PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Current research opportunities for image processing and computer vision

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Image processing and computer vision is an important and essential area in today’s scenario. Several problems can be solved through computer vision techniques. There are a large number of challenges and opportunities which require skills in the field of computer vision to address them. Computer vision applications cover each band of the electromagnetic spectrum and there are numerous applications in every band. This article is targeted to the research students, scholars and researchers who are interested to solve the problems in the field of image processing and computer vision. It addresses the opportunities and current trends of computer vision applications in all emerging domains. The research needs are identified through available literature survey and classified in the corresponding domains. The possible exemplary images are collected from the different repositories available for research and shown in this paper. The opportunities mentioned in this paper are explained through the images so that a naive researcher can understand it well before proceeding to solve the corresponding problems. The databases mentioned in this article could be useful for researchers who are interested in further solving the problem. The motivation of the article is to expose the current opportunities in the field of image processing and computer vision along with corresponding repositories. Interested researchers who are working in the field can choose a problem through this article and can get the experimental images through the cited references for working further.
Wydawca
Czasopismo
Rocznik
Strony
387--410
Opis fizyczny
Bibliogr. 114 poz., rys., tab.
Twórcy
  • School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu, and Kashmir
Bibliografia
  • [1] 3D Volumetric Data in Slicer. https://www.slicer.org.
  • [2] Adao T., Hruska J., Padua L., Bessa J., Peres E., Morais R., Sousa J.J.: Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry, Remote Sensing, vol. 9(11), p. 1110,2017.
  • [3] Aguate F.M., Trachsel S., Perez L.G., Burgueno J., Crossa J., Balzarini M.,Gouache D., Bogard M., Campos de los G.:. Use of Hyperspectral Image Data Outperforms Vegetation Indices in Prediction of Maize Yield, Crop Science,vol. 57(5), pp. 2517–2524, 2017. https://doi.org/10.2135/cropsci2017.01.0007.
  • [4] Aldave I.J., Bosom P.V., Gonzalez L.V., Lopez de Santiago I., Vollheim B., Krausz L., Georges M.: Review of thermal imaging systems in composite defectdetection, Infrared Physics&Technology, vol. 61, pp. 167–175, 2013. https://doi.org/10.1016/j.infrared.2013.07.009.
  • [5] Al-Turki T.A., Baskin C.C.: Determination of seed viability of eight wild Saudi Arabian species by germination and X-ray tests, Saudi Journal of Biological Sciences, vol. 24, pp. 822–829, 2017.
  • [6] Araujo T., Aresta G., Castro E., Rouco J., Aguiar P., Eloy C., Polonia A.: Classification of breast cancer histology images using Convolutional Neural Networks, PLOS ONE, vol. 12, p. e0177544, 2017. https://doi.org/10.1371/journal.pone.0177544.
  • [7] Aresta G. et al.: BACH: Grand challenge on breast cancer histology images, Medical Image Analysis, vol. 56, pp. 122–139, 2019.
  • [8] Arica N., Yarman-Vural F.T.: An overview of character recognition focusedon off-line handwriting,IEEE Transactions on Systems, Man, and Cybernetics,Part C (Applications and Reviews), vol. 31(2), pp. 216–233, 2001. https://doi.org/10.1109/5326.941845.
  • [9] ASU-Mayo Clinic Colonoscopy Video (c) Database. https://polyp.grand--challenge.org/AsuMayo/
  • [10] Automatic Cephalometric X-ray Landmark Detection Challenge 2014. http://www-o.ntust.edu.tw/∼cweiwang/celph/.
  • [11] Bahadure N.B., Ray A.K., Thethi H.P.: Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM,International Journal of Biomedical Imaging, vol. 2017, p. 12, 2017. https://doi.org/10.1155/2017/9749108.
  • [12] Billah M.M., Rabbani M.A.E., bu Alimuzzaman T.M., Automatic recognitionof pulse crops using image processing, Research in Agriculture Livestock and Fisheries, vol. 2(2), pp. 215–220, 2015. https://doi.org/10.3329/ralf.v2i2.25001.
  • [13] Blasco J., Aleixos N., Gomez J., Molto E.: Citrus sorting by identification of the most common defects using multispectral computer vision, Journal of Food Engineering, vol. 83(3), pp. 384–393, 2007.
