G. The Fourier theory of vision. Perception, 2001, 30(5):531–542
CrossRef
Google scholar
|
[30] |
Oppenheim A V, Lim J S. The importance of phase in signals. In: Proceedings of the IEEE. 1981, 69(5): 529–541
CrossRef
Google scholar
|
[31] |
Piotrowski L N, Campbell F W. A demonstration of the visual importanceand flexibility of spatial-frequency amplitude and phase. Perception,1982, 11(3): 337–346
CrossRef
Google scholar
|
[32] |
Guyader N, Chauvin A, Peyrin C, Hérault J, Marendaz C. Image phaseor amplitude? Rapid scene categorization is an amplitude-based process. Comptes Rendus Biologies, 2004, 327(4): 313–318
CrossRef
Google scholar
|
[33] |
Chen W F, Liang J, Liu Y J, Fu Q F,Fu X L. Rapid natural scene categorization of line drawings is less influenced by amplitude spectra:evidence from a subliminal perception study. ASSC 18: Poster Session
|
[34] |
Morrone M C, Burr D C. Feature detection in human vision: a phasedependenten ergy model. In: Proceedings of the Royal Society of London. 1988, 221–245
|
[35] |
Devlin H, Tracey I, Johansen-Berg H, Clare S. What is Functional Magnetic Resonance Imaging (fMRI)? FMRIB Centre, Department of Clinical Neurology, University of Oxford
|
[36] |
Walther D B, Caddigan E, L F F, Beck D M. Natural scene categories revealed in distributed patterns of activity in the human brain. The Journalof Neuroscience, 2009, 29(34): 10573–10581
CrossRef
Google scholar
|
[37] |
Walther D B, Chai B, Caddigan E, Beck D M, Li F F. Simple linedrawings suffice for functional MRI decoding of natural scene categories. In: Proceedings of the National Academy of Sciences. 2011, 9661–9666
|
[38] |
Kim S G, Richter W, Uˇgurbil K. Limitations of temporal resolution infunctional MRI. Magnetic Resonance in Medicine, 1997, 37(4): 631–636
CrossRef
Google scholar
|
[39] |
Vanrullen R, Thorpe S J. The time course of visual processing: fromearly perception to decision-making. Journal of Cognitive Neuroscience, 2001, 13(4): 454–461
CrossRef
Google scholar
|
[40] |
Johnson J S, Olshausen B A. Timecourse of neural signatures of objectrecognition. Journal of Vision, 2003, 3(7): 4
CrossRef
Google scholar
|
[41] |
Fu Q F, Liu Y J, Dienes Z, Wu J H, Chen W F, Fu X L. Differenttime courses of natural scene categorization of color photographs andline-drawings: evidence from event-related potentials. Submitted forpublication, 2014
|
[42] |
Liu Y J, Ma C X, Fu Q, Fu X L, Qin S F, Xie L X. A Sketch-Based Approachfor Interactive Organization of Video Clips. ACM Transactionson Multimedia Computing, Communications, and Applications, 2014 ,11(1)
|
[43] |
Snodgrass J G, Vanderwart M. A standardized set of 260 pictures:norms for name agreement, image agreement, familiarity, and visualcomplexity. Journal of Experimental Psychology: Human Learningand Memory, 1980, 6(2): 174
CrossRef
Google scholar
|
[44] |
Magnié M N, Besson M, Poncet M, Dolisi C. The Snodgrass and Vanderwartset revisited: norms for object manipulability and for pictorialambiguity of objects, chimeric objects, and nonobjects. Journal ofClinical and Experimental Neuropsychology, 2003, 25(4): 521–560
|
[45] |
Rossion B, Pourtois G. Revisiting Snodgrass and Vanderwart’s objectpictorial set: the role of surface detail in basic-level object recognition. Perception, 2004, 33(2): 217–236
CrossRef
Google scholar
|
[46] |
Viggiano M P, Vannucci M, Righi S. A new standardized set of ecologicalpictures for experimental and clinical research on visual objectprocessing. Cortex, 2004, 40(3): 491–509
CrossRef
Google scholar
|
[47] |
Abel S, Weiller C, Huber W, Willmes K. Neural underpinnings formodel-oriented therapy of aphasic word production. Neuropsychologia, 2014, 57: 154–165
CrossRef
Google scholar
|
[48] |
Janssen N, Carreiras M, Barber H A. Electrophysiological effects of semanticcontext in picture and word naming. Neuroimage, 2011, 57(3):1243–1250
CrossRef
Google scholar
|
[49] |
Schnur T T. The persistence of cumulative semantic interference duringnaming. Journal of Memory and Language, 2014, 75: 27–44
CrossRef
Google scholar
|
[50] |
Strijkers K, Holcomb P J, Costa A. Conscious intention to speak proactively facilitates lexical access during overt object naming. Journal of Memory and Language, 2011, 65(4): 345–362
CrossRef
Google scholar
|
[51] |
Kang H, Lee S, Chui C. Coherent line drawing. In: Proceedings of 5thInternational Symposium on Non-photorealistic Animation and Rendering. 2007, 43–50
CrossRef
Google scholar
|
[52] |
Arbel T, Ferrie F P. Viewpoint selection by navigation through entropymaps. In: Proceedings of the 7th IEEE International Conference on Computer Vision. 1999, 248–254
CrossRef
Google scholar
|
[53] |
Laporte C, Arbel T. Efficient discriminant viewpoint selection for activebayesian recognition. International Journal of Computer Vision, 2006, 68(3): 267–287
CrossRef
Google scholar
|
[54] |
Secord A, Lu J, Finkelstein A, Sing M, Nealen A. Perceptual modelsof viewpoint preference. ACM Transactions on Graphics, 2011, 30(5):109
CrossRef
Google scholar
|
[55] |
Chen D Y, Tian X P, Shen Y T, Ouhyoung M. On visual similaritybased 3D model retrieval. Computer graphics forum. Publishing, 2003, 22(3): 223–232
|
[56] |
Cyr C M, Kimia B B. 3D object recognition using shape similiaritybased aspect graph. In: Proceedings the 8th IEEE International Conferenceon Computer Vision. 2001. 254–261
|
[57] |
Liu Y J, Luo X, Joneja A, Ma C X, Fu X L, Song D W. User-adaptivesketch-based 3-D CAD model retrieval. IEEE Transactions on AutomationScience and Engineering, 2013, 10(3): 783–795
CrossRef
Google scholar
|
[58] |
Duda R O, Hart P E, Stork D G. Pattern Classification. John Wiley &Sons, 1999.
