Results 11 to 20 of about 919,707 (194)
Path Aggregation Network for Instance Segmentation [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting information flow in proposal-based instance segmentation framework.
Shu Liu +4 more
semanticscholar +1 more source
Human‐in‐the‐Loop Segmentation of Earth Surface Imagery
Earth and Space Science, 2022 Segmentation, or the classification of pixels (grid cells) in imagery, is ubiquitously applied in the natural sciences. Manual methods are often prohibitively time‐consuming, especially those images consisting of small objects and/or significant spatial ...
D. Buscombe +11 more
doaj +1 more source
“Tonga”: A Novel Toolbox for Straightforward Bioimage Analysis
Frontiers in Computer Science, 2022 Techniques to acquire and analyze biological images are central to life science. However, the workflow downstream of imaging can be complex and involve several tools, leading to creation of very specialized scripts and pipelines that are difficult to ...
Alexandra Ritchie +5 more
doaj +1 more source
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers [PDF]
Computer Vision and Pattern Recognition, 2020 Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolution and learns more abstract/semantic visual concepts with larger receptive ...
Sixiao Zheng +10 more
semanticscholar +1 more source
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
LISA: Reasoning Segmentation via Large Language Model [PDF]
Computer Vision and Pattern Recognition, 2023 Although perception systems have made remarkable ad-vancements in recent years, they still rely on explicit human instruction or pre-defined categories to identify the target objects before executing visual recognition tasks. Such systems cannot actively
Xin Lai +6 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
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
semanticscholar +1 more source
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation [PDF]
Medical Image Analysis, 2023 The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface.
Junde Wu +7 more
semanticscholar +1 more source
Fully convolutional networks for semantic segmentation [PDF]
Computer Vision and Pattern Recognition, 2014 Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation.
Evan Shelhamer +2 more
semanticscholar +1 more source