References |
: |
[2]Liu DH, Lam KM, Shen LS. Illumination invariant face recognition. Pattern Recognition. 2005; 38(10):1705-16.
|
[Crossref] |
[Google Scholar] |
[4]Primack H, Schanz H, Smilansky U, Ussishkin I. Penumbra diffraction in the quantization of dispersing billiards. Physical Review Letters. 1996; 76(10).
|
[Crossref] |
[Google Scholar] |
[5]Finlayson GD, Hordley SD, Drew MS. Removing shadows from images. In European conference on computer vision 2002 (pp. 823-36). Springer, Berlin, Heidelberg.
|
[Crossref] |
[Google Scholar] |
[6]Wu TP, Tang CK. A bayesian approach for shadow extraction from a single image. In tenth IEEE international conference on computer vision 2005 (pp. 480-7). IEEE.
|
[Crossref] |
[Google Scholar] |
[7]Xu L, Qi F, Jiang R. Shadow removal from a single image. In sixth international conference on intelligent systems design and applications 2006 (pp. 1049-54). IEEE.
|
[Crossref] |
[Google Scholar] |
[8]Mamassian P, Knill DC, Kersten D. The perception of cast shadows. Trends in Cognitive Sciences. 1998; 2(8):288-95.
|
[Crossref] |
[Google Scholar] |
[9]Salvador E, Cavallaro A, Ebrahimi T. Shadow identification and classification using invariant color models. In international conference on acoustics, speech, and signal processing proceedings 2001 (pp. 1545-8). IEEE.
|
[Crossref] |
[Google Scholar] |
[10]Mohan A, Tumblin J, Choudhury P. Editing soft shadows in a digital photograph. IEEE Computer Graphics and Applications. 2007; 27(2):23-31.
|
[Crossref] |
[Google Scholar] |
[11]Sanin A, Sanderson C, Lovell BC. Shadow detection: a survey and comparative evaluation of recent methods. Pattern Recognition. 2012; 45(4):1684-95.
|
[Crossref] |
[Google Scholar] |
[12]Prati A, Mikic I, Trivedi MM, Cucchiara R. Detecting moving shadows: algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2003; 25(7):918-23.
|
[Crossref] |
[Google Scholar] |
[13]Rela M, Rao SN, Patil RR. Performance analysis of liver tumor classification using machine learning algorithms. International Journal of Advanced Technology and Engineering Exploration. 2022; 9(86): 143-54.
|
[Crossref] |
[Google Scholar] |
[14]Dubey A, Gupta U, Jain S. Medical data clustering and classification using TLBO and machine learning algorithms. Computers, Materials and Continua. 2021; 70(3):4523-43.
|
[Crossref] |
[Google Scholar] |
[15]Xu M, Zhu J, Lv P, Zhou B, Tappen MF, Ji R. Learning-based shadow recognition and removal from monochromatic natural images. IEEE Transactions on Image Processing. 2017; 26(12):5811-24.
|
[Crossref] |
[Google Scholar] |
[16]Gao J, Dai J, Zhang P. Region-based moving shadow detection using watershed algorithm. In international symposium on computer, consumer and control (IS3C) 2016 (pp. 846-9). IEEE.
|
[Crossref] |
[Google Scholar] |
[17]Li H, Zhang L, Shen H. An adaptive nonlocal regularized shadow removal method for aerial remote sensing images. IEEE Transactions on Geoscience and Remote Sensing. 2013; 52(1):106-20.
|
[Crossref] |
[Google Scholar] |
[18]Hsieh JW, Hu WF, Chang CJ, Chen YS. Shadow elimination for effective moving object detection by Gaussian shadow modeling. Image and Vision Computing. 2003; 21(6):505-16.
|
[Crossref] |
[Google Scholar] |
[19]Yoneyama A, Yeh CH, Kuo CC. Moving cast shadow elimination for robust vehicle extraction based on 2D joint vehicle/shadow models. In proceedings of the IEEE conference on advanced video and signal based surveillance, 2003 (pp. 229-36). IEEE.
|
[Crossref] |
[Google Scholar] |
[20]Yoneyama A, Yeh CH, Kuo CC. Robust vehicle and traffic information extraction for highway surveillance. EURASIP Journal on Advances in Signal Processing. 2005; 2005(14):1-17.
|
[Crossref] |
[Google Scholar] |
[21]Nicolas H, Pinel JM. Joint moving cast shadows segmentation and light source detection in video sequences. Signal processing: Image Communication. 2006; 21(1):22-43.
|
[Crossref] |
[Google Scholar] |
[22]Nemade V, Pathak S, Dubey AK. A systematic literature review of breast cancer diagnosis using machine intelligence techniques. Archives of Computational Methods in Engineering. 2022:1-30.
|
[Crossref] |
[Google Scholar] |
[23]He Z, Zhang Z, Guo M, Wu L, Huang Y. Adaptive unsupervised-shadow-detection approach for remote-sensing image based on multichannel features. Remote Sensing. 2022; 14(12):1-25.
|
[Crossref] |
[Google Scholar] |
[24]Dubey AK, Kumar A, Agrawal R. An efficient ACO-PSO-based framework for data classification and preprocessing in big data. Evolutionary Intelligence. 2021; 14(2):909-22.
|
[Crossref] |
[Google Scholar] |
[25]Ghewari T, Khot SR, Khatavkar MD. Analysis of model based shadow detection and removal in color images. In third international conference on inventive systems and control (ICISC) 2019 (pp. 508-13). IEEE.