  • [14] Bogunovic H. et al.: RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge, IEEE Transactions on Medical Imaging, vol. 38(8), pp. 1858–1874, 2019.
  • [15] Brain MRI Images for Brain Tumor Detection. https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection.
  • [16] Cardone D., Merla A.: New Frontiers for Applications of Thermal Infrared Imaging Devices: Computational Psychopshysiology in the Neurosciences, Sensors (Basel), vol. 17(5), p. E1042, 2017. https://doi.org/10.3390/s17051042.
  • [17] Chen X., Flynn P.J., Bowyer K.W.: IR and visible light face recognition, Computer Vision and Image Understanding, vol. 99(3), pp. 332–358, 2005.https://doi.org/10.1016/j.cviu.2005.03.001.
  • [18] Chen B., Kitasaka T., Honma H., Takabatake H., Mori M., Natori H., Mori K.:Automatic segmentation of pulmonary blood vessels and nodules based on local intensity structure analysis and surface propagation in 3D chest CT images,International Journal of Computer Assisted Radiology and Surgery, vol. 7,pp. 465–482, 2012. https://doi.org/10.1007/s11548-011-0638-5.
  • [19] Codella N.C.F., Gutman D., Celebi M.E., Helba B., Marchetti M.A.,Dusza S.W., Kalloo A., Liopyris K., Mishra N., Kittler H., Halpern A.:Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC), CoRR, vol. abs/1710.05006, 2017.https://arxiv.org/abs/1710.05006.
  • [20] Cui Y., Yuan C., Ji Z.: A review of microwave-induced thermoacoustic imaging: Excitation source, data acquisition system and biomedical applications, Journal of Innovative Optical Health Sciences, vol. 10(04), p. 1730007, 2017. https://doi.org/10.1142/S1793545817300075.
  • [21] Cunha J.B.: Application of image processing techniques in the characterization of plant leafs. In: 2003 IEEE International Symposium on Industrial Electronics(Cat. No.03TH8692), vol. 1, pp. 612–616, 2003. https://doi.org/10.1109/ISIE.2003.1267322.
  • [22] Daqi X., Guoqiang N., Tao J., Lili J., Mingmin C.: Integration of field workand hyperspectral data for oil and gas exploration. In: 2007 IEEE International Geoscience and Remote Sensing Symposium, pp. 3194–3197, 2007.
  • [23] Data Science Bowl Cardiac Challenge Data, https://www.kaggle.com/c/second-annual-data-science-bowl/data.
  • [24] De Sanctis V., Di Maio S., Soliman A.T., Raiola G., Elalaily R., Millimaggi G.: Hand X-ray in pediatric endocrinology: Skeletal age assessment and beyond, Indian Journal of Endocrinology and Metabolism, vol. 18(7), pp. 63–71, 2014.
  • [25] Dehkordi A.L., Seiiedlou S., Golmohammadi S.: Detecting bruises on applesusing ultraviolet (UV) imaging for grading purposes, International Journal of Biosciences (IJB), vol. 4(1), pp. 220–224, 2014. http://www.innspub.net/wp-content/uploads/2013/12/IJB-V4No1-p220-224.pdf.
  • [26] Despotovic I., Goossens B., Philips W.: MRI Segmentation of the Human Brain:Challenges, Methods, and Applications, Computational and Mathematical Methods in Medicine, vol. 2015, p. 23, 2015. https://doi.org/10.1155/2015/450341.
  • [27] Dubosclard P., Larnier S., Konik H., Herbulot A., Devy M.: Automatic Methodfor Visual Grading of Seed Food Products. In: Campilho A., Kamel M. (eds.), Image Analysis and Recognition. ICIAR 2014, Lecture Notes in Computer Science, vol. 8814, Springer, Cham, pp. 485–495, 2014.
  • [28] Dumoulin J. Criniere A.:Infrared Thermography applied to transport infrastructures monitoring: outcomes and perspectives, SPIE – Thermosense: ThermalInfrared Applications XXXIX, Apr 2017.
  • [29] El-Gamal F.E.-Z.A., Elmogy M., Atwan A.: Current trends in medical imageregistration and fusion, Egyptian Informatics Journal, vol. 17(1), pp. 99–124, 2016.
  • [30] Finding and Measuring Lungs in CT Data. https://www.kaggle.com/kmader/finding-lungs-in-ct-data.
