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Academic literature on the topic 'Segmentation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Segmentation"

Saker, Halima, Rainer Machné, Jörg Fallmann, Douglas B. Murray, Ahmad M. Shahin, and Peter F. Stadler. "Weighted Consensus Segmentations." Computation 9, no. 2 (2021): 17. http://dx.doi.org/10.3390/computation9020017.

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The problem of segmenting linearly ordered data is frequently encountered in time-series analysis, computational biology, and natural language processing. Segmentations obtained independently from replicate data sets or from the same data with different methods or parameter settings pose the problem of computing an aggregate or consensus segmentation. This Segmentation Aggregation problem amounts to finding a segmentation that minimizes the sum of distances to the input segmentations. It is again a segmentation problem and can be solved by dynamic programming. The aim of this contribution is (

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Buser, Myrthe A. D., Alida F. W. van der Steeg, Marc H. W. A. Wijnen, et al. "Radiologic versus Segmentation Measurements to Quantify Wilms Tumor Volume on MRI in Pediatric Patients." Cancers 15, no. 7 (2023): 2115. http://dx.doi.org/10.3390/cancers15072115.

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Wilms tumor is a common pediatric solid tumor. To evaluate tumor response to chemotherapy and decide whether nephron-sparing surgery is possible, tumor volume measurements based on magnetic resonance imaging (MRI) are important. Currently, radiological volume measurements are based on measuring tumor dimensions in three directions. Manual segmentation-based volume measurements might be more accurate, but this process is time-consuming and user-dependent. The aim of this study was to investigate whether manual segmentation-based volume measurements are more accurate and to explore whether these

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Nanni, Loris, Daniel Fusaro, Carlo Fantozzi, and Alberto Pretto. "Improving Existing Segmentators Performance with Zero-Shot Segmentators." Entropy 25, no. 11 (2023): 1502. http://dx.doi.org/10.3390/e25111502.

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This paper explores the potential of using the SAM (Segment-Anything Model) segmentator to enhance the segmentation capability of known methods. SAM is a promptable segmentation system that offers zero-shot generalization to unfamiliar objects and images, eliminating the need for additional training. The open-source nature of SAM allows for easy access and implementation. In our experiments, we aim to improve the segmentation performance by providing SAM with checkpoints extracted from the masks produced by mainstream segmentators, and then merging the segmentation masks provided by these two

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Liu, Qiming, Qifan Lu, Yezi Chai, et al. "Radiomics-Based Quality Control System for Automatic Cardiac Segmentation: A Feasibility Study." Bioengineering 10, no. 7 (2023): 791. http://dx.doi.org/10.3390/bioengineering10070791.

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Purpose: In the past decade, there has been a rapid increase in the development of automatic cardiac segmentation methods. However, the automatic quality control (QC) of these segmentation methods has received less attention. This study aims to address this gap by developing an automatic pipeline that incorporates DL-based cardiac segmentation and radiomics-based quality control. Methods: In the DL-based localization and segmentation part, the entire heart was first located and cropped. Then, the cropped images were further utilized for the segmentation of the right ventricle cavity (RVC), myo

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Sithole, G., and L. Majola. "FRAMEWORK FOR COMPARING SEGMENTATION ALGORITHMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W5 (May 11, 2015): 131–36. http://dx.doi.org/10.5194/isprsarchives-xl-4-w5-131-2015.

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The notion of a ‘Best’ segmentation does not exist. A segmentation algorithm is chosen based on the features it yields, the properties of the segments (point sets) it generates, and the complexity of its algorithm. The segmentation is then assessed based on a variety of metrics such as homogeneity, heterogeneity, fragmentation, etc. Even after an algorithm is chosen its performance is still uncertain because the landscape/scenarios represented in a point cloud have a strong influence on the eventual segmentation. Thus selecting an appropriate segmentation algorithm is a process of trial and er

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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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Harkey, Matthew S., Nicholas Michel, Christopher Kuenze, et al. "Validating a Semi-Automated Technique for Segmenting Femoral Articular Cartilage on Ultrasound Images." CARTILAGE 13, no. 2 (2022): 194760352210930. http://dx.doi.org/10.1177/19476035221093069.

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Objective To validate a semi-automated technique to segment ultrasound-assessed femoral cartilage without compromising segmentation accuracy to a traditional manual segmentation technique in participants with an anterior cruciate ligament injury (ACL). Design We recruited 27 participants with a primary unilateral ACL injury at a pre-operative clinic visit. One investigator performed a transverse suprapatellar ultrasound scan with the participant’s ACL injured knee in maximum flexion. Three femoral cartilage ultrasound images were recorded. A single expert reader manually segmented the femoral

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Mendoza Garay, Juan Ignacio. "Segmentation boundaries in accelerometer data of arm motion induced by music: Online computation and perceptual assessment." Human Technology 18, no. 3 (2022): 250–66. http://dx.doi.org/10.14254/1795-6889.2022.18-3.4.

