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Deep Learning-Based Computer Vision Is Not Yet the Answer to Taphonomic Equifinality in Bone Surface Modifications Cover

Deep Learning-Based Computer Vision Is Not Yet the Answer to Taphonomic Equifinality in Bone Surface Modifications

Journal of Computer Applications in Archaeology
Volume 7 (2024): Issue 1
By: Lloyd Austin Courtenay,  Nicolas Vanderesse,  Luc Doyon and  Antoine Souron  
Open Access
|Dec 2024

References

  1. 1Abadi, A and Berrada, AM. 2018. ‘Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI)’. IEEE Access, 6: 52138–52160. DOI: 10.1109/ACCESS.2018.2870052
    Back to article
  2. 2Abellán, N, Baquedano, E and Domínguez-Rodrigo, M. 2022. ‘High-accuracy in the classification of butchery cut marks and crocodile tooth marks using machine learning methods and computer vision algorithms’. Geobios, 72–73: 12–21. DOI: 10.1016/j.geobios.2022.07.001
    Back to article
  3. 3Abellán, N, Jiménez-García, B, Aznarte, J, Baquedano, E and Domínguez-Rodrigo, M. 2021. ‘Deep learning classification of tooth scores made by different carnivores: achieving high accuracy when comparing African carnivore taxa and testing the hominin shift in the balance for power’. Archaeological and Anthropological Sciences, 13: 31. DOI: 10.1007/s12520-021-01273-9
    Back to article
  4. 4Agrawal, A, Lu, J, Antol, S, Mitrchell, M, Zitnick, CL, Batra, D and Parikh, D. 2016. ‘VQA: Visual Question Answering’. IEEE International Conference on Computer Vision, 2425–2433. DOI: 10.1109/ICCV.2015.279
    Back to article
  5. 5Aiello, LC and Wheeler, P. 1995. ‘The expensive-tissue hypothesis: The brain and the digestive system in human and primate evolution’. Current Anthropology, 36: 199–221. DOI: 10.1086/204350
    Back to article
  6. 6Ali, U and Mahmood, MT. 2018. ‘Analysis of blur measure operator for single image blur segmentation’. Applied Sciences, 8(5): 807. DOI: 10.3390/app8050807
    Back to article
  7. 7Andrews, P and Cook, J. 1985. ‘Natural modifications to bones in a temperate setting’. Man, 20(4): 675–691. DOI: 10.2307/2802756
    Back to article
  8. 8Arman, SD, Ungar, PS, Brown, CA, DeSantis, LRG, Schmidt, C and Prideaux, GJ. 2016. ‘Minimizing inter-microscope variability in dental microwear texture analysis’. Surface Topography: Metrology and Properties, 4(2): 024007. DOI: 10.1088/2051-672X/4/2/024007
    Back to article
  9. 9Athanasiadou, E, Geradts, Z and Eijk, EV. 2018. ‘Camera recognition with deep learning’. Forensic Sciences Research, 3(3): 210–218. DOI: 10.1080/20961790.2018.1485198
    Back to article
  10. 10Baquedano, E, Domínguez-Rodrigo, M and Musiba, C. 2012. ‘An experimental study of large mammal bone modification by crocodiles and its bearing on the interpretation of crocodile predation at FLK Zinj and FLK NN3’. Journal of Archaeological Science, 39: 1728–1737. DOI: 10.1016/j.jas.2012.01.010
    Back to article
  11. 11Barr, WA, Pobiner, B, Rowan, J, Du, A and Faith, JT. 2022. No sustained increase in zooarchaeological evidence for carnivory after the appearance of Homo erectus. 119(5): e2115540119. DOI: 10.1073/pnas.2115540119
    Back to article
  12. 12Ben-David, S, Blitzer, J, Crammer, K, Kulesza, A, Pereira, F and Vaughan, JW. 2010. ‘A theory of learning from different domains’. Machine Learning, 79(1–2): 151–175. DOI: 10.1007/s10994-009-5152-4
    Back to article
  13. 13Bengio, Y. 2012. ‘Deep learning of representations for unsupervised and transfer learning’. JMLR Workshop and Conference Proceedings, 27: 17–37. DOI: 10.1007/978-3-642-39593-2_1
    Back to article
  14. 14Binford, LR. 1981. Bones: Ancient Men and Modern Myths. New York: Academic Press Inc.
    Back to article
  15. 15Bishop, CM. 1991. ‘Novelty detection and neural network validation’. International Conference on Artificial Neural Networks, 789–794. DOI: 10.1007/978-1-4471-2063-6_225
    Back to article
  16. 16Bishop, CM. 1995. Neural networks for pattern recognition. Oxford: Claredon Press. DOI: 10.1093/oso/9780198538493.001.0001
    Back to article
  17. 17Bishop, CM. 2006. Pattern recognition and machine learning. Singapore: Springer.
    Back to article
  18. 18Blumenschine, R. 1995. ‘Percussion Marks, Tooth Marks and Experimental Determinations of the Timing of Hominid and Carnivore Access to Long Bones at FLK Zinjanthropus, Olduvai Gorge, Tanzania’. Journal of Human Evolution, 29(1): 21–51. DOI: 10.1006/jhev.1995.1046
    Back to article
  19. 19Borel, A, Ollé, A, Vergès, JM and Sala, R. 2014. ‘Scanning electron and optical light microscopy: two complementary approaches for the understanding and interpretation of usewear and residues on stone tools’. Journal of Archaeological Science, 48: 46–59. DOI: 10.1016/j.jas.2013.06.031
    Back to article
  20. 20Brownlee, J. 2017. Deep Learning with Python. Melbourne: Machine Learning Mastery.
