Image as set of points

X Ma, Y Zhou, H Wang, C Qin, B Sun, C Liu… - arXiv preprint arXiv …, 2023 - arxiv.org
What is an image and how to extract latent features? Convolutional Networks (ConvNets)
consider an image as organized pixels in a rectangular shape and extract features via …

Semi-supervised hyperspectral image classification via spatial-regulated self-training

Y Wu, G Mu, C Qin, Q Miao, W Ma, X Zhang - Remote Sensing, 2020 - mdpi.com
Because there are many unlabeled samples in hyperspectral images and the cost of manual
labeling is high, this paper adopts semi-supervised learning method to make full use of …

Generatively inferential co-training for unsupervised domain adaptation

C Qin, L Wang, Y Zhang, Y Fu - Proceedings of the IEEE …, 2019 - openaccess.thecvf.com
Abstract Deep Neural Networks (DNNs) have greatly boosted the performance on a wide
range of computer vision and machine learning tasks. Despite such achievements, DNN is …

Selecting optimal k for K-means in image segmentation using GLCM

M Sabha, M Saffarini - Multimedia Tools and Applications, 2024 - Springer
Region growing, clustering, and thresholding are some of the segmentation techniques that
are employed on images. K-means clustering is one of the proven efficient techniques in …

3d segmentation learning from sparse annotations and hierarchical descriptors

P Yin, L Xu, J Ji, S Scherer… - IEEE Robotics and …, 2021 - ieeexplore.ieee.org
One of the main obstacles to 3D semantic segmentation is the significant amount of
endeavor required to generate expensive point-wise annotations for fully supervised …

GIDSeg: Learning 3D Segmentation from Sparse Annotations via Hierarchical Descriptors

E Yi-Ge, Y Zhu - 2020 2nd International Conference on …, 2020 - ieeexplore.ieee.org
One of the main obstacles to 3D semantic segmentation is the significant amount of
endeavor required to generate expensive point-wise annotations for fully supervised …