  • [31] Gade R., Moeslund T.B.: Constrained multi-target tracking for team sportsactivities, IPSJ Transactions on Computer Vision and Applications, vol. 10(1),2018. http://www.doi.org/10.1186/s41074-017-0038-z, https://www.kaggle.com/aalborguniversity/thermal-soccer-dataset/data.
  • [32] Gil Gonzalez J., Alvarez M.A., Orozco A.A.: Automatic segmentation of nerve structures in ultrasound images using Graph Cuts and Gaussian processes, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3089–3092, 2015.
  • [33] Gotra A., Sivakumaran L., Chartrand G., Vu K.-N., Vandenbroucke-Menu F., Kauffmann C., Kadoury S., Gallix B., de Guise J.A., Tang A.: Liver segmentation: indications, techniques and future directions, Insights into Imaging, vol. 8,pp. 377–392, 2017.
  • [34] Gruber F., Wollmann P., Schumm B., Grahlert W., Kaskel S.: Quality Control of Slot-Die Coated Aluminum Oxide Layers for Battery Applications Using Hyperspectral Imaging, Journal of Imaging, vol. 2(2), p. 12, 2016. https://doi.org/10.3390/jimaging2020012.
  • [35] Gupta A.: Challenges for Computer Aided Diagnostics using X-Ray and Tomographic Reconstruction Images in craniofacial applications, International Journal of Computational Vision and Robotics, In-press, pp. 1–12, 2019.
  • [36] Gupta A., Sardana H.K., Kharbanda O.P., Sardana V.: Method for automatic detection of anatomical landmarks in volumetric data, US PatentUS10318839B2, 2019.
  • [37] Gupta A., Kharbanda O., Sardana V., Balachandran R., Sardana H.:A knowledge-based algorithm for automatic detection of cephalometric land-marks on CBCT images, International Journal of Computer Assisted Radiologyand Surgery, vol. 10, pp. 1737–1752, 2015.
  • [38] Gupta A., Kharbanda O.P., Balachandran R., Sardana V., Kalra S., Chaurasia S., Sardana H.K.: Precision of manual landmark identification betweenas-received and oriented volume-rendered cone-beam computed tomographyimages, American Journal of Orthodontics and Dentofacial Orthopedics, vol.151(1), pp. 118–131. https://doi.org/10.1016/j.ajodo.2016.06.027.
  • [39] Gupta A., Kharbanda O.P., Sardana V., Balachandran R., Sardana H.K.: Accuracy of 3D cephalometric measurements based on an automatic knowledge-basedlandmark detection algorithm, International Journal of Computer Assisted Radiology and Surgery, vol. 11, pp. 1297–1309, 2015.
  • [40] Hand A.J., Sun T., Barber D.C., Hose D.R., MacNeil S.: Automated trackingof migrating cells in phase-contrast video microscopy sequences using imageregistration, Journal of Microscopy, vol. 234, pp. 62–79, 2009.
  • [41] Hoheisel M.: Review of medical imaging with emphasis on X-ray detectors, Nuclear Instruments and Methods in Physics Research Section A: Accelerators,Spectrometers, Detectors and Associated Equipment, vol. 563, pp. 215–224, 2006.
  • [42] Hu S., Hoffman E.A., Reinhardt J.M.: Automatic lung segmentation for accu-rate quantitation of volumetric X-ray CT images, IEEE Transactions on MedicalImaging, vol. 20, pp. 490–498, 2001.
  • [43] Huete M.I., Ibanez O., Wilkinson C., Kahana T.: Past, present, and futureof craniofacial superimposition: Literature and international surveys, Legal Medicine, vol. 17(4), pp. 267–278, 2015.
  • [44] Huisman J.A., Hubbard S.S., Redman J.D., Annan A.P.: Measuring Soil WaterContent with Ground Penetrating Radar: A Review, Vadose Zone Journal, vol. 2, pp. 476–491, 2003.
  • [45] Jolesz F.A., Blumenfeld S.M.: Interventional use of magnetic resonance imaging, Magn Reson Q, vol. 10, pp. 85–96, Jun 1994.
  • [46] Kalender W.A.: X-ray computed tomography, Physics in Medicine&Biology, vol. 51(13), pp. R29–R43, 2006. http://dx.doi.org/10.1088/0031-9155/51/13/R03.