    Back to article
  21. 21Bunn, HT. 1991. ‘A taphonomic perspective on the archaeology of human origins’. Annual Review in Anthropology, 20: 433–467. DOI: 10.1146/annurev.anthro.20.1.433
    Back to article
  22. 22Byeon, W, Domínguez-Rodrigo, M, Arampatzis, G, Baquedano, E, Yravedra, J, Maté-González, MÁ and Koumoutsakos, P. 2019. ‘Automated identification and deep classification of cut marks on bones and its paleoanthropological implications’. Journal of Computational Science, 32: 36–43. DOI: 10.1016/j.jocs.2019.02.005
    Back to article
  23. 23Calandra, I, Schunk, L, Bob, K, Gneisinger, W, Pedergnana, A, Paixao, E, Hildebrandt, A and Marreiros, J. 2019. ‘The effect of numerical aperture on quantitative use-wear studies and its implication on reproducibility’. Scientific Reports. DOI: 10.1038/s41598-019-42713-w
    Back to article
  24. 24Calder, J, Coil, R, Melton, A, Olver, PJ, Tostevin, G and Yezzi-Woodley, K. 2022. ‘Use and misuse of machine learning in anthropology’. IEEE BITS the Information Theory Magazine, 2(1): 102–115. DOI: 10.1109/MBITS.2022.3205143
    Back to article
  25. 25Canny, J. 1986. ‘A computational approach to edge detection’. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6): 679–698. DOI: 10.1109/TPAMI.1986.4767851
    Back to article
  26. 26Carlini, N and Wagner, D. 2017. ‘Towards evaluating the robustness of neural networks’. IEEE Symposium on Security and Privacy, 39–57. DOI: 10.1109/SP.2017.49
    Back to article
  27. 27Chen, X, Zheng, B and Liu, H. 2011. ‘Optical and digital microscopic imaging techniques and applications in pathology’. Analytical Cellular Pathology, 34(1–2): 5–18. DOI: 10.1155/2011/150563
    Back to article
  28. 28Chollet, F. 2017. Deep Learning with Python. New York: Manning.
    Back to article
  29. 29Cifuentes-Alcobendas, G and Domínguez-Rodrigo, M. 2019. ‘Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks’. Scientific Reports, 9: 18933. DOI: 10.1038/s41598-019-55439-6
    Back to article
  30. 30Cifuentes-Alcobendas, G and Domínguez-Rodrigo, M. 2021. ‘More than meets the eye: use of computer vision algorithms to identify stone tool material through the analysis of cut mark micro-morphology’. Archaeological and Anthropological Sciences, 13: 167. DOI: 10.1007/s12520-021-01424-y
    Back to article
  31. 31Cobo-Sánchez, L, Pizarro-Monzo, M, Cifuentes-Alcobendas, G, Jiménez-García, B, Abellán-Beltrán, N, Courtenay, LA, Mabulla, A, Baquedano, E and Domínguez-Rodrigo, M. 2022. ‘Computer vision supports primary Access to meat by early Homo, 1.84 million years ago’. PeerJ, 10: e1418. DOI: 10.7717/peerj.14148
    Back to article
  32. 32Courtenay, LA. 2023. ‘Can we restore balance to geometric morphometrics? A theoretical evaluation of how sample imbalance conditions ordination and classification’. Evolutionary Biology, 50: 90–110. DOI: 10.1007/s11692-022-09590-0
    Back to article
  33. 33Courtenay, LA, Herranz-Rodrigo, D, González-Aguilera, D and Yravedra, J. 2021. ‘Developments in data science solutions for carnivore tooth pit classification’. Scientific Reports, 11: 10209. DOI: 10.1038/s41598-021-89518-4
    Back to article
  34. 34Da Rin, G, Seghezzi, M, Padoan, A, Pajola, R, Bengiamo, A, Di Fabio, AM, Dima, F, Fanelli, A, Francione, S, Germagnoli, L, Lorubbio, M, Marzoni, A, Pipitone, S, Rolla, R, Bagorria Vaca, MC, Bartolini, A, Bonato, L, Sciacovelli, L and Buoro, S. 2022. ‘Multicentric evaluation of the variability of digital morphology performances also respect to the reference methods by optical microscopy’. International Journal of Laboratory Hematology, 44(6): 1040–1049. DOI: 10.1111/ijlh.13943
    Back to article
  35. 35Dablain, D, Jacobson, KN, Bellinger, C, Roberts, M and Chawla, NV. 2023. ‘Understanding CNN fragility when learning with imbalanced data’. Machine Learning. DOI: 10.1007/s10994-023-06326-9
    Back to article
  36. 36Deng, J, Berg, AC, Li, K and Fei-Fei, L. 2010. What does classifying more than 10,000 image categories tell us? In: Daniilidis, K, Margos, P and Paragios, N (eds.) Proceedings of the 11th European Conference on Computer Vision. Heidelberg: Springer, 71–84. DOI: 10.1007/978-3-642-15555-0_6
    Back to article
  37. 37Deng, J, Dong, W, Socher, R, Li, LJ, Li, K and Fei-Fei, L. 2009. ‘Imagenet: A large-scale hierarchical image database’. IEEE Conference on Computer Vision and Pattern Recognition, 2009: 248–255. DOI: 10.1109/CVPR.2009.5206848
    Back to article
  38. 38Dhamija, AR, Günther, M and Boult, TE. 2018. ‘Reducing network agnostophobia’. Neural Information Processing Systems. 32: 1–10
    Back to article
  39. 39Diakonikolas, I, Kamath, G, Kane, DM, Li, J, Moitra, A and Stewart, A. 2017. ‘Being robust (in high dimensions) can be practical’. Proceedings of the International Conference on Machine Learning, 34: 1–10.
