Results 31 to 40 of about 2,106 (179)

Improved Fast Generation of Superpixel Algorithms with Deep Network

Jisuanji kexue yu tansuo, 2020
Superpixels are the result of over-segmentation of the image and provide an intermediate representation of the image data. It plays an important role in the research of computer vision and other fields. However, the existing superpixel algorithms are non-
SHENG Jiachuan, WANG Jiayuan, LI Yuzhi, WANG Jun
doaj   +1 more source

Superpixel Segmentation of Marine SAR Images Based on Local Fuzzy Iteration and Edge Information for Target Detection

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Most of the existing superpixel segmentation-based synthetic aperture radar (SAR) target detection algorithms cannot keep the independence of small targets under complex background, especially when the size of the targets varies greatly.
Shichao Chen, Qian Sun, Ming Liu, Mingliang Tao, Yiyang Li, Mengdao Xing   +5 more
doaj   +1 more source

Superpixel Segmentation Method Based on Foreground–Background Separation and Independent Multicycle Labeling

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Accurate superpixel segmentation of ocean remote sensing data plays a crucial role in the success of monitoring the changes on the ocean surface. Recently, so many superpixel segmentation methods have attracted much attention to ocean remote sensing ...
Qianna Cui, Haiwei Pan, Kejia Zhang
doaj   +1 more source

A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC

Applied Sciences, 2018
An image semantic segmentation algorithm using fully convolutional network (FCN) integrated with the recently proposed simple linear iterative clustering (SLIC) that is based on boundary term (BSLIC) is developed.
Wei Zhao, Haodi Zhang, Yujin Yan, Yi Fu, Hai Wang   +4 more
doaj   +1 more source

Semantic Segmentation of High-Resolution Remote Sensing Imagery via an End-to-End Graph Attention Network With Superpixel Embedding

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Semantic segmentation of high-resolution remote sensing images is crucial in ecological evaluation, natural resource surveys, etc. Compared with CNN-based and transformer-based methods, graph neural networks (GNNs) have drawn increasing attention because
Ying Tang, Xiangyun Hu, Tao Ke, Mi Zhang
doaj   +1 more source

Monitoring of Crustose Coralline Algae Using Low‐Altitude Unmanned Aerial Vehicles in Intertidal Reefs

Remote Sensing in Ecology and Conservation, EarlyView.
High‐resolution visible‐light imagery from low‐altitude unmanned aerial vehicles, combined with superpixel segmentation and a Random Forest classifier, provides an efficient and scalable framework for mapping and monitoring crustose coralline algae and reef habitats.
Po‐Chien Lin, Han‐Yang Yeh, Zhi‐Cheng Huang   +2 more
wiley   +1 more source

Quantitative Metrics for Edge Bundling of Network Visualizations

Computer Graphics Forum, EarlyView.
Abstract Edge bundling is widely used for reducing visual clutter in large 2D network and trajectory visualizations. Various edge bundling methods have been proposed, each producing qualitatively distinct outputs for the same data; however, few quantitative metrics exist for systematic evaluation. In this paper, we propose a set of quantitative metrics
M. Wallinger, J. Miller, A. Maftei, S. G. Kobourov   +3 more
wiley   +1 more source

A Hybrid Model Based on Superpixel Entropy Discrimination for PolSAR Image Classification

Remote Sensing, 2022
Superpixel segmentation is widely used in polarimetric synthetic aperture radar (PolSAR) image classification. However, the classification method using simple majority voting cannot easily handle evidence conflicts in a single superpixel.
Jili Sun, Lingdong Geng, Yize Wang
doaj   +1 more source

Explainable Deep Learning for Imaging‐Based Skin Lesion Diagnosis: A Systematic Literature Review

Concurrency and Computation: Practice and Experience, Volume 38, Issue 16, August 2026.
ABSTRACT In the latest years, the use of Deep Learning (DL) in imaging‐based skin lesion diagnosis has become increasingly prevalent. The deep models have revolutionized the computer‐aided diagnosis systems in terms of performance. However, DL models are often criticized as black boxes due to their complex and opaque internal design of numerous ...
Rym Dakhli, Walid Barhoumi
wiley   +1 more source

Improved Spatial-Spectral Superpixel Hyperspectral Unmixing

Remote Sensing, 2019
In this paper, an unsupervised unmixing approach based on superpixel representation combined with regional partitioning is presented. A reduced-size image representation is obtained using superpixel segmentation where each superpixel is represented by ...
Mohammed Q. Alkhatib, Miguel Velez-Reyes   +1 more
doaj   +1 more source