Gotowe bibliografie tematyczne / Image segmentation

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Image segmentation”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "Image segmentation"

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Hassan, Raghad Mahdie, and Luma Salal Hassan. "Assessments Image Segmentation Using Genetic Algorithm." Al-Furat Journal of Innovations in Electronics and Computer Engineering 3, no. 2 (2024): 352–63. http://dx.doi.org/10.46649/fjiece.v3.2.23a.2.6.2024.

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Image segmentation is a crucial technique for processing images. It is a challenging task to process images, and the quality of the segmentation process affects the following assignments, which include classification, object recognition, feature extraction, and object detection.It's a significant phase of a system for computer vision. Image segmentation is the basic problem in many applications for image processing. Over time, image segmentation has gotten more challenging due to its extensive use in numerous applications. It is the procedure of segmenting the image into various areas by using
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Stevens, Michiel, Afroditi Nanou, Leon W. M. M. Terstappen, Christiane Driemel, Nikolas H. Stoecklein, and Frank A. W. Coumans. "StarDist Image Segmentation Improves Circulating Tumor Cell Detection." Cancers 14, no. 12 (2022): 2916. http://dx.doi.org/10.3390/cancers14122916.

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After a CellSearch-processed circulating tumor cell (CTC) sample is imaged, a segmentation algorithm selects nucleic acid positive (DAPI+), cytokeratin-phycoerythrin expressing (CK-PE+) events for further review by an operator. Failures in this segmentation can result in missed CTCs. The CellSearch segmentation algorithm was not designed to handle samples with high cell density, such as diagnostic leukapheresis (DLA) samples. Here, we evaluate deep-learning-based segmentation method StarDist as an alternative to the CellSearch segmentation. CellSearch image archives from 533 whole blood sample
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Rahman, Fathur, Nuzul Hikmah, and Misdiyanto Misdiyanto. "Analysis Influence Segmentation Image on Classification Image X-raylungs with Method Convolutional Neural." Journal of Informatics Development 2, no. 1 (2023): 23–29. http://dx.doi.org/10.30741/jid.v2i1.1159.

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The impact of image segmentation on the classification of lung X-ray images using Convolutional Neural Networks (CNNs) has been scrutinized in this study. The dataset used in this research comprises 150 lung X-ray images, distributed as 78 for training, 30 for validation, and 42 for testing. Initially, image data undergoes preprocessing to enhance image quality, employing adaptive histogram equalization to augment contrast and enhance image details. The evaluation of segmentation's influence is based on a comparison between image classification with and without the segmentation process. Segmen
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Wang, Guodong, Jie Xu, Qian Dong, and Zhenkuan Pan. "Active Contour Model Coupling with Higher Order Diffusion for Medical Image Segmentation." International Journal of Biomedical Imaging 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/237648.

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Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active co
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Pitkänen, Johanna, Juha Koikkalainen, Tuomas Nieminen, et al. "Evaluating severity of white matter lesions from computed tomography images with convolutional neural network." Neuroradiology 62, no. 10 (2020): 1257–63. http://dx.doi.org/10.1007/s00234-020-02410-2.

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Abstract Purpose Severity of white matter lesion (WML) is typically evaluated on magnetic resonance images (MRI), yet the more accessible, faster, and less expensive method is computed tomography (CT). Our objective was to study whether WML can be automatically segmented from CT images using a convolutional neural network (CNN). The second aim was to compare CT segmentation with MRI segmentation. Methods The brain images from the Helsinki University Hospital clinical image archive were systematically screened to make CT-MRI image pairs. Selection criteria for the study were that both CT and MR
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Liu, Hong, Haijun Wei, Lidui Wei, Jingming Li, and Zhiyuan Yang. "The Segmentation of Wear Particles Images UsingJ-Segmentation Algorithm." Advances in Tribology 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4931502.