  • [47] Karaca A.C., Erturk A., G̈ullu M.K., Elmas M., Erturk S.: Analysis of evidencein forensic documents using hyperspectral imaging system. In:2012 20th Signal Processing and Communications Applications Conference (SIU), pp. 1–4, 2012.https://doi.org/10.1109/SIU.2012.6204724.
  • [48] Kavur A.E., Selver M.A., Dicle O., Barıs M., Gezer N.S.: CHAOS – Combined(CT-MR) Healthy Abdominal Organ Segmentation Challenge Data [Data set],2019. https://doi.org/10.5281/zenodo.3362845.
  • [49] Kawase K., Ogawa Y., Watanabe Y., Inoue H.: Non-destructive terahertz imaging of illicit drugs using spectral fingerprints, Optics Express, vol. 11,pp. 2549–2554, 2003.
  • [50] Khandhediya Y., Sav K., Gajjar V.: Human Detection for Night Surveillanceusing Adaptive Background Subtracted Image. In:arXiv:1709.09389, 2017. https://arxiv.org/abs/1709.09389v1.
  • [51] Kolaric D., Lipic T., Grubisic I., Gjenero L., Skala K.: Application of in-frared thermal imaging in blade system temperature monitoring. In: Proceedings ELMAR-2011, pp. 309–312, Zadar, 2011.
  • [52] Kotwaliwale N., Singh K., Kalne A., Jha S.N., Seth N., Kar A.: X-ray imaging methods for internal quality evaluation of agricultural produce, Journal of Food Science and Technology, vol. 51, pp. 1–15, 2014.
  • [53] Kraszewski A.W., Nelson S.O.: Application of microwave techniques in agricultural research. In: Proceedings of 1995 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference, vol. 1, pp. 117–126, 1995.
  • [54] Kumar P., Mittal A., Kumar P.: Fusion of Thermal Infrared and Visible Spectrum Video for Robust Surveillance. In: Kalra P.K., Peleg S. (eds.), Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science, vol. 4338, Berlin, Heidelberg, 2006, pp. 528–539.
  • [55] Kumar P., Bhondekar A.P., Kapur P.: Measurement of changes in glacier extentin the Rimo glacier, a sub-range of the Karakoram Range, determined from Landsat imagery, Journal of King Saud University – Computer and Information Sciences, vol. 26(1), pp. 121–130, 2014.
  • [56] Kumar P.N.S., Deepak R.U., Sathar A., Sahasranamam V., Kumar R.R.: Automated Detection System for Diabetic Retinopathy Using Two Field Fundus Photography, Procedia Computer Science, vol. 93, pp. 486–494, 2016.
  • [57] Kuo T.-Y., Chung C.-L., Chen S.-Y., Lin H.-A., Kuo Y.-F.: Identifying ricegrains using image analysis and sparse-representation-based classification, Computers and Electronics in Agriculture, vol. 127, pp. 716–725, 2016.
  • [58] Lamecker H., Wenckebach T., Hege H.-C.: Atlas-based 3D-Shape Reconstruction from X-Ray Images. In:18th International Conference on Pattern Recognition (ICPR’06), pp. 371–374, 2006.
  • [59] Lavers C., Franks K., Floyd M., Plowman A.: Application of remote thermalimaging and night vision technology to improve endangered wildlife resource management with minimal animal distress and hazard to humans, Journal of Physics: Conference Series, vol. 15, pp. 207–215, 2005.
  • [60] Leonardi R., Giordano D., Maiorana F., Spampinato C.: Automatic Cephalometric Analysis,The Angle Orthodontist, vol. 78(1), pp. 145–151, 2008.
  • [61] Li G., Chen X., Shi F., Zhu W., Tian J., Xiang D.: Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images, IEEE Transactions on Image Processing, vol. 24(12), pp. 5315–5329, 2015.https://doi.org/10.1109/TIP.2015.2481326.
  • [62] Lin C.-Y., Lin C.-Y., Chompuchan C.: Risk-based models for potential large-scale landslide monitoring and management in Taiwan, Geomatics, Natural Hazards and Risk, vol. 8(2), pp. 1505–1523, 2017. https://doi.org/10.1080/19475705.2017.1345797
  • [63] Lindner C., Wang C.W., Huang C.T., Li C.H., Chang S.W., Cootes T.F.: Fully Automatic System for Accurate Localisation and Analysis of Cephalometric Landmarks in Lateral Cephalograms, Scientific Reports, vol. 6, p. 33581, 2016.https://doi.org/10.1038/srep33581.