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Segmentation is a cognitive process involved in the understanding of information perceived through the senses. Likewise, the automatic segmentation of data captured by sensors may be used for the identification of patterns. This study is concerned with the segmentation of dancing motion captured by accelerometry and its possible applications, such as pattern learning and recognition, or gestural control of devices. To that effect, an automatic segmentation system was formulated and tested. Two participants were asked to ‘dance with one arm’ while their motion was measured by an accelerometer.

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Schmidt-Richberg, A., J. Fiehler, T. Illies, et al. "Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets." Methods of Information in Medicine 51, no. 05 (2012): 415–22. http://dx.doi.org/10.3414/me11-02-0037.

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Summary Objectives: Exact cerebrovascular segmentations are required for several applications in today’s clinical routine. A major drawback of typical automatic segmentation methods is the occurrence of gaps within the segmentation. These gaps are typically located at small vessel structures exhibiting low intensities. Manual correction is very time-consuming and not suitable in clinical practice. This work presents a post-processing method for the automatic detection and closing of gaps in cerebrovascular segmentations. Methods: In this approach, the 3D centerline is calculated from an availa

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Priego, Torres Blanca María, and Richard J. Duro. "An approach for the customized high-dimensional segmentation of remote sensing hyperspectral images." Sensors 19, no. 13 (2019): 2887. https://doi.org/10.3390/s19132887.

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The work presents a methodology for the customized segmentation of remote sensing hyperspectral images using a multigradient cellular automaton (MGCA) approach coupled with an evolutionary algorithm (ECAS-II). The study addresses three main challenges in hyperspectral image segmentation: the need for segmentations tailored to user requirements, the scarcity of adequately labeled reference images, and the loss of information that occurs when high-dimensional images are projected into lower-dimensional spaces before segmentation. The proposed methodology allows for the segmentation of multidimen

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Dissertations / Theses on the topic "Segmentation"

Ross, Michael G. (Michael Gregory) 1975. "Learning static object segmentation from motion segmentation." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/34470.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.<br>Includes bibliographical references (p. 105-110).<br>This thesis describes the SANE (Segmentation According to Natural Examples) algorithm for learning to segment objects in static images from video data. SANE uses background subtraction to find the segmentation of moving objects in videos. This provides object segmentation information for each video frame. The collection of frames and segmentations forms a training set that SANE uses to learn the image and shape properties th

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Vyas, Aseem. "Medical Image Segmentation by Transferring Ground Truth Segmentation." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32431.

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The segmentation of medical images is a difficult task due to the inhomogeneous intensity variations that occurs during digital image acquisition, the complicated shape of the object, and the medical expert’s lack of semantic knowledge. Automated segmentation algorithms work well for some medical images, but no algorithm has been general enough to work for all medical images. In practice, most of the time the segmentation results are corrected by the experts before the actual use. In this work, we are motivated to determine how to make use of manually segmented data in automatic segmentation.

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Jomaa, Diala. "Fingerprint Segmentation." Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4264.

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In this thesis, a new algorithm has been proposed to segment the foreground of the fingerprint from the image under consideration. The algorithm uses three features, mean, variance and coherence. Based on these features, a rule system is built to help the algorithm to efficiently segment the image. In addition, the proposed algorithm combine split and merge with modified Otsu. Both enhancements techniques such as Gaussian filter and histogram equalization are applied to enhance and improve the quality of the image. Finally, a post processing technique is implemented to counter the undesirable

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Scholte, Huibert Steven. "Scene segmentation." [S.l. : Amsterdam : s.n.] ; Universiteit van Amsterdam [Host], 2003. http://dare.uva.nl/document/70449.

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Horne, Caspar. "Unsupervised image segmentation /." Lausanne : EPFL, 1991. http://library.epfl.ch/theses/?nr=905.

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Sundøy, Kristoffer Johan. "Audiovisual Contents Segmentation." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11264.

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The objective of this thesis is to detect high level semantic ideas to help to impose a structure on television talk shows. Indexing TV-shows is a subject that, to our knowledge, is rarely talked about in the scientific community.There is no common understanding of what this imposed structure should look like. We can say that the purpose is to organise the audiovisual content into sections that convey a specific information. It thus encompasses issues as diverse as scene segmentation, speech noise detection, speaker identification, etc. The basic problem of structuring is the gap between the

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Camilleri, Kenneth P. "Multiresolution texture segmentation." Thesis, University of Surrey, 1999. http://epubs.surrey.ac.uk/843549/.