    Back to article
  40. 40Diakonikolas, I, Kamath, G, Kane, DM, Li, J, Moitra, A and Stewart, A. 2019. Robust estimators in high dimensions with the computational intractability. Available at: https://arxiv.org/pdf/1604.06443.pdf [Last accessed 26/08/2023].
    Back to article
  41. 41Dodge, S and Karam, L. 2016. ‘Understanding how image quality affects deep neural networks’. Quality of Multimedia Experience, 8: 1–6. DOI: 10.1109/QoMEX.2016.7498955
    Back to article
  42. 42Domínguez-Rodrigo, M. 2015. ‘Taphonomy in early African archaeological sites: questioning some bone surface modification models for inferring fossil hominin and carnivore feeding interactions’. Journal of African Earth Sciences, 108: 42–46. DOI: 10.1016/j.jafrearsci.2015.04.011
    Back to article
  43. 43Domínguez-Rodrigo, M, Baquedano, E, Varela, L, Tambusso, PS, Melián, MJ and Fariá, RA. 2021c. ‘Deep classification of cut marks on bones from Arroyo del Vizcaíno (Uruguay)’. Proceedings of the Royal Society B, 288: 20210711. DOI: 10.1098/rspb.2021.0711
    Back to article
  44. 44Domínguez-Rodrigo, M, Barba, R and Egeland, CP. 2007. Deconstructing Olduvai: A taphomomic study of the Bed I sites. The Netherlands: Springer. DOI: 10.1007/978-1-4020-6152-3
    Back to article
  45. 45Domínguez-Rodrigo, M, Baquedano, E, Organista, E, Cobo-Sánchez, L, Mabulla, A, Maskara, V, Gidna, A, Pizarro-Monzo, M, Aramendi, J, Belén Galán, A, Cifuentes-Alcobendas, G, Vegara-Riquelme, M, Jiménez-García, B, Abellán, N, Barba, R, Uribelarrea, D, Martín-Perea, D, Diez-Martin, F, Maíllo-Fernández, JM, Rodríguez-Hidalgo, A, Courtenay, LA, Mora, R, Maté-González, MÁ and González-Aguilera, D. 2021a. ‘Early Pleistocene faunivorous hominins were not kleptoparasitic, and this impacted the evolution of human anatomy and socio-ecology’. Scientific Reports, 11: 16135. DOI: 10.1038/s41598-021-94783-4
    Back to article
  46. 46Domínguez-Rodrigo, M, Cifuentes-Alcobendas, G, Jiménez-García, B, Abellán, N, Pizarro-Monzo, M, Organista, E and Baquedano, E. 2020. ‘Artificial Intelligence provides greater accuracy in the classification of modern and ancient bone surface modifications’. Scientific Reports, 10: 18862. DOI: 10.1038/s41598-020-75994-7
    Back to article
  47. 47Domínguez-Rodrigo, M, Courtenay, LA, Cobo-Sánchez, L, Baquedano, E and Mabulla, A. 2021b. ‘A case of hominin scavenging 1.84 million years ago from Olduvai Gorge (Tanzania)’. Annals of the New York Academy of Sciences, 1510: 121–131. DOI: 10.1111/nyas.14727
    Back to article
  48. 48Domínguez-Rodrigo, M, Juana, S, Galán, AB and Rodríguez, M. 2009. ‘A new protocol to differentiate trampling marks from butchery marks’. Journal of Archaeological Science, 36(12): 2643–2654. DOI: 10.1016/j.jas.2009.07.017
    Back to article
  49. 49Domínguez-Rodrigo, M, Pizarro-Monzo, M, Cifuentes-Alcobendas, G, Vegara-Riquelme, M, Jiménez-García, B, Baquedano, E. 2024. ‘Computer visión enables taxon-specific identification of African carnivore tooth marks on bone’. Scientific Reports, 14: 6881. DOI: 10.1038/s41598-024-57015-z
    Back to article
  50. 50Domínguez-Rodrigo, M, Saladié, P, Cáceres, I, Huguet, R, Yravedra, J, Rodríguez-Hidalgo, A, Martín, P, Pineda, A, Marín, J, Gené, C, Aramendi, J and Cobo-Sánchez, L. 2017. ‘Use and abuse of cut mark analyses: the Rorschach effect’. Journal of Archaeological Science, 86: 14–23. DOI: 10.1016/j.jas.2017.08.001
    Back to article
  51. 51Domínguez-Solera, S and Domínguez-Rodrigo, M. 2011. ‘A taphonomic study of a carcass consumed by griffon vultures (Gyps fulvus) and its relevance for the interpretation of bone surface modifications’. Archaeological and Anthropological Sciences, 3: 385–392. DOI: 10.1007/s12520-011-0071-2
    Back to article
  52. 52Drumheller, SK and Brochu, CA. 2014. ‘A diagnosis of Alligator mississippiensis bite marks with comparisons to existing crocodylian datasets’. Ichnos, 21(2): 131–146. DOI: 10.1080/10420940.2014.909353
    Back to article
  53. 53Esteva, A, Kuprel, B, Navoa, RA, Ko, J, Swetter, SM, Blau, HM and Thrun, S. 2017. ‘Dermatologist-level classification of skin cancer with deep neural networks’. Nature letters, 542: 115–118. DOI: 10.1038/nature21056
    Back to article
  54. 54Fedorov, D, Sumengen, C and Manjunath, B. 2006. ‘Multi-focus imaging using local focus estimation and mosaicking’. IEEE International Conference on Image Processing, 2093–2096. DOI: 10.1109/ICIP.2006.312820
    Back to article
  55. 55Feris, R, Raskar, R, Tan, KH and Turk, M. 2006. ‘Specular highlights detection and reduction with multiflash photography’. Journal of the Brazilian Computer Society, 12: 35–42. DOI: 10.1590/S0104-65002006000200003
    Back to article
  56. 56Fink, M. 2004. ‘Object classification from a single example utilizing class relevance metrics’. Advances in Neural Information Processing Systems, 17: 449–456.
