Добірка наукової літератури з теми "Plant segmentation"
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Статті в журналах з теми "Plant segmentation"
Murray, Carl, and Mark O'Malley. "Segmentation of plant cell pictures." Image and Vision Computing 11, no. 3 (April 1993): 155–62. http://dx.doi.org/10.1016/0262-8856(93)90054-k.
Повний текст джерелаMahajan, Vatsal, Dilip Jain, and Abhinav Dua. "Plant Leaf Segmentation Invariant of Background." International Journal of Computer & Organization Trends 12, no. 1 (September 25, 2014): 24–26. http://dx.doi.org/10.14445/22492593/ijcot-v12p305.
Повний текст джерелаDiao, Zhi Hua, Yin Mao Song, Huan Wang, and Yun Peng Wang. "Study Surveys on Image Segmentation of Plant Disease Spot." Advanced Materials Research 542-543 (June 2012): 1047–50. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1047.
Повний текст джерела钟, 旭升. "Segmentation of Plant Point Cloud Segmentation Based on Dynamic Graph Convolution Network." Computer Science and Application 12, no. 03 (2022): 690–96. http://dx.doi.org/10.12677/csa.2022.123070.
Повний текст джерелаCao, Liying, Hongda Li, Helong Yu, Guifen Chen, and Heshu Wang. "Plant Leaf Segmentation and Phenotypic Analysis Based on Fully Convolutional Neural Network." Applied Engineering in Agriculture 37, no. 5 (2021): 929–40. http://dx.doi.org/10.13031/aea.14495.
Повний текст джерелаEt. al., Rajendra Prasad Bellapu,. "PERFORMANCE COMPARISON OF UNSUPERVISED SEGMENTATION ALGORITHMS ON RICE, GROUNDNUT, AND APPLE PLANT LEAF IMAGES." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (April 13, 2021): 1090–105. http://dx.doi.org/10.17762/itii.v9i2.457.
Повний текст джерелаSun, Guiling, Xinglong Jia, and Tianyu Geng. "Plant Diseases Recognition Based on Image Processing Technology." Journal of Electrical and Computer Engineering 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/6070129.
Повний текст джерелаWang, Yi, and Lihong Xu. "Unsupervised segmentation of greenhouse plant images based on modified Latent Dirichlet Allocation." PeerJ 6 (June 28, 2018): e5036. http://dx.doi.org/10.7717/peerj.5036.
Повний текст джерелаGuadarrama, Lili, Carlos Paredes, and Omar Mercado. "Plant Disease Diagnosis in the Visible Spectrum." Applied Sciences 12, no. 4 (February 20, 2022): 2199. http://dx.doi.org/10.3390/app12042199.
Повний текст джерелаLi, Dawei, Jinsheng Li, Shiyu Xiang, and Anqi Pan. "PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants." Plant Phenomics 2022 (May 23, 2022): 1–20. http://dx.doi.org/10.34133/2022/9787643.
Повний текст джерелаДисертації з теми "Plant segmentation"
Wang, Shisheng. "Plant segmentation for growth analysis in temporal datasets." Thesis, Aberystwyth University, 2018. http://hdl.handle.net/2160/1d6683d9-a530-416a-b0e8-594b87ecb684.
Повний текст джерелаDammannagari, Gangadhara Shravan. "Mobile high-throughput phenotyping using watershed segmentation algorithm." Thesis, Kansas State University, 2017. http://hdl.handle.net/2097/35387.
Повний текст джерелаDepartment of Computing and Information Sciences
Mitchell L. Neilsen
This research is a part of BREAD PHENO, a PhenoApps BREAD project at K-State which combines contemporary advances in image processing and machine vision to deliver transformative mobile applications through established breeder networks. In this platform, novel image analysis segmentation algorithms are being developed to model and extract plant phenotypes. As a part of this research, the traditional Watershed segmentation algorithm has been extended and the primary goal is to accurately count and characterize the seeds in an image. The new approach can be used to characterize a wide variety of crops. Further, this algorithm is migrated into Android making use of the Android APIs and the first ever user-friendly Android application implementing the extended Watershed algorithm has been developed for Mobile field-based high-throughput phenotyping (HTP).