  • [64] Linguraru M.G., Richbourg W.J., Liu J., Watt J.M., Pamulapati V., Wang S.,Summers R.M.: Tumor Burden Analysis on Computed Tomography by Automated Liver and Tumor Segmentation, IEEE Transactions on Medical Imaging,vol. 31(10), pp. 1965–1976, 2012.
  • [65] LiTS – Liver Tumor Segmentation Challenge. https://competitions.codalab.org/competitions/17094.
  • [66] Lopez A.R., Giro-i-Nieto X., Burdick J., Marques O.: Skin lesion classification from dermoscopic images using deep learning techniques. In:2017 13th IASTED International Conference on Biomedical Engineering (BioMed), pp. 49–54, 2017.
  • [67] Maska M., Ulman V., Svoboda D., Matula P., et. al: A benchmark for comparison of cell tracking algorithms,Bioinformatics, vol. 30(11), pp. 1609–1617,2014. https://doi.org/10.1093/bioinformatics/btu080, http://www.celltrackingchallenge.net/datasets.html.
  • [68] Maken P., Gupta A., Gupta M.K.: A Study on Various Techniques Involved inGender Prediction System: A Comprehensive Review, Cybernetics and Information Technologies, vol. 19(2), pp. 51–73, 2019. https://doi.org/10.2478/cait-2019-0015.
  • [69] Mallard J.R., Myers M.J., Clinical Applications of a Gamma Camera, Physicsin Medicine&Biology, vol. 8(2), pp. 183–192, 1963. https://doi.org/10.1088/0031-9155/8/2/305.
  • [70] Malm S., Frigstad S., Sagberg E., Larsson H., Skjaerpe T.: Accurate and reproducible measurement of left ventricular volume and ejection fraction by contrast echocardiography: A comparison with magnetic resonance imaging, Journal ofthe American College of Cardiology, vol. 44(5), pp. 1030–1035, 2004.
  • [71] Mehrnejad M., Albu A.B., Capson D., Hoeberechts M.: Detection of stationary animals in deep-sea video. In:2013 OCEANS – San Diego, 2013, pp. 1–5. https://ieeexplore.ieee.org/document/6741095.
  • [72] Menze B.H., et. al:The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS), IEEE Transactions on Medical Imaging, vol. 34(10),pp. 1993–2024, 2015. https://doi.org/10.1109/TMI.2014.2377694.
  • [73] Moallem P., Serajoddin A., Pourghassem H.: Computer vision-based apple grading for golden delicious apples based on surface features, Information Processingin Agriculture, vol. 4(1), pp. 33–40, 2017.
  • [74] Momin M.A., Rahman M.T., Sultana M.S., Igathinathane C., Ziauddin A.T.M.,Grift T.E.: Geometry-based mass grading of mango fruits using image processing, Information Processing in Agriculture, vol. 4(2), pp. 150–160, 2017.
  • [75] Moroni M., Lupo E., Marra E., Cenedese A.: Hyperspectral Image Analysis in Environmental Monitoring: Setup of a New Tunable Filter Platform, Procedia Environmental Sciences, vol. 19, pp. 885–894, 2013.
  • [76] MRI Images http://www.mr-tip.com/serv1.php?type=img&img=Anatomic%20Imaging%20of%20the%20Liver.
  • [77] Muresan H., Oltean M., Fruit recognition from images using deep learning, Acta Universitatis Sapientiae, Informatica, vol. 10(1), pp. 26–42, 2017. https://doi.org/10.2478/ausi-2018-0002. https://www.kaggle.com/litzar/fruits-classification/data
  • [78] Nansen C., Zhao G., Dakin N., Zhao C., Turner S.R.: Using hyperspectral imaging to determine germination of native Australian plant seeds, Journal of Photochemistry and Photobiology B: Biology, vol. 145, pp. 19–24, 2015.
  • [79] Nasrabadi N.M.: Hyperspectral Target Detection: An Overview of Current and Future Challenges, IEEE Signal Processing Magazine, vol. 31(1), pp. 34–44,2014.