    Back to article
  57. 57Finn, C, Abbeel, P and Levine, S. 2017. ‘Model-agnostic meta-learning for fast adaptation of deep net-works’. International Conference on Machine Learning, 1126–1135. Available at: https://arxiv.org/pdf/1703.03400.pdf [Last accessed 26/08/2023].
    Back to article
  58. 58Finn, C and Levine, S. 2017. ‘Deep visual foresight for planning robot motion’. IEEE International Conference on Robotics and Automation, 2786–2793. DOI: 10.1109/ICRA.2017.7989324
    Back to article
  59. 59Fortin, JP, Cullen, N, Sheline, YI, Taylor, WD, Aselcioglu, I, Cook, PA, Adams, P, Cooper, C, Fava, M, McGrath, PJ, McInnis, M, Phillips, ML, Trivedi, MH, Weissman, MM and Shinohara, RT. 2018. ‘Harmonization of cortical thickness measurements across scanners and sites’. NeuroImage, 167: 104–120. DOI: 10.1016/j.neuroimage.2017.11.024
    Back to article
  60. 60George, D, Lehrach, W, Kansky, K, Lázaro-Gredilla, M, Laan, C, Marthi, B, Lou, X, Meng, Z, Liu, Y, Wang, H, Lavin, A and Phoenix, DS. 2017. ‘A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs’. Science, 358(6368): eaag2612. DOI: 10.1126/science.aag2612
    Back to article
  61. 61Glorot, X and Bengio, Y. 2010. ‘Understanding the difficulty of training deep feedforward neural networks’. Artificial Intelligence and Statistics, 9: 249–256
    Back to article
  62. 62Gong, Y, Liu, G, Xue, Y, Li, R and Meng, L. 2023. ‘A survey on dataset quality in machine learning’. Information and Software Technology, 162: 107268. DOI: 10.1016/j.infsof.2023.107268
    Back to article
  63. 63González-Aguilera, D. 2005. ‘Reconstrucción 3D a partir de una sola vista’. PhD Thesis, Universidad de Salamanca.
    Back to article
  64. 64González-Aguilera, D, López-Fernández, L, Rodriguez-Gonzalvez, P, Hernandez-Lopez, D, Guerrero, D, Remondino, F, Menna, F, Nocerino, E, Toschi, I, Ballabeni, A and Gaiani, M. 2018. ‘Graphos – open-source software for photogrammetric applications’. The Photogrammetric Record, 33(161): 11–19. DOI: 10.1111/phor.12231
    Back to article
  65. 65González-Aguilera, D, Ruiz de Oña, E, López-Fernandez, L, Farella, EM, Stathopoulou, EK, Toschi, I, Romondino, F, Rodríguez-Gonzálvez, P, Hernández-López, D, Fusiello, A and Nex, F. 2020. ‘Photomatch: an open-source multi-view and multi-modal feature matching tolos for photogrammetric applications’. International Archive of Photogrammetry and Remote Snesing Spatial Information Sciences, XLIII(B5): 213–219. DOI: 10.5194/isprs-archives-XLIII-B5-2020-213-2020
    Back to article
  66. 66Goodfellow, IJ, Bengio, Y and Courville, A. 2016. Deep Learning. Cambridge, Massachusetts: MIT Press.
    Back to article
  67. 67Goodfellow, IJ, Schlens, J and Szegedy, C. 2015. ‘Explaining and harnessing adversarial examples’. International Conference on Learning Representations. Available online: https://arxiv.org/pdf/1412.6572.pdf [Last accessed 26/08/2023].
    Back to article
  68. 68Gümrükçu, M and Pante, MC. 2018. ‘Assessing the effects of fluvial abrasion on bone surface modifications using high-resolution 3D scanning’. Journal of Archaeological Science Reports, 21: 208–221. DOI: 10.1016/j.jasrep.2018.06.037
    Back to article
  69. 69Guo, JJ, Shen, DF, Lin, S, Huang, JC, Liu, KC and Lie, WN. 2016. ‘A specular reflection suppression method for endoscopic images’. IEEE Multimedia Big Data, 16: 125–128. DOI: 10.1109/BigMM.2016.78
    Back to article
  70. 70Hartley, R and Zisserman, A. 2003. Multiple view geometry in computer vision. Cambridge: Cambridge University Press. DOI: 10.1017/CBO9780511811685
    Back to article
  71. 71He, H and Ma, Y. 2013. Imbalanced Learning: foundations, algorithms and applications. New Jersey: Wiley. DOI: 10.1002/9781118646106
    Back to article
  72. 72He, K, Girschick, R and Dollár, P. 2018. Rethinking ImageNet pre-training. arXiv. Available online: https://arxiv.org/pdf/1811.08883.