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The problem of unsupervised texture segmentation was studied and a texture segmentation algorithm was developed making use of the minimum number of prior assumptions. In particular, no prior information about the type of textures, the number of textures and the appropriate scale of analysis for each texture was required. The texture image was analysed by the multiresolution Gabor expansion. The Gabor expansion generates a large number of features for each image and the most suitable feature space for segmentation needs to be determined automatically. The two-point correlation function was used

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Debeir, Olivier. "Segmentation supervisée d'images." Doctoral thesis, Universite Libre de Bruxelles, 2001. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211474.

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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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Fournier, Christopher. "Evaluating Text Segmentation." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/24064.

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This thesis investigates the evaluation of automatic and manual text segmentation. Text segmentation is the process of placing boundaries within text to create segments according to some task-dependent criterion. An example of text segmentation is topical segmentation, which aims to segment a text according to the subjective definition of what constitutes a topic. A number of automatic segmenters have been created to perform this task, and the question that this thesis answers is how to select the best automatic segmenter for such a task. This requires choosing an appropriate segmentation eval

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Books on the topic "Segmentation"

McDonald, Malcolm, and Ian Dunbar. Market Segmentation. Palgrave Macmillan UK, 1998. http://dx.doi.org/10.1007/978-1-349-26591-6.

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McDonald, Malcolm, and Ian Dunbar, eds. Market Segmentation. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119207863.

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Wedel, Michel, and Wagner A. Kamakura. Market Segmentation. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4651-1.

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Malcolm, McDonald. Market Segmentation. Elsevier Science & Technology, 2010.

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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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Dolnicar, Sara, Bettina Grün, and Friedrich Leisch. Market Segmentation Analysis. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8818-6.

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Protopappa-Sieke, Margarita, and Ulrich W. Thonemann, eds. Supply Chain Segmentation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54133-4.

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He, Jia, Chang-Su Kim, and C. C. Jay Kuo. Interactive Segmentation Techniques. Springer Singapore, 2014. http://dx.doi.org/10.1007/978-981-4451-60-4.

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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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Book chapters on the topic "Segmentation"

McDonald, Malcolm, and Ian Dunbar. "Preparing for Segmentation." In Market Segmentation. Palgrave Macmillan UK, 1998. http://dx.doi.org/10.1007/978-1-349-26591-6_1.

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McDonald, Malcolm, and Ian Dunbar. "Company Competitiveness and the Portfolio Matrix (Step 12)." In Market Segmentation. Palgrave Macmillan UK, 1998. http://dx.doi.org/10.1007/978-1-349-26591-6_10.

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McDonald, Malcolm, and Ian Dunbar. "Setting Marketing Objectives and Strategies for Identified Segments." In Market Segmentation. Palgrave Macmillan UK, 1998. http://dx.doi.org/10.1007/978-1-349-26591-6_11.

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McDonald, Malcolm, and Ian Dunbar. "Organisational Issues in Market Segmentation." In Market Segmentation. Palgrave Macmillan UK, 1998. http://dx.doi.org/10.1007/978-1-349-26591-6_12.

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McDonald, Malcolm, and Ian Dunbar. "The Contribution of Segmentation to Business Planning: A Case Study of the Rise, Fall and Recovery of ICI Fertilizers." In Market Segmentation. Palgrave Macmillan UK, 1998. http://dx.doi.org/10.1007/978-1-349-26591-6_13.

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McDonald, Malcolm, and Ian Dunbar. "Market Mapping (Step 1)." In Market Segmentation. Palgrave Macmillan UK, 1998. http://dx.doi.org/10.1007/978-1-349-26591-6_2.

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McDonald, Malcolm, and Ian Dunbar. "Who Buys (Step 2)." In Market Segmentation. Palgrave Macmillan UK, 1998. http://dx.doi.org/10.1007/978-1-349-26591-6_3.

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McDonald, Malcolm, and Ian Dunbar. "What, Where, When and How (Step 3)." In Market Segmentation. Palgrave Macmillan UK, 1998. http://dx.doi.org/10.1007/978-1-349-26591-6_4.

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McDonald, Malcolm, and Ian Dunbar. "Who Buys What, Where, When and How (Step 4)." In Market Segmentation. Palgrave Macmillan UK, 1998. http://dx.doi.org/10.1007/978-1-349-26591-6_5.