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This study aims to use a JSEG algorithm to segment the wear particle’s image. Wear particles provide detailed information about the wear processes taking place between mechanical components. Autosegmentation of their images is key to intelligent classification system. This study examined whether this algorithm can be used in particles’ image segmentation. Different scales have been tested. Compared with traditional thresholding along with edge detector, the JSEG algorithm showed promising result. It offers a relatively higher accuracy and can be used on color image instead of gray image with l
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Yazdi, Mahsa Badiee, Mohammad Mahdi Khalilzadeh, and Mohsen Foroughipour. "MRI SEGMENTATION BY FUZZY CLUSTERING METHOD BASED ON PRIOR KNOWLEDGE." Biomedical Engineering: Applications, Basis and Communications 28, no. 04 (2016): 1650025. http://dx.doi.org/10.4015/s1016237216500253.

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Image segmentation is often required as a fundamental stage in medical image processing, particularly during the clinical analysis of magnetic resonance (MR) brain images. Fuzzy c-means (FCM) clustering algorithm is one of the best known and widely used segmentation methods, but this algorithm has some problem for segmenting simulated MRI images to high number of clusters with different noise levels and real images because of spatial complexities. Anatomical segmentation usually requires information derived from the manual segmentations done by experts, prior knowledge can be useful to modify
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Yao, Hongtai, Xianpei Wang, Le Zhao, et al. "An Object-Based Markov Random Field with Partition-Global Alternately Updated for Semantic Segmentation of High Spatial Resolution Remote Sensing Image." Remote Sensing 14, no. 1 (2021): 127. http://dx.doi.org/10.3390/rs14010127.

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The Markov random field (MRF) method is widely used in remote sensing image semantic segmentation because of its excellent spatial (relationship description) ability. However, there are some targets that are relatively small and sparsely distributed in the entire image, which makes it easy to misclassify these pixels into different classes. To solve this problem, this paper proposes an object-based Markov random field method with partition-global alternately updated (OMRF-PGAU). First, four partition images are constructed based on the original image, they overlap with each other and can be re
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Ding, Ruiyao. "Segmentation analysis of UAV images based on Unet deep learning algorithm." Applied and Computational Engineering 54, no. 1 (2024): 248–53. http://dx.doi.org/10.54254/2755-2721/54/20241644.

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The continuous development of UAV technology provides us with more and higher quality data, in which the application of UAV image segmentation technology can help us better understand and process these data. Traditional image segmentation methods can no longer meet the needs of UAV image segmentation, so researchers have begun to explore the application of deep learning methods in UAV image segmentation.U-Net, as a classical deep learning model, is also widely used in UAV image segmentation.U-Net is characterized by two parts: encoder and decoder, which are used to extract the image features,
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Mohd Ghani, Noor Ain Syazwani, and Abdul Kadir Jumaat. "Selective Segmentation Model for Vector-Valued Images." Journal of Information and Communication Technology 21, No.2 (2022): 149–73. http://dx.doi.org/10.32890/jict2022.21.2.1.

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One of the most important steps in image processing and computer vision for image analysis is segmentation, which can be classified into global and selective segmentations. Global segmentation models can segment whole objects in an image. Unfortunately, these models are unable to segment a specific object that is required for extraction. To overcome this limitation, the selective segmentation model, which is capable of extracting a particular object or region in an image, must be prioritised. Recent selective segmentation models have shown to be effective in segmenting greyscale images. Nevert
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Rozprawy doktorskie na temat "Image segmentation"

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Zeng, Ziming. "Medical image segmentation on multimodality images." Thesis, Aberystwyth University, 2013. http://hdl.handle.net/2160/17cd13c2-067c-451b-8217-70947f89164e.

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Segmentation is a hot issue in the domain of medical image analysis. It has a wide range of applications on medical research. A great many medical image segmentation algorithms have been proposed, and many good segmentation results were obtained. However, due to the noise, density inhomogenity, partial volume effects, and density overlap between normal and abnormal tissues in medical images, the segmentation accuracy and robustness of some state-of-the-art methods still have room for improvement. This thesis aims to deal with the above segmentation problems and improve the segmentation accurac
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Horne, Caspar. "Unsupervised image segmentation /." Lausanne : EPFL, 1991. http://library.epfl.ch/theses/?nr=905.

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Bhalerao, Abhir. "Multiresolution image segmentation." Thesis, University of Warwick, 1991. http://wrap.warwick.ac.uk/60866/.