    Back to article
  73. 73He, K, Zhang, X, Ren, S and Sun, J. 2016. ‘Deep residual learning for image recognition’. IEEE Computer Vision and Pattern Recognition, 2016: 770–778. DOI: 10.1109/CVPR.2016.90
    Back to article
  74. 74Huang, G, Liu, Z, Van der Maaten, L and Weinberger, KQ. 2017. ‘Densely connected Convolutional Networks’. IEEE Computer Vision and Pattern Recognition, 2017: 2261–2269. DOI: 10.1109/CVPR.2017.243
    Back to article
  75. 75Ibrahim, R and Shafiq, MO. 2023. ‘Explainable convolutional neural networks: a taxonomy, review, and future directions’. ACM Computing Suverys, 55(10): 206. DOI: 10.1145/3563691
    Back to article
  76. 76Jiménez-García, B, Aznarte, J, Abellán, N, Baquedano, E and Domínguez-Rodrigo, M. 2020a. ‘Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars’. Journal of the Royal Society Interface, 17: 20200446. DOI: 10.1098/rsif.2020.0446
    Back to article
  77. 77Jiménez-García, B, Aznarte, J, Abellán, N, Baquedano, E and Domínguez-Rodrigo, M. 2020b. ‘Corrigendum to Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars’. Journal of the Royal Society Interface, 17: 20200782. DOI: 10.1098/rsif.2020.0782
    Back to article
  78. 78Kaehler, A and Bradski, G. 2017. Learning OpenCV3: Computer Vision in C++ with the OpenCV Library. Tokyo: O’Reilly
    Back to article
  79. 79Kornblith, S, Shlens, J, Le, QV. 2018. ‘Do better ImageNet models transfer better?’ Computer Vision and Pattern Recognition, 2656–2666. DOI: 10.1109/CVPR.2019.00277
    Back to article
  80. 80Kothari, S, Phan, JH, Stokes, TH, Osunkoya, AO, Young, AN and Wang, MD. 2014. ‘Removing batch effects from histopathological images for enhanced cancer diagnosis’. IEEE Journal of Biomedical Health Informatics, 18(3): 765–772. DOI: 10.1109/JBHI.2013.2276766
    Back to article
  81. 81Koziarski, M and Cyganek, B. 2018. ‘Impact of low resolution on image recognition with deep neural networks’. International Journal of Applied Mathematics and Computer Science, 28(4): 735–744. DOI: 10.2478/amcs-2018-0056
    Back to article
  82. 82Krizhevsky, A. 2010. Convolutional deep belief networks on CIFAR-10. Technical report, University of Toronto, Toronto, Canada.
    Back to article
  83. 83Krizhevsky, A and Hinton, G. 2009. Learning multiple layers of features from tiny images. Technical report, University of Toronto, Toronto, Canada.
    Back to article
  84. 84Krizhevsky, A, Sutskever, I and Hinton, G. 2012. ‘ImageNet classification with deep convolutional networks’. Advances in Neural Information Processing Systems, 25: 1–9.
    Back to article
  85. 85Kurakin, A, Goodfellow, IJ and Bengio, S. 2017. ‘Adversarial examples in the physical world’. International Conference of Learning Representations. Available at: https://arxiv.org/pdf/1607.02533.pdf [Last accessed 27/08/2023].
    Back to article
  86. 86Kuzin, A, Fattakhov, A, Kibardin, I, Iglovikov, VI and Dautov, R. 2018. ‘Camera model identification using convolutional neural networks’. arXiv. Available at: https://arxiv.org/pdf/1810.02981.pdf [Last accessed 31/08/2023].
    Back to article
  87. 87Leach, R. 2011. Optical measurement of surface topography. The Netherlands: Springer. DOI: 10.1007/978-3-642-12012-1
    Back to article
  88. 88LeCun, Y, Bengio, Y and Hinton, G. 2015. ‘Deep Learning’. Nature, 521: 436–444. DOI: 10.1038/nature14539
    Back to article
  89. 89LeCun, Y, Bottou, L, Bengio, Y and Haffner, P. 1998. ‘Gradient-based learning applied to document recognition’. Proceedings of the IEEE, 86(11): 2278–2324. DOI: 10.1109/5.726791
    Back to article
  90. 90Lent, M, Fisher, W and Mancuso, M. 2004. ‘An explainable artificial intelligence system for small-unit tactical behaviour’. In: Proceedings of the 16th Conference of Innovated Applications of Artificial Intelligence. pp. 900–907
    Back to article
  91. 91Li, J and Liu, Z. 2018. ‘Efficient camera self-calibration method for remote sensing photogrammetry’. Optics Express, 26(11): 14213–14231. DOI: 10.1364/OE.26.014213
    Back to article
  92. 92Lowe, DG. 2004. ‘Distinctive image features from scale-invariant keypoints’. International Journal of Computer Vision, 60: 91–110. DOI: 10.1023/B:VISI.0000029664.99615.94
    Back to article
  93. 93Madry, A, Makelov, A, Schmidt, L, Tsipras, D and Vladu, A. 2019. ‘Towards deep learning models resistant to adversarial attacks’. International Conference on Learning Representations. Online: https://arxiv.org/pdf/1706.06083.pdf [Last accessed 27/08/2023].
