Results 21 to 30 of about 3,488 (207)

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
Fengting Yang, Qian Sun, Hailin Jin, Zihan Zhou 0001   +3 more
openaire   +2 more sources

TSSP-UNet: A Two-Stage Weakly Supervised Pathological Image Segmentation With Point Annotations. [PDF]

IET Syst Biol
Deep convolutional neural networks excel at image segmentation but face challenges with complex instance training and high‐precision annotation acquisition. This study proposes TSSP‐UNet, a two‐stage weakly supervised segmentation approach: the first stage trains a segmentation network with constraint and attention mechanisms plus a feature aggregation
Wang S, Shi G, Wang Y, Li Q, Zhao Y, Cheng X.   +5 more
europepmc   +2 more sources

Sizes of Superpixels and their Effect on Interactive Segmentation [PDF]

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 2021
Semi-automated segmentation, also known as interactive image segmentation, is an algorithm that extracts a region of interest (ROI) from an image based on user input. The said algorithm will be fed the user input information repeatedly until the required region of interest is successfully segmented.
Kok Luong Goh, Giap Weng Ng, Muzaffar Hamzah, Soo-See Chai   +3 more
openaire   +1 more source

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
doaj   +1 more source

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, Jiansheng Chen, Jie Feng, Yangyang Li, Licheng Jiao, Rustam Stolkin   +5 more
doaj   +1 more source

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.
Fabio Galasso, Roberto Cipolla, Bernt Schiele   +2 more
openaire   +3 more sources

Tumor localization in tissue microarrays using rotation invariant superpixel pyramids [PDF]

, 2015
Tumor localization is an important component of histopathology image analysis; it has yet to be reliably automated for breast cancer histopathology. This paper investigates the use of superpixel classification to localize tumor regions.
Akbar, Shazia, Thompson, Alastair M., Stephen J. McKenna, Shazia Akbar, Alastair M. Thompson, Jordan, Lee, Lee Jordan, McKenna, Stephen J.; id_orcid   +7 more
core   +1 more source

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
doaj   +1 more source

Superpixel labeling for medical image segmentation [PDF]

, 2023
openNowadays, most methods for image segmentation consider images in a pixel- wise manner, which is a huge job and also time-consuming. On the other hand, superpixel labeling can make the segmentation task easier in some aspects. First, superpixels carry
REZAEI, SOMAYEH
core  

Background Subtraction Based on Random Superpixels Under Multiple Scales for Video Analytics

IEEE Access, 2018
Background subtraction is a fundamental problem of computer vision, which is usually the first step of video analytics to extract the interesting region.
Weitao Fang, Tingting Zhang, Chenqiu Zhao, Danyal Badar Soomro, Rizwan Taj, Haibo Hu   +5 more
doaj   +1 more source