Results 21 to 30 of about 6,662,539 (239)
MULTIFRACTAL SEGMENTATION OF IMAGES [PDF]
Fractals, 1994 We propose a multifractal approach to the problem of image analysis. We show that an alternative description of images, based on a multifractal characterization, can be used instead of the classical approach that involves smoothing of the discrete data in order to compute local extrema.
Lévy Véhel, Jacques, Mignot, Pascal
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UNet++: A Nested U-Net Architecture for Medical Image Segmentation [PDF]
DLMIA/ML-CDS@MICCAI, 2018 In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of ...
Zongwei Zhou +3 more
semanticscholar +1 more source
Image Segmentation Using Deep Learning: A Survey [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among others, and ...
Shervin Minaee +5 more
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Image Segmentation Techniques for Intelligent Monitoring of Putonghua Examinations
Advances in Mathematical Physics, 2022 Image recognition and image processing usually contain the technique of image segmentation. Excellent segmentation results can directly affect the accuracy of image recognition and processing. The essence of image segmentation is to segment each frame of
Hui Liu
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UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020 Recently, a growing interest has been seen in deep learning-based semantic segmentation. UNet, which is one of deep learning networks with an encoder-decoder architecture, is widely used in medical image segmentation.
Huimin Huang +8 more
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A Method of Polished Rice Image Segmentation Based on YO-LACTS for Quality Detection
Agriculture, 2023 The problem of small and multi-object polished rice image segmentation has always been one of importance and difficulty in the field of image segmentation.
Jinbo Zhou +5 more
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016 In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit.
Liang-Chieh Chen +4 more
semanticscholar +1 more source
Sensors, 2021 High-spatial-resolution images play an important role in land cover classification, and object-based image analysis (OBIA) presents a good method of processing high-spatial-resolution images.
Shuang Hao, Yuhuan Cui, Jie Wang
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V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation [PDF]
International Conference on 3D Vision, 2016 Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used ...
F. Milletarì +2 more
semanticscholar +1 more source
UNeXt: MLP-based Rapid Medical Image Segmentation Network [PDF]
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022 UNet and its latest extensions like TransUNet have been the leading medical image segmentation methods in recent years. However, these networks cannot be effectively adopted for rapid image segmentation in point-of-care applications as they are parameter-
Jeya Maria Jose Valanarasu +1 more
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