    Back to article
  94. 94Malassé, AD, Moigne, AM, Singh, M, Calligaro, T, Karir, B, Gaillard, C, Kaur, A, Bharwaj, V, Pal, S, Abdessadok, S, Sao, CC, Gargani, J, Tudryn, A and Sanz, MG. 2016. ‘Intentional cut marks on bovid from the Quranwala zone, 2.6 Ma, Siwalik Frontal Range, Northwestern India’. Comptes Rendus Palevol, 15(3–4): 317–339. DOI: 10.1016/j.crpv.2015.09.019
    Back to article
  95. 95Martín-Viveros, JI and Ollé, A. 2020. ‘Use-wear and residue mapping on experiment chert tools. A multiscalar approach combining digital 3D, optical, and scanning electron microscopy’. Journal of Archaeological Science: Reports, 30: 102236. DOI: 10.1016/j.jasrep.2020.102236
    Back to article
  96. 96McPherron, SP, Alemseged, Z, Marean, CW, Wynn, JG, Reed, D, Geraads, D, Bobe, R and Béarat, H. 2010. ‘Evidence for stone-tool-assisted consumption of animal tissues before 3.39 million years ago at Dikika, Ethiopia’. Nature Letters, 466: 857–860. DOI: 10.1038/nature09248
    Back to article
  97. 97McPherron, SP, Archer, W, Otárola-Castillo, ER, Torquato, MG and Keevil, TL. 2022. ‘Machine learning, bootstrapping, null models, and why we are still not 100% sure which bone surface modifications were made by crocodiles’. Journal of Human Evolution, 164: 103071. DOI: 10.1016/j.jhevol.2021.103071
    Back to article
  98. 98Mittal, A, Moorthy, AK and Bovik, AC. 2012. ‘No-reference image quality assessment in the spatial domain’. IEEE Transactions on Image Processing, 21(12): 4695–4708. DOI: 10.1109/TIP.2012.2214050
    Back to article
  99. 99Moclán, A, Domínguez-Rodrigo, M, Huguet, R, Pizzaro-Monzo, M, Arsuaga, JL, Pérez-González, A, Baquedano, E. 2024. ‘Deep learning identification of anthropogenic modifications on a carnivore remain suggests use of hyena pelts by Neanderthals in the Navalmaíllo rock shelter (Pinilla del Valle, Spain)’. Quaternary Science Reviews, 329: 108560. DOI: 10.1016/j.quascirev.2024.108560
    Back to article
  100. 100Morgand, A and Tamaazousti, M. 2014. ‘Generic and real-time detection of specular reflections in images’. IEEE Computer Vision Theory and Applications, 14: 274–282. DOI: 10.5220/0004680102740282
    Back to article
  101. 101Njau, J and Blumenschine, RJ. 2006. ‘A diagnosis of crocodile feeding traces on larger mammal bone, with fossil examples from the Plio-Pleistocene Olduvai Basin, Tanzania’. Journal of Human Evolution, 50(2): 142–162. DOI: 10.1016/j.jhevol.2005.08.008
    Back to article
  102. 102Nguyen, A, Yosinski, J and Clune, J. 2015. ‘Deep neural networks are easily fooled: high confidence predictions for unrecognizable images’. IEEE Computer Vision and Pattern Recognition, 2015: 427–436. DOI: 10.1109/CVPR.2015.7298640
    Back to article
  103. 103Njau, J and Gilbert, H. 2016. ‘Standardizing terms for crocodile-induced bite marks on bone surfaces in light of the frequent bone modification equifinality found to result from crocodile feeding behaviour, stone tool modification, and trampling’. FOROST (Forensic Osteology) Occasional Publications, 3: 1–13
    Back to article
  104. 104Oliver, JS. 1984. ‘Analogues and site context: bone damages from shield trap cave (24CB91), Carbon County, Montana, USA’. In: Bonischen, R and Sorg, MH (Eds.) Bone Modification. Maine: University of Maine Press. 61–72.
    Back to article
  105. 105Pante, MC, Scott, RS, Blumenschine, RJ and Capaldo, SD. 2015. ‘Revalidation of bone surface modification models for inferring fossil hominin and carnivore feeding interactions’. Quaternary International, 355: 164–168. DOI: 10.1016/j.quaint.2014.09.007
    Back to article
  106. 106Pech-Pacheco, JL, Cristóbal, G, Chamorro-Martínez, J and Fernández-Valdivia, J. 2000. ‘Diatom autofocusing in brightfield microscopy: a comparative study’. Proceedings of the 15th International Conference on Pattern Recognition. DOI: 10.1109/ICPR.2000.903548
    Back to article
  107. 107Pertuz, S, Puig, D and Garcia, MA. 2013. ‘Analysis of focus measure operators for shape-from-focus’. Pattern Recognition, 46: 1415–1432. DOI: 10.1016/j.patcog.2012.11.011
    Back to article
  108. 108Pickering, TR. 2013. Rough and Tumble: Aggression, hunting and human evolution. California: University of California Press. DOI: 10.1525/9780520955127
    Back to article
  109. 109Pineda, A, Cáceres, I, Saladié, P, Huguet, R, Morales, JI, Rosas, A and Vallverdú, J. 2019. ‘Tumbling effects on bone surface modifications (BSM): An experimental application on archaeological deposits from the Barranc de la Boella site (Tarragona, Spain)’. Journal of Archaeological Science, 102: 35–47. DOI: 10.1016/j.jas.2018.12.011
    Back to article
  110. 110Pineda, A, Courtenay, LA, Téllez, E and Yravedra, J. 2023. ‘An experimental approach to the analysis of altered cut marks in archaeological contexts from Geometric Morphometrics’. Journal of Archaeological Science: Reports, 48: 103850. DOI: 10.1016/j.jasrep.2023.103850
    Back to article
  111. 111Pineda, A, Saladié, P, Vergès, JM, Huguet, R, Cáceres, I and Vallverdú, J. 2014. ‘Trampling versus cut marks on chemically altered surfaces: An experimental approach and archaeological application at the Barranc de la Boella site (la Canonja, Tarragona, Spain)’. Journal of Archaeological Science, 50: 84–93. DOI: 10.1016/j.jas.2014.06.018
    Back to article