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Image segmentation is an important area in the general field of image processing and computer vision. It is a fundamental part of the 'low level' aspects of computer vision and has many practical applications such as in medical imaging, industrial automation and satellite imagery. Traditional methods for image segmentation have approached the problem either from localisation in class space using region information, or from localisation in position, using edge or boundary information. More recently, however, attempts have been made to combine both region and boundary information in order to ove
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Craske, Simon. "Natural image segmentation." Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266990.

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Draelos, Timothy John 1961. "INTERACTIVE IMAGE SEGMENTATION." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/276392.

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Salem, Mohammed Abdel-Megeed Mohammed. "Multiresolution image segmentation." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2008. http://dx.doi.org/10.18452/15846.

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Systeme der Computer Vision spielen in der Automatisierung vieler Prozesse eine wichtige Rolle. Die wichtigste Aufgabe solcher Systeme ist die Automatisierung des visuellen Erkennungsprozesses und die Extraktion der relevanten Information aus Bildern oder Bildsequenzen. Eine wichtige Komponente dieser Systeme ist die Bildsegmentierung, denn sie bestimmt zu einem großen Teil die Qualitaet des Gesamtsystems. Fuer die Segmentierung von Bildern und Bildsequenzen werden neue Algorithmen vorgeschlagen. Das Konzept der Multiresolution wird als eigenstaendig dargestellt, es existiert unabhaengig vo
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Hillman, Peter. "Segmentation of motion picture images and image sequences." Thesis, University of Edinburgh, 2002. http://hdl.handle.net/1842/15026.

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For Motion Picture Special Effects, it is often necessary to take a source image of an actor, segment the actor from the unwanted background, and then composite over a new background. The resultant image appears as if the actor was filmed in front of the new background. The standard approach requires the unwanted background to be a blue or green screen. While this technique is capable of handling areas where the foreground (the actor) blends into the background, the physical requirements present many practical problems. This thesis investigates the possibility of segmenting images where the un
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Chowdhury, Md Mahbubul Islam. "Image segmentation for coding." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0017/MQ55494.pdf.

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Wang, Jingdong. "Graph based image segmentation /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20WANG.

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Linnett, L. M. "Multi-texture image segmentation." Thesis, Heriot-Watt University, 1991. http://hdl.handle.net/10399/856.

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Visual perception of images is closely related to the recognition of the different texture areas within an image. Identifying the boundaries of these regions is an important step in image analysis and image understanding. This thesis presents supervised and unsupervised methods which allow an efficient segmentation of the texture regions within multi-texture images. The features used by the methods are based on a measure of the fractal dimension of surfaces in several directions, which allows the transformation of the image into a set of feature images, however no direct measurement of the fra
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Książki na temat "Image segmentation"

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El-Baz, Ayman, Xiaoyi Jiang, and Suri Jasjit, eds. Biomedical Image Segmentation. CRC Press, 2016. http://dx.doi.org/10.4324/9781315372273.

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Bhalerao, Abhir H. Multiresolution image segmentation. typescript, 1991.

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Hati, Avik, Rajbabu Velmurugan, Sayan Banerjee, and Subhasis Chaudhuri. Image Co-segmentation. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8570-6.

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Roland, Wilson. Image segmentation and uncertainty. Research Studies Press, 1988.

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Acton, Scott T., and Nilanjan Ray. Biomedical Image Analysis: Segmentation. Springer International Publishing, 2009. http://dx.doi.org/10.1007/978-3-031-02245-6.

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Siddiqui, Fasahat Ullah, and Abid Yahya. Clustering Techniques for Image Segmentation. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-81230-0.

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Morel, Jean Michel, and Sergio Solimini. Variational Methods in Image Segmentation. Birkhäuser Boston, 1995. http://dx.doi.org/10.1007/978-1-4684-0567-5.

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Köster, Klaus. Robust clustering and image segmentation. University of Birmingham, 1999.

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Martin, Ian John. Multi-spectral image segmentation and compression. typescript, 1999.