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McDonald, Malcolm, and Ian Dunbar. "Why it is Bought (Step 5)." In Market Segmentation. Palgrave Macmillan UK, 1998. http://dx.doi.org/10.1007/978-1-349-26591-6_6.

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Conference papers on the topic "Segmentation"

Tsai, Yi-Chin, and Yung-Nien Sun. "KiTS19 Challenge Segmentation." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.021.

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Sirazitdinov, Ilyas, and Bulat Ibragimov. "Kits19 segmentation challenge." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.062.

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Sharma, Rochan. "Kidney Tumour Segmentation." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.080.

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Dahiya, Navdeep, and Alok Sharma. "Segmentation of Kidney Tumor." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.060.

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Chen, Tung-I., Min-Sheng Wu, Yu-Cheng Chang, and Jhih-Yuan Lin. "2019 Kidney Tumor Segmentation Challenge: Medical Image Segmentation with Two-Stage Process." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.065.

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Cui, Zhiying. "3D Segmentation For Kidney Data." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.081.

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Meng, Zhe. "Kidney Segmentation Framework using 3D CNN." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.040.

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Adão, Milena Menezes, Silvio Jamil F. Guimarães, and Zenilton K. G. Patrocı́nio Jr. "Evaluation of machine learning applied to the realignment of hierarchies for image segmentation." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8311.

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A hierarchical image segmentation is a set of image segmentations at different detail levels. However, objects can be located at different scales due to their size differences or to their distinct distances from the camera. In literature, many works have been developed to improve hierarchical image segmentation results. One possible solution is to realign the hierarchy such that every region containing an object (or its parts) is at the same level. In this work, we have explored the use of random forest and artificial neural network as regressors models to predict score values for regions belo

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Araújo, Rafael Luz, Daniel de S. Luz, Bruno Vicente de Lima, et al. "Quantifying the effects of segmentation in image classification for melanoma recognition." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2024. https://doi.org/10.5753/eniac.2024.245228.

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Melanoma remains the leading cause of skin cancer-related deaths worldwide, emphasizing the critical need for early detection to enhance survival rates. Computational methods are pivotal in aiding its diagnosis through medical imaging, necessitating accurate lesion segmentation to facilitate effective interpretation. Our study investigates the comparative efficacy of skin lesion classification with and without segmentation, leveraging pre-trained convolutional neural networks (CNNs) and CapsNet architectures. Findings underscore CNNs’ superiority, highlighting segmentation’s beneficial impact

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Hou, Xiaoshuai, Chunmei Xie, Fengyi Li, and Yang Nan. "Cascaded Semantic Segmentation for Kidney and Tumor." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.002.

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Reports on the topic "Segmentation"

Kobla, Vikrant, David Doermann, and Azriel Rosenfeld. Compressed Video Segmentation. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada458852.

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King, Lucy. FSA Consumer segmentation. Food Standards Agency, 2021. http://dx.doi.org/10.46756/sci.fsa.bmo506.

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For our audiences, it is important to find out how their attitudes and behaviours relating to food safety differ, in order to understand who is more likely to take food safety risks and in what context. This is essential for effective communications and helps us to shape food safety policy. The audiences in these documents have been created using attitudinal and behavioural segmentation that categorises people based on their attitudes to food and their reported hygiene and food safety behaviours.

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Pool, Tristen. Segmentation Model Distillation. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2429880.

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Bonney, Bradford L. Non-Orthogonal Iris Segmentation. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada437155.

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Brown, S. Kathi. Retirement Attitudes Segmentation Survey. AARP Research, 2013. http://dx.doi.org/10.26419/res.00069.001.

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Vu, Tuan-Hung. Zero-shot Semantic Segmentation. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.s35o4gav.1.

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Aroonmanakun, Wirote. Thoughts on word and sentence segmentation in Thai. Chulalongkorn University, 2007. https://doi.org/10.58837/chula.res.2007.92.

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This paper discusses problems of word and sentence segmentation in Thai. Disagreements on word segmentation are caused mostly from compound words. To set a standard resource and tool of word segmentation, we suggest that only simple words and true compound words should be segmented in the process of word segmentation. Other compounds can be grouped later by the same means as multiword identification in other languages. Sentence segmentation is also difficult because the boundary of sentence in Thai is fuzzy. We suggest that a discourse should be seen as a combination of clauses rather than sen

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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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Shah, Jayant. Object Oriented Segmentation of Images. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada290792.

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Snyder, Wesley E. Segmentation Using Multispectral Adaptive Contours. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada424462.

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