Results 31 to 40 of about 19,154,024 (348)
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation [PDF]
European Conference on Computer Vision, 2018 Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling
Liang-Chieh Chen +4 more
semanticscholar +1 more source
Medical Transformer: Gated Axial-Attention for Medical Image Segmentation [PDF]
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021 Over the past decade, Deep Convolutional Neural Networks have been widely adopted for medical image segmentation and shown to achieve adequate performance.
Jeya Maria Jose Valanarasu +3 more
semanticscholar +1 more source
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
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
openaire +2 more sources
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
Customized Segment Anything Model for Medical Image Segmentation [PDF]
arXiv.org, 2023 We propose SAMed, a general solution for medical image segmentation. Different from the previous methods, SAMed is built upon the large-scale image segmentation model, Segment Anything Model (SAM), to explore the new research paradigm of customizing ...
Kaiwen Zhang, Dong Liu
semanticscholar +1 more source
Environment Conservation Journal, 2020 Image segmentation is an essential and critical step in huge number of applications of image processing. Accuracy of image segmentation influence retrieved information for further processing in classification and other task.
Neeraj Kumari +3 more
doaj +1 more source
Automatic texture segmentation for content-based image retrieval application [PDF]
, 2006 In this article, a brief review on texture segmentation is presented, before a novel automatic texture segmentation algorithm is developed. The algorithm is based on a modified discrete wavelet frames and the mean shift algorithm.
Fauzi, M.F.A. +3 more
core +2 more sources
MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation [PDF]
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023 There has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achieving performances equivalent to those in natural images challenging ...
Saikat Roy +7 more
semanticscholar +1 more source
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by ...
Vijay Badrinarayanan +2 more
semanticscholar +1 more source