|
[Crossref] |
[Google Scholar] |
[26]Hanafy WA, Pina A, Salem SA. Machine learning approach for photovoltaic panels cleanliness detection. In 15th international computer engineering conference (ICENCO) 2019 (pp. 72-7). IEEE.
|
[Crossref] |
[Google Scholar] |
[27]Khan S, Pirani Z, Fansupkar T, Maghrabi U. Shadow removal from digital images using multi-channel binarization and shadow matting. In 2019 third international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) 2019 (pp. 723-8). IEEE.
|
[Crossref] |
[Google Scholar] |
[28]Sidorov O. Conditional gans for multi-illuminant color constancy: revolution or yet another approach?. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops 2019.
|
[Google Scholar] |
[29]Zhang Y, Wen F, Gao Z, Ling X. A coarse-to-fine framework for cloud removal in remote sensing image sequence. IEEE Transactions on Geoscience and Remote Sensing. 2019; 57(8):5963-74.
|
[Crossref] |
[Google Scholar] |
[30]Talavera-martinez L, Bibiloni P, Gonzalez-hidalgo M. Hair segmentation and removal in dermoscopic images using deep learning. IEEE Access. 2020; 9:2694-704.
|
[Crossref] |
[Google Scholar] |
[31]Ji S, Dai P, Lu M, Zhang Y. Simultaneous cloud detection and removal from bitemporal remote sensing images using cascade convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59(1):732-48.
|
[Crossref] |
[Google Scholar] |
[32]Guo R, Ayinde B, Sun H. Efficient shadow detection and removal using synthetic data with domain adaptation. In 25th international conference on pattern recognition (ICPR) 2021 (pp. 5867-74). IEEE.
|
[Crossref] |
[Google Scholar] |
[33]Kim YC, Bae TW, Ahn SH. Background subtraction with shadow removal using hue and texture model for moving object detection. In international conference on electronics, information, and communication 2020 (pp. 1-2). IEEE.
|
[Crossref] |
[Google Scholar] |
[34]Batchuluun G, Baek NR, Nguyen DT, Pham TD, Park KR. Region-based removal of thermal reflection using pruned fully convolutional network. IEEE Access. 2020; 8:75741-60.
|
[Crossref] |
[Google Scholar] |
[35]Gad A, Yaghi M, Alkhedher M, Ghazal M. Real-time shadow detection and removal by illumination drop point analysis. In international conference on innovation and intelligence for informatics, computing and technologies 2020 (pp. 1-5). IEEE.
|
[Crossref] |
[Google Scholar] |
[36]Luo S, Li H, Shen H. Deeply supervised convolutional neural network for shadow detection based on a novel aerial shadow imagery dataset. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 167:443-57.
|
[Crossref] |
[Google Scholar] |
[37]Kurbatova E, Pavlovskaya Y. Shaded roads detection based on contour segmentation. In international conference on digital signal processing and its applications 2020 (pp. 1-4). IEEE.
|
[Crossref] |
[Google Scholar] |
[38]Wang C, Xu H, Zhou Z, Deng L, Yang M. Shadow detection and removal for illumination consistency on the road. IEEE Transactions on Intelligent Vehicles. 2020; 5(4):534-44.
|
[Crossref] |
[Google Scholar] |
[39]Gound RS, Thepade SD. Removing haze influence from remote sensing images captured with airborne visible/infrared imaging spectrometer by cascaded fusion of DCP, GF, LCC with AHE. In international conference on computing, communication, and intelligent systems 2021 (pp. 658-64). IEEE.
|
[Crossref] |
[Google Scholar] |
[40]He S, Peng B, Dong J, Du Y. Mask-ShadowNet: toward shadow removal via masked adaptive instance normalization. IEEE Signal Processing Letters. 2021; 28:957-61.
|
[Crossref] |
[Google Scholar] |
[41]Hongjuan Y, Decai M, Yunchu Z. Preprocessing of automobile engine connecting rod based on shadow removal and image enhancement. In international conference on communications, information system and computer engineering 2021 (pp. 428-32). IEEE.
|
[Crossref] |
[Google Scholar] |
[42]Hu Y, Liu W. Shadow elimination based on multiple feature differences and glvq. In 13th international conference on measuring technology and mechatronics automation 2021(pp. 872-7). IEEE.
|
[Crossref] |
[Google Scholar] |
[43]Kim D, Kim J. Non-local self-attention mechanism for real-time context embedding deep shadow removal network. In 2021 international conference on information networking 2021 (pp. 43-5). IEEE.
|
[Crossref] |
[Google Scholar] |
[44]Sahoo S, Nanda PK. Adaptive feature fusion and spatio-temporal background modeling in KDE framework for object detection and shadow removal. IEEE Transactions on Circuits and Systems for Video Technology. 2021; 32(3):1103-18.
|
[Crossref] |
[Google Scholar] |
[45]Sarker N, Chaki S, Das A, Forhad MS. Illegal trash thrower detection based on hogsvm for a real-time monitoring system. In 2nd international conference on robotics, electrical and signal processing techniques 2021 (pp. 483-7). IEEE.
|
[Crossref] |
[Google Scholar] |
[46]Trapal DD, Leong BC, Ng HW, Zhong JT, Srigrarom S, Chan TH. Improvement of vision-based drone detection and tracking by removing cluttered background, shadow and water reflection with super resolution. In 6th international conference on control and robotics engineering 2021 (pp. 162-8). IEEE.
|
[Crossref] |
[Google Scholar] |
[47]Srinivasan D., Seow. T.H.: evolutionary computation, CEC ’03, 8–12. 4(2003), Canberra, Australia, 2003:2292–7.
|
|