Results 21 to 30 of about 9,167 (192)
Hybrid superpixel segmentation [PDF]
2015 International Conference on Image and Vision Computing New Zealand (IVCNZ), 2015 Superpixel over-segment image into meaningful clusters so that pixels in each cluster belong to one object. Many state-of-art superpixel algorithms have to make trade-offs between different concerns. As a result, algorithms that can produce good result in some situations fail in another.
Yu, Y, Lai, S, Liu, Y, Du, T
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Bioengineering, 2022 Lung segmentation of chest X-ray (CXR) images is a fundamental step in many diagnostic applications. Most lung field segmentation methods reduce the image size to speed up the subsequent processing time.
Chien-Cheng Lee +3 more
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BASS: Boundary-Aware Superpixel Segmentation [PDF]
2016 23rd International Conference on Pattern Recognition (ICPR), 2016 This work is partly funded by the Spanish MINECO project RobInstruct TIN2014-58178-R, by the ERA-Net Chistera project I-DRESS PCIN-2015-147 and by the EU project AEROARMS H2020-ICT-2014-1-644271. A. Rubio is supported by the industrial doctorate grant 2015-DI-010 of the AGAUR.
Rubio, Antonio +3 more
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Superpixel Generation for SAR Imagery Based on Fast DBSCAN Clustering With Edge Penalty
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 In this article, we propose an adaptive superpixel generation algorithm for synthetic aperture radar (SAR) imagery, which is implemented based on fast density-based spatial clustering of applications with noise (DBSCAN) clustering and superpixel merging ...
Liang Zhang +5 more
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Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC
Brain Sciences, 2020 Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI).
Yu Wang, Qi Qi, Xuanjing Shen
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SAR Image Segmentation Based on Fisher Vector Superpixel Generation and Label Revision
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 This article addresses the problem of superpixel-bases segmentation of synthetic aperture radar (SAR) images. Most superpixel segmentation methods have difficulties in segmenting adjacent regions with similar gray values, due to only considering spatial ...
Ronghua Shang +5 more
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Video Segmentation with Superpixels [PDF]
, 2013 Due to its importance, video segmentation has regained interest recently. However, there is no common agreement about the necessary ingredients for best performance. This work contributes a thorough analysis of various within- and between-frame affinities suitable for video segmentation.
Galasso F, Cipolla R, Schiele B
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Superpixel Segmentation With Fully Convolutional Networks [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 In computer vision, superpixels have been widely used as an effective way to reduce the number of image primitives for subsequent processing. But only a few attempts have been made to incorporate them into deep neural networks. One main reason is that the standard convolution operation is defined on regular grids and becomes inefficient when applied to
Yang, Fengting +3 more
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Superpixel Segmentation Algorithm by Spatial Constrained Density Clustering
Journal of Harbin University of Science and Technology, 2020 The superpixel segmentation is an important pre-processing step in computer image processing. Thetraditional superpixel segmentation algorithm based on density clustering is better for boundary processing，but the resulting superpixel shape is irregular ...
HAN Jian-hui, TANG Jun-chao
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Detection of Hypergranulation Tissue in Chronic Wound Images Using Artificial Intelligence Algorithms. [PDF]
Wound Repair RegenABSTRACT Hypergranulation in chronic wounds reflects impaired healing, leading to delayed recovery, increased risk of infection and higher treatment costs for healthcare systems. Despite its impact, hypergranulation is often misidentified in the early stages, hindering timely intervention. This study presents a deep learning‐based method to distinguish
Reifs D +3 more
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