|
[59] |
Chalechale A, Naghdy G, Mertins A. Sketch-based image matchingusing angular partitioning. IEEE Transactions on Systems, Man andCybernetics, 2005, 35(1): 28–41
|
[60] |
Eitz M, Richter R, Boubekeur T, Hildebrand K, Alexa M. Sketch-basedshape retrieval. ACM Transactions on Graphics, 2012, 31(4): 31
CrossRef
Google scholar
|
[61] |
Hung C C, Carlson E T, Connor C E. Medial axis shape coding inmacaque inferotemporal cortex. Neuron, 2012, 74(6): 1099–1113
CrossRef
Google scholar
|
[62] |
Lescroart M D, Biederman I. Cortical representation of medial axisstructure. Cerebral Cortex, 2013, 23(3): 629–637
CrossRef
Google scholar
|
[63] |
Li Z, Qin S, Jin X. Skeleton-enhanced line drawings for 3D models. Graphical Models, 2014, 76(6): 620–632
CrossRef
Google scholar
|
[64] |
Lake B M, Salakhutdinov R, Tenenbaum J. One-shot learning by invertinga compositional causal process. Advances in neural information processing systems. 2013: 2526–2534
|
[65] |
Ma C X, Liu Y J, Yang H Y, Teng D X, Wang H A, Dai G Z. KnitSketch:a sketch pad for conceptual design of 2D garment patterns. IEEE Transactions on Automation Science and Engineering, 2011, 8(2): 431–437
CrossRef
Google scholar
|
[66] |
Sivic J, Zisserman A. Video Google: A text retrieval approach to objectmatching in videos
|
[67] |
Baeza-Yates R, Ribeiro-Neto B. Modern Information Retrieval. NewYork: ACM Press, 1999
|
[68] |
Sugihara K. Machine Interpretation of Line Drawings. Cambridge:MIT Press, 1986
|
[69] |
Hoffman D D. Visual Intelligence: How We Create WhatWe See. NewYork: W.W. Norton & Company, 2000
|
[70] |
Chen T, Cheng M M, Tan P, Ariel S, Hu S M. Sketch2Photo: internetimage montage. ACM Transactions on Graphics, 2009, 28(5): 124
CrossRef
Google scholar
|
[71] |
Cao Y, Wang C H, Zhang L Q, Zhang L. Edgel index for large-scalesketch-based image search. IEEE Conference on Computer Vision and Pattern Recognition, 2011: 761–768
|
[72] |
Truong B T, Venkatesh S. Video abstraction: a systematic review andclassification. ACM Transactions on Multimedia Computing, Communications, and Applications, 2007, 3(1): 3
CrossRef
Google scholar
|
[73] |
Ma C X, Liu Y J, Wang H A, Teng D X, Dai G Z. Sketch-based annotationand visualization in video authoring. IEEE Transactions onMultimedia, 2012, 14(4): 1153–1165
|
[74] |
Collomosse J P, McNeill G, Qian Y. Storyboard sketches for contentbased video retrieval. In: Proceedings of the 12th IEEE International Conference on Computer Vision. 2009, 245–252
|
[75] |
Igarashi T, Matsuoka S, Tanaka H. Teddy: a sketching interface for 3Dfreeform design. In: Proceedings of ACM SIGGRAPH Courses, 2007:21
CrossRef
Google scholar
|
[76] |
Nealen A, Igarashi T, Sorkine O, Alexa M. FiberMesh: designingfreeform surfaces with 3D curves. ACM Transactions on Graphics, 2007, 26(3): 41
CrossRef
Google scholar
|
[77] |
Liu Y J, Ma C X, Zhang D L. EasyToy: plush toy design using editablesketching curves. IEEE Computer Graphics and Applications, 2011,31(2): 49–57
CrossRef
Google scholar
|
[78] |
Zhu X, Jin X, Liu S, Zhoo H. Analytical solutions for sketch-basedconvolution surface modeling on the GPU. The Visual Computer, 2012,28(11): 1115–1125
CrossRef
Google scholar
|
[79] |
Yu C C, Liu Y J, Wu T F, Li K Y, Fu X L. A global energy optimization framework for 2.1D sketch extraction from monocular images. Graphical Models, 2014, 76(5): 507–521
CrossRef
Google scholar
|
[80] |
Liu Y J, Zhang J B, Hou J C, Ren J C. Cylinder detection in large-scalepoint cloud of pipeline plant. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(10): 1700–1707
CrossRef
Google scholar
|
/
〈 | 〉 |