  • [80] Neelapu B.C., Sardana H.K., Kharbanda O.P., Sardana V., Gupta A., Vasamsetti S.: Method And System For Automatic Volumetric-Segmentation Of Human Upper Respiratory Tract, US Patent US20190066303A1, 2018.
  • [81] Neelapu B.C., Kharbanda O.P., Sardana H.K., Gupta A., Vasamsetti S., Balachandran R., Rana S.S., Sardana V.: The reliability of different methodsof manual volumetric segmentation of pharyngeal and sinonasal subregions, Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, vol. 124(6),pp. 577–587, 2017.
  • [82] Neelapu B.C., Kharbanda O.P., Sardana V., Gupta A., Vasamsetti S., Bal-achandran R., Rana S.S., Sardana H.K.: A pilot study for segmentation of pharyngeal and sino-nasal airway subregions by automatic contour initialization, International Journal of Computer Assisted Radiology and Surgery, vol. 12(11),pp. 1877–1893, 2017.
  • [83] Neelapu B.C., Kharbanda O.P., Sardana V., Gupta A., Vasamsetti S., Balachandran R., Sardana H.K.: Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull, Dentomaxillofacial Radiology, vol. 47(2), p. 20170054, 2018.
  • [84] Omar M., Khelifi F., Tahir M.A.: Detection and classification of retinal fundusimages exudates using region based multiscale LBP texture approach. In: 2016 International Conference on Control, Decision and Information Technologies (CoDIT), pp. 227–232, 2016.
  • [85] Park B., Lu R. (eds.):Hyperspectral Imaging Technology in Food and Agriculture, Springer, New York, 2015.
  • [86] Pediatric Bone Age Challenge, Organized by RSNA.organizing.committee.https://www.kaggle.com/kmader/rsna-bone-age.
  • [87] Pieri G., Salvetti O.: Active video-surveillance based on stereo and infraredimaging. In:2006 14th European Signal Processing Conference, pp. 1–5, IEEE,2006.
  • [88] Porwal P., Pachade S., Kamble R., Kokare M., Deshmukh G., Sahasrabuddhe V., Meriaudeau F.: Indian Diabetic Retinopathy Image Dataset (IDRiD), IEEE Dataport, 2018. https://doi.org/10.21227/H25W98.
  • [89] Punithavathy K., Ramya M.M., Poobal S.: Analysis of statistical texture fea-tures for automatic lung cancer detection in PET/CT images. In:2015 International Conference on Robotics, Automation, Control and Embedded Systems(RACE), pp. 1–5, 2015.
  • [90] Rahman A., Cho B.-K.: Assessment of seed quality using non-destructive measurement techniques: a review,Seed Science Research, vol. 26, pp. 285–305,2016.
  • [91] Rajkumar S., Bardhan P., Akkireddy S.K., Munshi C.: CT and MRI imagefusion based on Wavelet Transform and Neuro-Fuzzy concepts with quantitative analysis. In:2014 International Conference on Electronics and Communication Systems (ICECS), pp. 1–6, 2014.
  • [92] Rechtman L.R., Lenihan M.J., Lieberman J.H., Teal C.B., Torrente J., Rape-lyea J.A., Brem R.F.: Breast-Specific Gamma Imaging for the Detectionof Breast Cancer in Dense Versus Nondense Breasts, American Journal of Roentgenology, vol. 202(2), pp. 293–298, 2014.
  • [93] Regier M., Knoerzer K., Schubert H.: 1 – Introducing microwave-assistedprocessing of food: Fundamentals of the technology. In: Regier M., Knoerzer K., Schubert H. (eds.),The Microwave Processing of Foods (Second Edition), pp. 1–22, Woodhead Publishing, 2017.
  • [94] Roy A.G., Conjeti S., Karri S.P.K., Sheet D., Katouzian A., Wachinger C.,Navab N.: ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network, Biomedical Optics Express, vol. 8(8), pp. 3627–3642, 2017.
  • [95] Sankaran S., Ehsani R.: Introduction to the Electromagnetic Spectrum. In: Manickavasagan A., Jayasuriya H.(eds.), Imaging with Electromagnetic Spectrum: Applications in Food and Agriculture, pp. 1–15, Berlin–Heidelberg, Springer, 2014.