Cerutti, Guillaume. "Segmentation et interprétation d'images naturelles pour l'identification de feuilles d'arbres sur smartphone." Thesis, Lyon 2, 2013. http://www.theses.fr/2013LYO22022/document.
Повний текст джерелаPlant species, and especially tree species, constitute a well adapted target for an automatic recognition process based on image analysis. The criteria that make their identification possible are indeed often morphological visual elements, which are well described and referenced by botany. This leads to think that a recognition through shape is worth considering. Leaves stand out in this context as the most accessible discriminative plant organs, and are subsequently the most often used for this problem recently receiving a particular attention. Automatic identification however gives rise to a fair amount of complex problems, linked with the processing of images, or in the difficult nature of the species classification itself, which make it an advanced application for pattern recognition.This thesis considers the problem of tree species identification from leaf images within the framework of a smartphone application intended for a non-specialist audience. The images on which we expect to work are then potentially very complex scenes and their acquisition rather unsupervised. We consequently propose dedicated methods for image analysis, in order to segment and interpret tree leaves, using an original shape modelling and deformable templates. The introduction on prior knowledge on the shape of objects enhances significatively the quality and the robustness of the information we extract from the image. All processing being carried out on the mobile device, we developed those algorithms with concern towards the material constraints of their exploitation. We also introduce a very specific description of leaf shapes, inspired by the determining characteristics listed in botanical references. These different descriptors constitute independent sources of high-level information that are fused at the end of the process to identify species, while providing the user with a possible semantic interpretation. The classification performance demonstrated over approximately 100 tree species are competitive with state-of-the-art methods of the domain, and show a particular robustness to difficult natural background images. Finally, we integrated the implementation of our recognition system into the \textbf{Folia} application for iPhone, which constitutes a validation of our approaches and methods in a real-world use
von, Koch Christian, and William Anzén. "Detecting Slag Formation with Deep Learning Methods : An experimental study of different deep learning image segmentation models." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177269.
Повний текст джерелаBertrand, Sarah. "Analyse d'images pour l'identification multi-organes d'espèces végétales." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE2127/document.
Повний текст джерелаThis thesis is part of the ANR ReVeRIES, which aims to use mobile technologies to help people better understand their environment and in particular the plants that surround them. More precisely, the ReVeRIES project is based on a mobile application called Folia developed as part of the ANR ReVeS project and capable of recognising tree and shrub species based on photos of their leaves. This prototype differs from other tools in that it is able to simulate the behaviour of the botanist. In the context of the ReVeRIES project, we propose to go much further by developing new aspects: multimodal species recognition, learning through play and citizen science. The purpose of this thesis is to focus on the first of these three aspects, namelythe analysis of images of plant organs for identification.More precisely, we consider the main trees and shrubs, endemic or exotic, found in metropolitan France. The objective of this thesis is to extend the recognition algorithm by taking into account other organs in addition to the leaf. This multi-modality is indeed essential if we want the user to learn and practice the different methods of recognition for which botanists use the variety of organs (i.e. leaves, flowers, fruits and bark). The method used by Folia for leaf recognition being dedicated, because simulating the work of a botanist on the leaf, cannot be applied directly to other organs. Thus, new challenges are emerging, both in terms of image processing and data fusion.The first part of the thesis was devoted to the implementation of image processing methods for the identification of plant species. The identification of tree species from bark images was the first to be studied. The descriptors developed take into account the structure of the bark inspired from the criteria used by botanists. Fruits and flowers required a segmentation step before their description. A new segmentation method that can be used on smartphones has been developed to work in spite of the high variability of flowers and fruits. Finally, descriptors were extracted on fruits and flowers after the segmentation step. We decided not to separate flowers and fruits because we showed that a user new to botany does not always know the difference between these two organs on so-called "ornamental" trees (not fruit trees). For fruits and flowers, prediction is not only made on their species but also on their genus and family, botanical groups reflecting a similarity between these organs.The second part of the thesis deals with the combination of descriptors of the different organs: leaves, bark, fruits and flowers. In addition to basic combination methods, we propose to consider the confusion between species, as well as predictions of affiliations in botanical taxa higher than the species.Finally, an opening chapter is devoted to the processing of these images by convolutional neural networks. Indeed, Deep Learning is increasingly used in image processing, particularly for plant organs. In this context, we propose to visualize the learned convolution filters extracting information, in order to make the link between the information extracted by these networks and botanical elements
Pollak, Williamson Bernardo. "Frameworks for reprogramming early diverging land plants." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/273535.