  112. 112Pizarro-Monzo, M and Domínguez-Rodrigo, M. 2020. ‘Dynamic modification of cut marks by trampling: temporal assessment through the use of mixed-effect regressions and Deep learning methods’. Archaeological and Anthropological Sciences, 12: 4. DOI: 10.1007/s12520-019-00966-6
    Back to article
  113. 113Pizarro-Monzo, M, Organista, E, Cobo-Sánchez, L, Baquedano, E and Domínguez-Rodrigo, M. 2022. ‘Determining the diagenetic paths of archaeofaunal assemblages and their palaeoecology through artificial intelligence: an application to Oldowan sites from Olduvai Gorge (Tanzania)’. Journal of Quaternary Science, 37(3): 543–557. DOI: 10.1002/jqs.3385
    Back to article
  114. 114Pizarro-Monzo, M, Rosell, J, Rufá, A, Rivals, F and Blasco, R. 2023. ‘A Deep learning-based taphonomical approach to distinguish the modifying agent in the Late Pleistocene site of Toll Cova (Barcelona, Spain)’. Historical Biology, DOI: 10.1080/08912963.2023.2242370
    Back to article
  115. 115Pobiner, BL. 2020. ‘The zooarchaeology and paleoecology of early hominin scavenging’. Evolutionary Anthropology, 29: 68–82. DOI: 10.1002/evan.21824
    Back to article
  116. 116Raghu, M, Zhang, C, Kleinberg, J and Bengio, S. 2019. ‘Transfusion: understanding transfer learning for medical imaging’. Neural Information Processing Systems, 33: 1–11
    Back to article
  117. 117Ray, I, Raipuria, G and Singhal, N. 2022. ‘Rethinking ImageNet Pre-training for computational histopathology’. IEEE Engineering in Medicine and Biology Society, 44: 3059–3062. DOI: 10.1109/EMBC48229.2022.9871687
    Back to article
  118. 118Redmon, J, Divvala, S, Girshick, R and Farhadi, A. 2016. ‘You only look once: Unified, real-time object detection’. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 779–788. DOI: 10.1109/CVPR.2016.91
    Back to article
  119. 119Reeves, NM. 2009. ‘Taphonomic effects of vulture scavenging’. Journal of Forensic Sciences, 54(3): 503–528. DOI: 10.1111/j.1556-4029.2009.01020.x
    Back to article
  120. 120Roberts, M, Driggs, D, Thorpe, M, Gilbey, J, Yeung, M, Ursprung, S, Aviles-Rivero, AI, Etmann, C, McCague, C, Beer, L, Weir-McCall, JR, Teng, Z, Gkrania-Klotsas, E, Rudd, JHF, Sala, E and Schönlieb, CB. 2021. ‘Common pifalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans’. Nature Machine Intelligence, 3: 199–217. DOI: 10.1038/s42256-021-00307-0
    Back to article
  121. 121Rosebrock, A. 2021. Practical Python and OpenCV. Toronto: PyImageSearch.
    Back to article
  122. 122Ruiz de Oña, E, Barbero-Garcia, I, González-Aguilera, D, Remondino, F, Rodríguez-Gonálvez, P and Hernández-López, D. 2023. ‘PhotoMatch: An open source tool for multi-view and multi-modal feature based image matching’. Applied Sciences, 113(9): 5467. DOI: 10.3390/app13095467
    Back to article
  123. 123Sahle, Y, El Zaatari, S and White, TD. 2017. ‘Hominid butchers and biting crocodiles in the African Plio-Pleistocene’. Proceedings of the National Academy of Sciences, 114: 13164–13169. DOI: 10.1073/pnas.1716317114
    Back to article
  124. 124Santoro, A, Barunov, S, Botvinick, M, Wierstra, D and Lillicrap, T. 2016. ‘Meta-learning with memory augmented neural networks’. Proceedings of the International Conference on Machine Learning, 33: 1–9.
    Back to article
  125. 125Schenk, T. 1999. Digital Photogrammetry. Marcombo: The Ohio State University.
    Back to article
  126. 126Selvaggio, MM. 1998. ‘Concerning the three stage model of carcass processing at FLK Zinjanthropus: a reply to Capaldo’. Journal of Human Evolution, 35: 313–315. DOI: 10.1006/jhev.1998.0241
    Back to article
  127. 127Selvaraju, RR, Cogswell, M, Das, A, Vedantam, R, Parikh, D and Batra, D. 2019. Grad-CAM: Visual explanations from deep networks via Gradient-based localization. Available at: https://arxiv.org/pdf/1610.02391.pdf [Last accessed 30/08/2023].
    Back to article
  128. 128Selvaraju, RR, Das, A, Vedantam, R, Cogswell, M, Parikh, D and Batra, D. 2017. Grad-CAM: Why did you say that? Available at: https://arxiv.org/pdf/1611.07450.pdf [Last accessed 30/08/2023].
    Back to article
  129. 129Shanmugavel, AB, Ellappan, V, Mahendran, A, Subramanian, M, Lakshmanan, R, Mazzara, M. 2023. ‘A novel ensemble based reduced overfitting model with convolutional neural network for traffic sign recognition system’. Electronics, 12(4): 926. DOI: 10.3390/electronics12040926
    Back to article
  130. 130Shipman, P. 1988. ‘Actualistic studies of animal resources and hominid activities’. In: Olsen, SL (Ed.), Scanning Electron Microscopy in Archaeology. Oxford: BAR International Series 452. 261–285.
    Back to article
  131. 131Shipman, P and Rose, J. 1983. ‘Evidence of butchery and hominid activities at Torralba and Ambrona: an evaluation using microscopic techniques’. Journal of Archaeological Science, 10(5): 465–474. DOI: 10.1016/0305-4403(83)90061-4
    Back to article
  132. 132Shipman, P and Walker, A. 1989. ‘The costs of becoming a predator’. Journal of Human Evolution, 18: 373–392. DOI: 10.1016/0047-2484(89)90037-7
    Back to article
  133. 133Simonyan, K and Zisserman, A. 2015. ‘Very deep convolutional networks for large-scale image recognition’. International Conference on Learning Representations, 3: 1–14. Available online: https://arxiv.org/pdf/1409.1556.pdf [Last accessed 28/08/2023].
