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Bibliografías temáticas / Classification/segmentation

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Artículos de revistas

Literatura académica sobre el tema "Classification/segmentation"

Autor: Grafiati

Publicado: 1 de junio de 2024

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Artículos de revistas sobre el tema "Classification/segmentation"

1

Levner, Ilya, and Hong Zhang. "Classification-Driven Watershed Segmentation." IEEE Transactions on Image Processing 16, no. 5 (2007): 1437–45. http://dx.doi.org/10.1109/tip.2007.894239.

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Pavlidis, Theo, and Jiangying Zhou. "Page segmentation and classification." CVGIP: Graphical Models and Image Processing 54, no. 6 (1992): 484–96. http://dx.doi.org/10.1016/1049-9652(92)90068-9.

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3

Khanykov, I. G. "Classification of image segmentation algorithms." Izvestiâ vysših učebnyh zavedenij. Priborostroenie 61, no. 11 (2018): 978–87. http://dx.doi.org/10.17586/0021-3454-2018-61-11-978-987.

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4

Wang, Ying, Jie Su, Qiuyu Xu, and Yixin Zhong. "A Collaborative Learning Model for Skin Lesion Segmentation and Classification." Diagnostics 13, no. 5 (2023): 912. http://dx.doi.org/10.3390/diagnostics13050912.

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The automatic segmentation and classification of skin lesions are two essential tasks in computer-aided skin cancer diagnosis. Segmentation aims to detect the location and boundary of the skin lesion area, while classification is used to evaluate the type of skin lesion. The location and contour information of lesions provided by segmentation is essential for the classification of skin lesions, while the skin disease classification helps generate target localization maps to assist the segmentation task. Although the segmentation and classification are studied independently in most cases, we fi

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5

Sekhar, Mr Ch, Ms A. Sharmila, Mr Ch Narayana, et al. "Osteoporosis Diagnosis through Visual Segmentation and Classification: Extensive Review." International Journal of Research Publication and Reviews 5, no. 3 (2024): 3748–53. http://dx.doi.org/10.55248/gengpi.5.0324.0771.

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6

Hyun-Cheol Park, Hyun-Cheol Park, Raman Ghimire Hyun-Cheol Park, Sahadev Poudel Raman Ghimire, and Sang-Woong Lee Sahadev Poudel. "Deep Learning for Joint Classification and Segmentation of Histopathology Image." 網際網路技術學刊 23, no. 4 (2022): 903–10. http://dx.doi.org/10.53106/160792642022072304025.

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<p>Liver cancer is one of the most prevalent cancer deaths worldwide. Thus, early detection and diagnosis of possible liver cancer help in reducing cancer death. Histopathological Image Analysis (HIA) used to be carried out traditionally, but these are time-consuming and require expert knowledge. We propose a patch-based deep learning method for liver cell classification and segmentation. In this work, a two-step approach for the classification and segmentation of whole-slide image (WSI) is proposed. Since WSIs are too large to be fed into convolutional neural networks (CNN) directly, we

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7

Pandeya, Yagya Raj, Bhuwan Bhattarai, and Joonwhoan Lee. "Tracking the Rhythm: Pansori Rhythm Segmentation and Classification Methods and Datasets." Applied Sciences 12, no. 19 (2022): 9571. http://dx.doi.org/10.3390/app12199571.

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This paper presents two methods to understand the rhythmic patterns of the voice in Korean traditional music called Pansori. We used semantic segmentation and classification-based structural analysis methods to segment the seven rhythmic categories of Pansori. We propose two datasets; one is for rhythm classification and one is for segmentation. Two classification and two segmentation neural networks are trained and tested in an end-to-end manner. The standard HR network and DeepLabV3+ network are used for rhythm segmentation. A modified HR network and a novel GlocalMuseNet are used for the cl

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8

Vohra, Sumit K., and Dimiter Prodanov. "The Active Segmentation Platform for Microscopic Image Classification and Segmentation." Brain Sciences 11, no. 12 (2021): 1645. http://dx.doi.org/10.3390/brainsci11121645.

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Image segmentation still represents an active area of research since no universal solution can be identified. Traditional image segmentation algorithms are problem-specific and limited in scope. On the other hand, machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation. However, machine learning only can not address the question as to which features are appropriate for a certain classification problem. The article presents an automated image segmentation and classification platform,

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9

Abbas, Khamael, and Mustafa Rydh. "Satellite Image Classification and Segmentation by Using JSEG Segmentation Algorithm." International Journal of Image, Graphics and Signal Processing 4, no. 10 (2012): 48–53. http://dx.doi.org/10.5815/ijigsp.2012.10.07.

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10

Mittal, Praveen, and Charul Bhatnagar. "Detection of DME by Classification and Segmentation Using OCT Images." Webology 19, no. 1 (2022): 601–12. http://dx.doi.org/10.14704/web/v19i1/web19043.

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Optical Coherence Tomography (OCT) is a developing medical scanning technique proposing non- protruding scanning with high resolution for biological tissues. It is extensively employed in optics to accomplish investigative scanning of the eye, especially the retinal layers. Various medical research works are conducted to evaluate the usage of Optical Coherence Tomography to detect diseases like DME. The current study provides an innovative, completely automated algorithm for disease detection such as DME through OCT scanning. We performed the classification and segmentation for the detection o

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