Image Classification Approaches for Segregation of Plastic Waste Based on Resin Identification Code
Tóm tắt
Từ khóa
Tài liệu tham khảo
Altae-Tran H, Ramsundar B, Pappu AS, Pande V (2017) Low data drug discovery with one-shot learning. ACS Central Sci 3(4):283–293
Bay H, Tuytelaars T, Gool LV (2006) Surf: speeded up robust features. In: Leonardis A, Bischof H, Pinz A (eds) Computer vision–ECCV 2006. Springer, Berlin, pp 404–417
Bezdek JC (1981) Objective function clustering. In: Pattern recognition with fuzzy objective function algorithms, p 43–93. Springer
Bobulski J, Kubanek M (2019) Waste classification system using image processing and convolutional neural networks. In: International work-conference on artificial neural networks, p 350–361. Springer
Bobulski J, Piatkowski J (2017) Pet waste classification method and plastic waste database-wadaba. In: International conference on image processing and communications, p 57–64. Springer
Bromley J, Bentz JW, Bottou L, Guyon I, LeCun Y, Moore C, Säckinger E, Shah R (1993) Signature verification using a “siamese’’ time delay neural network. Int J Pattern Recognit Artif Intell 7(04):669–688
Calonder M, Lepetit V, Strecha C, Fua P (2010) Brief: binary robust independent elementary features. In: European conference on computer vision, p 778–792. Springer
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In : 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 1, p 886–893
Detroja KP, Gudi RD, Patwardhan SC (2006) A possibilistic clustering approach to novel fault detection and isolation. J Process Control 16(10):1055–1073
Detroja KP, Gudi RD, Patwardhan SC, Roy K (2006) Fault detection and isolation using correspondence analysis. Ind Eng Chem Res 45(1):223–235
Dong X, Shen J (2018) Triplet loss in siamese network for object tracking. In: Proceedings of the European conference on computer vision (ECCV), p 459–474
Fei-Fei L, Fergus R, Perona P (2006) One-shot learning of object categories. IEEE Trans Pattern Anal Mach Intell 28(4):594–611
Fulton M, Hong J, Jahidul MI, Sattar J (2019) Robotic detection of marine litter using deep visual detection models. In: 2019 international conference on robotics and automation (ICRA), p 5752–5758. IEEE
Goulding PR, Lennox B, Sandoz DJ, Smith KJ, Marjanovic O (2000) Fault detection in continuous processes using multivariate statistical methods. Int J Syst Sci 31(11):1459–1471
Hong J, Fulton M, Sattar J (2020) Trashcan: A semantically-segmented dataset towards visual detection of marine debris. arXiv preprint arXiv:2007.08097
Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24(6):417
Huang J, Pretz T, Bian Z (2010) Intelligent solid waste processing using optical sensor based sorting technology. In: 2010 3rd international congress on image and signal processing, vol 4, p 1657–1661. IEEE
PolyChem (2017) Plastic coding system guide for resin types. https://polychemusa.com/plastic-coding-system/. Accessed 29 Apr 2020
Koch G, Zemel R, Salakhutdinov R (2015) Siamese neural networks for one-shot image recognition. In: ICML deep learning workshop, vol 2. Lille
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105
Lake B, Salakhutdinov R, Gross J, Tenenbaum J (2011) One shot learning of simple visual concepts. In Proceedings of the annual meeting of the cognitive science society, vol 33
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Lynch S (2018) Openlittermap. com—open data on plastic pollution with blockchain rewards (littercoin). Open Geospat Data Softw Stand 3(1):1–10
Nomikos P, MacGregor JF (1995) Multivariate SPC charts for monitoring batch processes. Technometrics 37(1):41–59
Palatucci M, Pomerleau D, Hinton GE, Mitchell TM (2009) Zero-shot learning with semantic output codes
Pearson K (1901) Liii. on lines and planes of closest fit to systems of points in space. Lond Edinb Dublin Philos Mag J Sci 2(11):559–572
Proença PF, Simões P (2020) Taco: Trash annotations in context for litter detection. arXiv preprint arXiv:2003.06975
Quiroga F, Ronchetti F, Lanzarini L, Bariviera AF (2018) Revisiting data augmentation for rotational invariance in convolutional neural networks. In: International conference on modelling and simulation in management sciences, p 127–141. Springer
Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. In: European conference on computer vision, p 430–443. Springer
Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 815–823
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Thung G, Yang M (2016) Trashnet. GitHub repository
Tjhi W-C, Chen L (2007) Possibilistic fuzzy co-clustering of large document collections. Pattern Recognit 40(12):3452–3466
Vinyals O, Blundell C, Lillicrap T, Kavukcuoglu K, Wierstra D (2016) Matching networks for one shot learning. arXiv preprint arXiv:1606.04080
Wahab DA, Hussain A, Scavino E, Mustafa MM, Basri H (2006) Development of a prototype automated sorting system for plastic recycling. Am J Appl Sci 3(7):1924–1928
Wang J, Song Y, Leung T, Rosenberg C, Wang J, Philbin J, Chen B, Wu Y (2014) Learning fine-grained image similarity with deep ranking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 1386–1393
Wilhelm R (2016) Resin identification codes—new astm standard based on society of the plastics industry code will facilitate recycling. Standardization News (September/October 2008). ASTM International