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Bhanu, Bir, and Sungkee Lee. Genetic Learning for Adaptive Image Segmentation. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2774-9.

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Części książek na temat "Image segmentation"

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Jähne, Bernd. "Segmentation." In Digital Image Processing. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-11565-7_10.

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Sundararajan, D. "Segmentation." In Digital Image Processing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6113-4_10.

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Jähne, Bernd. "Segmentation." In Digital Image Processing. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-21817-4_10.

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Bräunl, Thomas, Stefan Feyrer, Wolfgang Rapf, and Michael Reinhardt. "Segmentation." In Parallel Image Processing. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04327-1_7.

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Soille, Pierre. "Segmentation." In Morphological Image Analysis. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03939-7_9.

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Jähne, Bernd. "Segmentation." In Digital Image Processing. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04781-1_16.

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Soille, Pierre. "Segmentation." In Morphological Image Analysis. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05088-0_9.

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Awcock, G. J., and R. Thomas. "Segmentation." In Applied Image Processing. Macmillan Education UK, 1995. http://dx.doi.org/10.1007/978-1-349-13049-8_5.

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Jähne, Bernd. "Segmentation." In Digital Image Processing. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03174-2_10.

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Adamo, Jean-Marc. "Image Segmentation." In Multi-Threaded Object-Oriented MPI-Based Message Passing Interface. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5761-6_10.

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Streszczenia konferencji na temat "Image segmentation"

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Liu, Jinze, Jayaram K. Udupa, Drew A. Torigian, et al. "Diffusion semantic segmentation: a generative segmentation model based on joint distributions." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3047126.

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Hua, Shiqi, Dunbo Ning, Wei Xie, and Hao Sun. "Segmentation Foundation Model-Aided Medical Image Segmentation." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821961.

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Kudo, Chihana, Xinyu Yan, and Kyosuke Yoshimi. "Microstructural Analysis of MoSiBTiC Alloys Based on Scanning Electron Microscopy Image Segmentation." In AM-EPRI 2024. ASM International, 2024. http://dx.doi.org/10.31399/asm.cp.am-epri-2024p0507.

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Abstract The microstructure of MoSiBTiC alloys is very complex, with three to four constituent phases and characteristic structures such as fine precipitates and lamellar structures. To perform the microstructural analysis efficiently, image segmentation was first performed for each phase of the microstructural images. Utilizing the Trainable Weka Segmentation method based on machine learning, the required segmentation time was dramatically reduced. Furthermore, by pre-adjusting the contrast of the images, the segmentation could be performed accurately for gray phases with different shades of
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Costa, Gustavo Martins L. da, Anna P. C. Rodrigues, Gabriel Barbosa da Fonseca, Zenilton K. G. do Patrocínio Jr, Giovanna Ribeiro Souto, and Silvio Jamil F. Guimarães. "Single-Shot Object Detection and Supervised Image Segmentation for Analysing Cell Images Obtained by Immunohistochemistry." In Anais Estendidos da Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sibgrapi.est.2023.27463.

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Analyzing cell images and identifying them correctly is a fundamental task in the immunohistochemical exam. In this paper we propose a novel method to segment FoxP3+ Regulatory T cells (Treg) images automatically, in order to assist healthcare professionals in the task of identifying and counting potentially cancerous cells. The proposed method relies on combining an object detection network, which is tailor-made for microscopy images, with a marker-based image segmentation method to produce the final segmentation, while requiring only a 50x50 training patch to do so. Our pipeline consists on
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Li, Shaohua, Xiuchao Sui, Xiangde Luo, Xinxing Xu, Yong Liu, and Rick Goh. "Medical Image Segmentation using Squeeze-and-Expansion Transformers." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/112.

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Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn image features that incorporate large context while keep high spatial resolutions. To approach this goal, the most widely used methods -- U-Net and variants, extract and fuse multi-scale features. However, the fused features still have small "effective receptive fields" with a focus on local image cues, limiting their performance. In this work, we propose Segtran, an alternative segmentation framework based on transform
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Mansilla, Lucy, and Paulo Miranda. "Image Segmentation by Image Foresting Transform with Boundary Polarity and Shape Constraints." In XXVIII Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/ctd.2015.10003.