    Back to article
  134. 134Srinivasa-Desikan, B. 2018. Natural Language Processing and Computational Linguistics. Birmingham: Packt
    Back to article
  135. 135Su, J, Vargas, DV and Sakurai, K. 2019. ‘One pixel attack for fooling Deep neural networks’. IEEE Transactions on Evolutionary Computation, 23(5): 828–841. DOI: 10.1109/TEVC.2019.2890858
    Back to article
  136. 136Szegedy, C, Liu, W, Jia, Y, Sermanet, P, Reed, S, Anguelov, D, Ehran, D, Vanhoucke, V and Rabinovich, A. 2015. ‘Going deeper with convolutions’. IEEE Computer Vision and Pattern Recognition, 2015: 1–9. DOI: 10.1109/CVPR.2015.7298594
    Back to article
  137. 137Szegedy, C, Vanhoucke, V, Ioffe, S, Shlens, J and Wojna, Z. 2016. Rethinking the inception architecture for computer vision. IEEE Computer Vision and Pattern Recognition, 2016: 2818–2826. DOI: 10.1109/CVPR.2016.308
    Back to article
  138. 138Szegedy, C, Zaremba, W, Sutskever, I, Bruna, J, Erhan, D, Goodfellow, I and Fergus, R. 2014. ‘Intriguing properties of neural networks’. International Conference of Learning Representations. Available online: https://arxiv.org/pdf/1312.6199.pdf [Last accessed 26/08/2023].
    Back to article
  139. 139Tan, M and Le, QV. 2020. ‘EfficientNet: Rethinking model scaling for convolutional neural networks’. Proceedings of the International Conference on Machine Learning, 36: 1–11. Available online: https://arxiv.org/pdf/1905.11946.pdf [Last accessed 30/10/2023].
    Back to article
  140. 140Tian, J and Chen, Li. 2012. ‘Adaptive multi-focus image fusion using a wavelet-based statistical sharpness measure’. Signal Processing, 92: 2137–2146. DOI: 10.1016/j.sigpro.2012.01.027
    Back to article
  141. 141Tuama, A, Comby, F and Chaumont, M. 2016. ‘Camera model identification with the use of deep convolutional neural networks’. In: Proceedings of the IEEE International Workshop on Information Forensics and Security. 1–16. DOI: 10.1109/WIFS.2016.7823908
    Back to article
  142. 142Valtierra, N, Courtenay, LA and López-Polín, L. 2020. ‘Microscopic analyses of the effects of mechanical cleaning interventions on cut marks’. Archaeological and Anthropological Sciences, 12: 193. DOI: 10.1007/s12520-020-01153-8
    Back to article
  143. 143Vedantam, R, Lin, X, Batra, T, Zitnick, CL and Parikh, D. 2015. ‘Learning common sense through visual abstraction’. IEEE International Conference on Computer Vision, 2542–2550. DOI: 10.1109/ICCV.2015.292
    Back to article
  144. 144Vegara-Riquelme, M, Gidna, A, Uribelarrea del Val, D, Baquedano, E and Domínguez-Rodrigo, M. 2023. ‘Reassessing the role of carnivores in the formation of FLK North 3 (Olduvai Gorge, Tanzania): A pilot taphonomic analysis using Artificial Intelligence Tools’. Journal of Archaeological Science: Reports, 47: 103736. DOI: 10.1016/j.jasrep.2022.103736
    Back to article
  145. 145Vinyals, O, Toshev, A, Bengio, S and Erhan, D. 2015. ‘Show and Tell: A Neural Image Caption Generator’. IEEE Computer Vision and Pattern Recognition Conference, 2015: 3156–3164. DOI: 10.1109/CVPR.2015.7298935
    Back to article
  146. 146Wang, Z, Sheikh, HR and Bovik, AC. 2002. ‘No-reference perceptual quality assessment of JPEG compressed images’. International Conference on Image Processing, 477–480. DOI: 10.1109/ICIP.2002.1038064
    Back to article
  147. 147Wolpert, DH. 1992. ‘Stacked Generalization’. Neural Networks, 5: 241–259. DOI: 10.1016/S0893-6080(05)80023-1
    Back to article
  148. 148Yezzi-Woodley, K, Terwilliger, A, Li, J, Chen, E, Tappen, M, Calder, J and Olver, P. 2024. ‘Using machine learning on new feature sets extracted from three-dimensional models of broken animal bones to classify fragments according to break agent’. Journal of Human Evolution, 187: 103495. DOI: 10.1016/j.jhevol.2024.103495
    Back to article
  149. 149Yokinski, J, Clune, J, Bengio, Y and Lipson, H. 2014. ‘How transferable are features in deep neural networks’. Advances in Neural Information Processing Systems, 27: 1–14.
    Back to article
  150. 150Zhang, C and Bastian, J, Shen, C, Hengel, A and Shen, T. 2013. ‘Extended depth-of-field via focus stacking and graph cuts’. IEEE International Conference on Image Processing, 1272–1276. DOI: 10.1109/ICIP.2013.6738262
    Back to article
  151. 151Zhou, ZH. 2012. Ensemble Methods. New York: Chapman & Hall. DOI: 10.1201/b12207
    Back to article
  152. 152Zitová, B and Flusser, J. 2003. ‘Image registration methods. A survey’. Image Visual Computation, 21: 977–1000. DOI: 10.1016/S0262-8856(03)00137-9
    Back to article
  153. 153Zou, WWW and Yuen, PC. 2012. ‘Very low face recognition problem’. IEEE Transaction on image processing, 21(1): 327–340. DOI: 10.1109/TIP.2011.2162423
    Back to article
DOI: https://doi.org/10.5334/jcaa.145 | Journal eISSN: 2514-8362
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Language: English
Submitted on: Dec 20, 2023
Accepted on: Oct 10, 2024
Published on: Dec 10, 2024
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Artificial Intelligence,
Computer Vision,
Cut Marks,
Carnivoran Tooth Marks,
Trampling Marks,
Crocodylian Tooth Marks

© 2024 Lloyd Austin Courtenay, Nicolas Vanderesse, Luc Doyon, Antoine Souron, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

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