  • [96] Saputra T.W., Masithoh R.E., Achmad B.: Development of Plant Growth Monitoring System Using Image Processing Techniques Based on Multiple Images.In: Isnansetyo A., Nuringtyas T. (eds.), Proceeding of the 1st International Conference on Tropical Agriculture, pp. 647–653, Springer, Cham, 2017.
  • [97] Sarkar P., Choudhary R.: UV Imaging. In: Manickavasagan A., Jayasuriya H.(eds.), Imaging with Electromagnetic Spectrum: Applications in Food and Agriculture, pp. 57–66, Springer-Verlag, Berlin–Heidelberg, 2014.
  • [98] Scharr H., Minervini M., Fischbach A., Tsaftaris S.A.:Annotated Image Datasets of Rosette Plants,Technical Report No.FZJ-2014-03837, Forschungszentrum J ̈ulich, 2014. https://juser.fz- juelich.de/record/154525/files/FZJ-2014-03837.pdf.
  • [99] Sharma N., Aggarwal L.M., Automated medical image segmentation techniques, Journal of Medical Physics, vol. 35(1), pp. 3–14, 2010.
  • [100] Sharma M., Singh S.: Evaluation of texture methods for image analysis. In: TheSeventh Australian and New Zealand Intelligent Information Systems Conference, 2001, pp. 117–121, IEEE, 2001.
  • [101] Siddiqui S.A., Salman A., Malik M.I., Shafait F., Mian A., Shortis M.R., Harvey E.S.: Automatic fish species classification in underwater videos: exploiting pre-trained deep neural network models to compensate for limited labelled data, ICES Journal of Marine Science, vol. 75(1), pp. 374–389, 2018.
  • [102] Simon C.J., Dupuy D.E., Mayo-Smith W.W.: Microwave Ablation: Principlesand Applications, RadioGraphics, vol. 25 (Suppl1), pp. S69–S83, 2005.
  • [103] Singh V., Misra A.K.: Detection of plant leaf diseases using image segmentation and soft computing techniques, Information Processing in Agriculture, vol. 4(1),pp. 41–49, 2017.
  • [104] Suganya R.: An automated computer aided diagnosis of skin lesions detection and classification for dermoscopy images. In:2016 International Conference onRecent Trends in Information Technology (ICRTIT), 2016, pp. 1–5.
  • [105] Tajbakhsh N., Gurudu S.R., Liang J.: Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks. In:2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015, pp. 79–83.
  • [106] Thenkabail P.S., Lyon J.G., Huete A. (eds.): Hyperspectral Remote Sensing of Vegetation, CRC Press, 2011.
  • [107] Tiulpin A., Thevenot J., Rahtu E., Lehenkari P., Saarakkala S.: Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-BasedApproach, Scientific Reports, vol. 8, p. 1727, 2018.
  • [108] Ultrasound Nerve Segmentation. https://www.kaggle.com/c/ultrasound-nerve-segmentation/data.
  • [109] Vejarano R., Siche R., Tesfaye W.: Evaluation of biological contaminants infoods by hyperspectral imaging: A review, International Journal of Food Properties, vol. 20(sup2), pp. 1264–1297, 2017.
  • [110] Viaud G., Loudet O., Cournede P.-H.: Leaf Segmentation and Tracking in Arabidopsis thaliana Combined to an Organ-Scale Plant Model for Genotypic Differentiation, Frontiers in Plant Science, vol. 7, 2017. https://doi.org/10.3389/fpls.2016.02057.
  • [111] Wang J., Li F., Li Q.: Automated segmentation of lungs with severe interstitial lung disease in CT, Medical Physics, vol. 36(1), pp. 4592–4599, 2009.
  • [112] Wang J., Zhang M., Pechauer A.D., Liu L., Hwang T.S., Wilson D.J.,Li D.,Jia Y.: Automated volumetric segmentation of retinal fluid on optical coherence tomography, Biomedical Optics Express, vol. 7(4), pp. 1577–1589, 2016.
  • [113] Wong W.K., Tan P.N., Loo C.K., Lim W.S.: An Effective Surveillance System Using Thermal Camera. In:2009 International Conference on Signal Acquisition and Processing, pp. 13–17, 2009.
  • [114] Zoughi R., Kharkovsky S.: Microwave and millimetre wave sensors for crack detection, Fatigue&Fracture of Engineering Materials&Structures, vol. 31,pp. 695–713, 2008.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a757f460-b94c-4bac-9377-c44617b2ebcf
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.