Повний текст джерелаKenouche, Samir. "Études expérimentales et modélisation de la dynamique de distribution des agents de contraste en imagerie RMN : applications à l'agronomie." Phd thesis, Université Montpellier II - Sciences et Techniques du Languedoc, 2013. http://tel.archives-ouvertes.fr/tel-01019641.
Повний текст джерелаRamalingam, Nagarajan. "Non-contact multispectral and thermal sensing techniques for detecting leaf surface wetness." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1104392582.
Повний текст джерелаTitle from first page of PDF file. Document formatted into pages; contains xxii, 271 p.; also includes graphics (some col.) Includes bibliographical references (p. 206-214).
Perissini, Ivan Carlos. "Análise experimental de algoritmos de constância de cor e segmentação para detecção de mudas de plantas." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/18/18149/tde-25052018-095947/.
Повний текст джерелаThe use of computer vision has been gaining ground in the agricultural context, especially with the evolution of the concept of precision agriculture. Applications such as irrigation, fertilization and pest control are just some of the scenarios that this technology can be used. However, the demand for accessible and efficient systems together with the variations and visual noise from an external environment presents challenges to these processes. It was proposed in this study an analysis of the literature and a series of experimental investigations of image processing techniques, to search for better relations between computational cost and performance in the detection of seedlings, aiming to achieve real time operations with the use of common and low cost hardware. For this, the work investigates the composition of segmentation strategies from different color spaces and color constancy methods, in order to combat light variation, one of the major sources of instability in agricultural vision applications. The proposed experiments were divided into two phases; in the first the measurement system was evaluated, defining the metrics and suitable conditions for the experiments at second phase, composed of a sequence of comparative experiments of segmentation strategies under different lighting conditions. The results showed that the solutions are very dependent on the conditions of the scene and a series of promising segmentation alternatives were obtained. Their eligibility, however, depends on considerations about the computational availability and context of the application.
Lin, Meng-Yen. "Assessing market segmentation success : developing a plan, fieldwork, action approach." Thesis, University of Warwick, 1996. http://wrap.warwick.ac.uk/36179/.
Повний текст джерелаКниги з теми "Plant segmentation"
Bank Marketing Association (U.S.), ed. Building a financial services marketing plan: Working plans for product and segment marketing. Naperville, Ill: Financial Sourcebooks, 1989.
Знайти повний текст джерелаMedicinal and Aromatic Plants Strategic Segmentation Analysis. World Bank, Washington, DC, 2018. http://dx.doi.org/10.1596/31613.
Повний текст джерелаCastellani, Claudia, and Marianne Wootton. Crustacea: Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199233267.003.0021.
Повний текст джерелаCanny, Nicholas, and Philip Morgan. Introduction. Edited by Nicholas Canny and Philip Morgan. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199210879.013.0001.
Повний текст джерелаЧастини книг з теми "Plant segmentation"
Aparna, S., and R. Aarthi. "Segmentation of Tomato Plant Leaf." In Advances in Intelligent Systems and Computing, 149–56. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3373-5_14.
Повний текст джерелаF. Danilevicz, Monica, and Philipp Emanuel Bayer. "Machine Learning for Image Analysis: Leaf Disease Segmentation." In Plant Bioinformatics, 429–49. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2067-0_22.
Повний текст джерелаGuedes-Pinto, H., O. Pinto-Carnide, and F. Leal. "Segmentation Effect of Immature Spike on Triticale Calli Induction." In Plant Aging, 361–65. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4684-5760-5_46.
Повний текст джерелаGuo, Wei, and Akshay L. Chandra. "Deep Learning in Plant Omics: Object Detection and Image Segmentation." In Plant Omics, 234–45. GB: CABI, 2022. http://dx.doi.org/10.1079/9781789247534.0018.
Повний текст джерелаFernàndez, G., M. Kunt, and J. P. Zrÿd. "A new plant cell image segmentation algorithm." In Image Analysis and Processing, 229–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_263.
