Results 21 to 30 of about 1,707,291 (329)

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, Alex Kendall, R. Cipolla   +2 more
semanticscholar   +1 more source

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, Lanfen Lin, Ruofeng Tong, Hongjie Hu, Qiaowei Zhang, Yutaro Iwamoto, Xianhua Han, Yenwei Chen, Jian Wu   +8 more
semanticscholar   +1 more source

TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images. [PDF]

Radiology: Artificial Intelligence, 2022
Purpose To present a deep learning segmentation model that can automatically and robustly segment all major anatomic structures on body CT images. Materials and Methods In this retrospective study, 1204 CT examinations (from 2012, 2016, and 2020) were ...
J. Wasserthal, H. Breit, Manfred T. Meyer, M. Pradella, Daniel Hinck, A. Sauter, T. Heye, Daniel T Boll, Joshy Cyriac, Shan Yang, M. Bach, M. Segeroth   +11 more
semanticscholar   +1 more source

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation [PDF]

IEEE Transactions on Medical Imaging, 2019
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extensive ...
Zongwei Zhou, M. R. Siddiquee, Nima Tajbakhsh, Jianming Liang   +3 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, Jonathan Long, Trevor Darrell   +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, Zhuotao Tian, Yukang Chen, Yanwei Li, Yuhui Yuan, Shu Liu, Jiaya Jia   +6 more
semanticscholar   +1 more source

Joint Learning of Intrinsic Images and Semantic Segmentation [PDF]

, 2018
Semantic segmentation of outdoor scenes is problematic when there are variations in imaging conditions. It is known that albedo (reflectance) is invariant to all kinds of illumination effects. Thus, using reflectance images for semantic segmentation task
A Garcia-Garcia, A Kundu, C Wang, DJ Butler, EH Land, G Csurka, J Shotton, JT Barron, L Ladicky, M Everingham, Q Zhao, S Shafer, V Badrinarayanan, Y Matsushita   +13 more
core   +2 more sources

Cellpose: a generalist algorithm for cellular segmentation

Nature Methods, 2020
Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets.
C. Stringer, Tim Wang, Michalis Michaelos, Marius Pachitariu   +3 more
semanticscholar   +1 more source

Optimization-based interactive segmentation interface for multiregion problems. [PDF]

, 2016
Interactive segmentation is becoming of increasing interest to the medical imaging community in that it combines the positive aspects of both manual and automated segmentation.
Baxter, JS, Chen, EC, Peters, TM, Rajchl, M   +3 more
core   +1 more source

The housing market in Serbia – Segmentation, arbitrage and overvaluation [PDF]

Economic Horizons
The paper discusses market trends and analyzes the regularities that appear on the Serbian national housing market and regional submarkets. It is assumed that, apart from the common market driving forces, the market for newly constructed houses and the ...
The housing market in Serbia – Segmentation, arbitrage and overvaluation, Ognjen Radović, Jelena Radojičić   +2 more
doaj   +1 more source