Results 21 to 30 of about 9,095 (206)

SUPERPIXEL CLASSIFICATION OF HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGE BASED ON MULTI-SCALE CNN AND SCALE PARAMETER ESTIMATION [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019
In recent years, considerable attention has been paid to integrate convolutional neural network (CNN) with land cover classification of high spatial resolution remote sensing image.
Y. Chen, D. Ming
doaj   +1 more source

SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection [PDF]

European Conference on Computer Vision, 2022
RGB-D salient object detection (SOD) has been in the spotlight recently because it is an important preprocessing operation for various vision tasks. However, despite advances in deep learning-based methods, RGB-D SOD is still challenging due to the large
Minhyeok Lee, Chaewon Park, Suhwan Cho, Sangyoun Lee   +3 more
semanticscholar   +1 more source

Adaptive Superpixel Segmentation of Marine SAR Images by Aggregating Fisher Vectors

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Superpixel segmentation is an important technique for image analysis. In this article, we develop a new superpixel segmentation approach and investigate its application on ship target detection in marine synthetic aperture radar (SAR) images.
Xueqian Wang, You He, Gang Li, Antonio Plaza   +3 more
doaj   +1 more source

Artificial Intelligence-Based Approaches for Brain Tumor Segmentation in MRI: A Review. [PDF]

NMR Biomed
Manually segmenting brain tumors in magnetic resonance imaging is a time‐consuming task that requires years of professional experience and clinical expertise. We proposed a study, which contains a comprehensive review of the brain tumor segmentation techniques. It selects the effective approaches to better understand the AI applications for brain tumor
Bibi K, Nawaz M, Khan S, Daud M, Masood A, Abdelgawad MA, Abbasi SMT, Rizwan, Khan A, Yuan W.   +9 more
europepmc   +2 more sources

Rooted Spanning Superpixels [PDF]

International Journal of Computer Vision, 2020
AbstractThis paper proposes a new approach for superpixel segmentation. It is formulated as finding a rooted spanning forest of a graph with respect to some roots and a path-cost function. The underlying graph represents an image, the roots serve as seeds for segmentation, each pixel is connected to one seed via a path, the path-cost function measures ...
openaire   +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

Superpixel Transformers for Efficient Semantic Segmentation [PDF]

IEEE/RJS International Conference on Intelligent RObots and Systems, 2023
Semantic segmentation, which aims to classify every pixel in an image, is a key task in machine perception, with many applications across robotics and autonomous driving.
Alex Zhu, Jieru Mei, Siyuan Qiao, Hang Yan, Yukun Zhu, Liang-Chieh Chen, Henrik Kretzschmar   +6 more
semanticscholar   +1 more source

Superpixel Embedding Network

IEEE Transactions on Image Processing, 2020
Superpixel segmentation is a fundamental computer vision technique that finds application in a multitude of high level computer vision tasks. Most state-of-the-art superpixel segmentation methods are unsupervised in nature and thus cannot fully utilize frequently occurring texture patterns or incorporate multiscale context.
Utkarsh Gaur, B. S. Manjunath
openaire   +5 more sources

Content-Based Superpixel Matching Using Spatially Constrained Student’s-t Mixture Model and Scale-Invariant Key-Superpixels

IEEE Access, 2020
This paper addresses an image matching methodology designed for correspondence problem in computer vision. Firstly, a novel superpixel segmentation model driven by spatially constrained Student's-t mixture model (SMM) is proposed.
Pengyu Wang, Hongqing Zhu, Xiaofeng Ling
doaj   +1 more source

Semantic-Aware Superpixel for Weakly Supervised Semantic Segmentation

AAAI Conference on Artificial Intelligence, 2023
Weakly-supervised semantic segmentation aims to train a semantic segmentation network using weak labels. Among weak labels, image-level label has been the most popular choice due to its simplicity.
Sangtae Kim, Daeyoung Park, B. Shim
semanticscholar   +1 more source