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Image segmentation, such as to extract an object from a background, is very useful for medical and biological image analysis. In this work, we propose new segmentation methods for interactive segmentation of multidimensional images, based on the Image Foresting Transform (IFT), by exploiting for the first time non-smooth connectivity functions (NSCF) with a strong theoretical background. The new algorithms provide global optimum solutions according to an energy function of graph cut, subject to high-level boundary constraints (polarity and shape). Our experimental results indicate substantial
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Paulose, Suja, and D. Veera Vanitha. "Image Segmentation using Optimization Algorithm: A Survey." In 2nd International Conference on Modern Trends in Engineering Technology and Management. AIJR Publisher, 2023. http://dx.doi.org/10.21467/proceedings.160.41.

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Image segmentation has proven to be an important step in the processing of images, computer vision algorithms, etc. It splits an image into different regions. This survey reviews major contributions in the healthcare l field using deep learning, including the common problems published over the last few years, and also explains the basics of deep learning concepts applicable to medical image segmentation. To solve current problems and improve the development of medical image segmentation problems, the Efficient Net Atrous convolutional encoder & and decoder can be used for segmentation in f
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Taouli, Sidi Ahmed. "Research on the Image Segmentation by Watershed Transforms." In 3rd International Conference on Machine Learning Techniques and Data Science (MLDS 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.122108.

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Segmentation is a very important step in medical image processing. The mathematical morphology is very suitable for the pretreatment and segmentation of medical images, which present rich information content. In this work we presented a segmentation paradigm by Watershed preceded by a filtering to eliminate insignificant minima, a marking to remove unmarked minima, and finally we implemented a hierarchical segmentation using the mosaic image of the original image. In principle, watershed segmentation depends on ridges to perform a proper segmentation, a property that is often fulfilled in cont
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Dias, 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.

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Brazil is the largest coffee producer in the world, and then there are many challenges to maintain the high quality and purity of the beans. Thus, it is important to study coffee plants, and help agronomists to detect diseases, such as rust, with resources of computer science. In this work, it is described experiments using image segmentation algorithm JSEG, which is capable to segment images in multi-scale. Using a coffee tree image database RoCoLe (Robusta Coffee Leaf Images), the JSEG algorithm is used to segment these images in four scales. It is selected typical segments in each scale and
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Nguyen, Chanh D. Tr, Huu-Hung Dao, Minh-Thanh Huynh, and Tan Phu Ward. "ResCap: Residual Capsules Network for Medical Image Segmentation." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.058.

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Raporty organizacyjne na temat "Image segmentation"

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Sharma, Karan. The Link Between Image Segmentation and Image Recognition. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.199.

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Kim, A., I. Pollak, H. Krim, and A. S. Willsky. Scale-Based Robust Image Segmentation. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada457838.

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Franaszek, Marek. Gauging Difficulty of Image Segmentation. National Institute of Standards and Technology, 2022. http://dx.doi.org/10.6028/nist.tn.2207.

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Ekeland, I., P. L. Lions, Y. Meyer, and J. M. Morel. Vibrations, Viscosity, Wavelets and Image Segmentation. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada225750.

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Franaszek, Marek. Gauging the difficulty of image segmentation. National Institute of Standards and Technology, 2022. http://dx.doi.org/10.6028/nist.tn.2207-upd1.

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Shaw, K. B., and M. C. Lohrenz. A Survey of Digital Image Segmentation Algorithms. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada499374.

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Ghosh, Payel. Medical Image Segmentation Using a Genetic Algorithm. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.25.

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Wanna, Selma. Uncertainty Quantification for the Image Segmentation Task. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1900437.

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Xu, Y., and E. C. Uberbacher. 2-D image segmentation using minimum spanning trees. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/113991.

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Wu, Jin Chu, Michael Halter, Raghu N. Kacker, John T. Elliot, and Anne L. Plant. Measurement uncertainty in cell image segmentation data analysis. National Institute of Standards and Technology, 2013. http://dx.doi.org/10.6028/nist.ir.7954.

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