Повний текст джерелаChoudhury, Sruti Das. "Segmentation Techniques and Challenges in Plant Phenotyping." In Intelligent Image Analysis for Plant Phenotyping, 69–92. First edition. | Boca Raton, FL : CRC Press, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9781315177304-6.
Повний текст джерелаPeng, Chen, Chuanliang Cheng, and Ling Wang. "Intelligent Segmentation of Furnace Flame Image." In Reconstruction and Intelligent Control for Power Plant, 39–63. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5574-7_3.
Повний текст джерелаHeiwolt, Karoline, Tom Duckett, and Grzegorz Cielniak. "Deep Semantic Segmentation of 3D Plant Point Clouds." In Towards Autonomous Robotic Systems, 36–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89177-0_4.
Повний текст джерелаKörschens, Matthias, Paul Bodesheim, Christine Römermann, Solveig Franziska Bucher, Mirco Migliavacca, Josephine Ulrich, and Joachim Denzler. "Weakly Supervised Segmentation Pretraining for Plant Cover Prediction." In Lecture Notes in Computer Science, 589–603. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92659-5_38.
Повний текст джерелаAgarwal, Mohit, Suneet Kr Gupta, and K. K. Biswas. "Plant Leaf Disease Segmentation Using Compressed UNet Architecture." In Lecture Notes in Computer Science, 9–14. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75015-2_2.
Повний текст джерелаТези доповідей конференцій з теми "Plant segmentation"
Paturkar, Abhipray, Gourab Sen Gupta, and Donald Bailey. "Plant Trait Segmentation for Plant Growth Monitoring." In 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 2020. http://dx.doi.org/10.1109/ivcnz51579.2020.9290575.
Повний текст джерелаDias, Jeferson de Souza, and José Hiroki Saito. "Coffee plant image segmentation and disease detection using JSEG algorithm." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/wvc.2021.18887.
Повний текст джерелаZhong, Changyuan, Zelin Hu, Xuanjiang Yang, Hualong Li, Fei Liu, and Miao Li. "Triple Stream Segmentation Network for Plant Disease Segmentation." In 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2021. http://dx.doi.org/10.1109/iaeac50856.2021.9390933.
Повний текст джерелаAbbasi, Arash, and Noah Fahlgren. "Naïve Bayes pixel-level plant segmentation." In 2016 IEEE Western New York Image and Signal Processing Workshop (WNYISPW). IEEE, 2016. http://dx.doi.org/10.1109/wnyipw.2016.7904790.
Повний текст джерелаJung, Joo-Yeon, Sang-Ho Lee, and Jong-Ok Kim. "Plant Leaf Segmentation Using Knowledge Distillation." In 2022 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia). IEEE, 2022. http://dx.doi.org/10.1109/icce-asia57006.2022.9954844.
Повний текст джерелаChau, Zhong Hoo, Ishara Paranawithana, Liangjing Yang, and U.-Xuan Tan. "Plant Cell Segmentation with Adaptive Thresholding." In 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). IEEE, 2018. http://dx.doi.org/10.1109/m2vip.2018.8600840.
Повний текст джерела"Automated segmentation of plant leaf images." In AGRICULTURAL INFORMATION TECHNOLOGY AND ENGINEERING AGROINFO-2021. SFSCA RAS, 2021. http://dx.doi.org/10.26898/agroinfo-2021-26-30.
Повний текст джерела"Applying neural network for the segmentation of spike structural elements." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Novosibirsk ICG SB RAS 2021, 2021. http://dx.doi.org/10.18699/plantgen2021-049.
Повний текст джерелаDayanand, R. B., and Daneshwari A. Noola. "Plant leaf segmentation through connected pixel approach." In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2019. http://dx.doi.org/10.1109/icssit46314.2019.8987781.
Повний текст джерелаRahman, Md Arifur, Md Mukitul Islam, G. M. Shahir Mahdee, and Md Wasi Ul Kabir. "Improved Segmentation Approach for Plant Disease Detection." In 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). IEEE, 2019. http://dx.doi.org/10.1109/icasert.2019.8934895.
Повний текст джерела