Classification of HEp-2 Cell Images Using Compact Multi-Scale Texture Information and Margin Distribution Based Bagging

2014 ◽  
pp. 299-308
Author(s):  
Gerald Schaefer ◽  
Niraj P. Doshi ◽  
Qinghua Hu ◽  
Aboul Ella Hassanien
Keyword(s):  
Multi Scale ◽  
Texture Information ◽  
Margin Distribution

scholarly journals An Inverse Node Graph-Based Method for the Urban Scene Segmentation of 3D Point Clouds

2021 ◽  
Vol 13 (15) ◽  
pp. 3021
Author(s):  
Bufan Zhao ◽  
Xianghong Hua ◽  
Kegen Yu ◽  
Xiaoxing He ◽  
Weixing Xue ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Point Clouds ◽  
Intelligent Vehicles ◽  
Critical Data ◽  
Multi Scale ◽  
3D Point Clouds ◽  
Cluster Optimization ◽  
Urban Scene ◽  
Processing Steps

Urban object segmentation and classification tasks are critical data processing steps in scene understanding, intelligent vehicles and 3D high-precision maps. Semantic segmentation of 3D point clouds is the foundational step in object recognition. To identify the intersecting objects and improve the accuracy of classification, this paper proposes a segment-based classification method for 3D point clouds. This method firstly divides points into multi-scale supervoxels and groups them by proposed inverse node graph (IN-Graph) construction, which does not need to define prior information about the node, it divides supervoxels by judging the connection state of edges between them. This method reaches minimum global energy by graph cutting, obtains the structural segments as completely as possible, and retains boundaries at the same time. Then, the random forest classifier is utilized for supervised classification. To deal with the mislabeling of scattered fragments, higher-order CRF with small-label cluster optimization is proposed to refine the classification results. Experiments were carried out on mobile laser scan (MLS) point dataset and terrestrial laser scan (TLS) points dataset, and the results show that overall accuracies of 97.57% and 96.39% were obtained in the two datasets. The boundaries of objects were retained well, and the method achieved a good result in the classification of cars and motorcycles. More experimental analyses have verified the advantages of the proposed method and proved the practicability and versatility of the method.

Classification of 3D terracotta warriors fragments based on geospatial and texture information

2021 ◽  
Author(s):  
Kang Yang ◽  
Xin Cao ◽  
Guohua Geng ◽  
Kang Li ◽  
Mingquan Zhou
Keyword(s):  
Texture Information

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

Multi-scale superpixel spectral–spatial classification of hyperspectral images

2016 ◽  
Vol 37 (20) ◽  
pp. 4905-4922 ◽  
Author(s):  
Shanshan Li ◽  
Li Ni ◽  
Xiuping Jia ◽  
Lianru Gao ◽  
Bing Zhang ◽  
...  
Keyword(s):  
Hyperspectral Images ◽  
Spatial Classification ◽  
Multi Scale

Terrain Classification of LiDAR Point Cloud Based on Multi-Scale Features and PointNet

2019 ◽  
Vol 56 (5) ◽  
pp. 052804
Author(s):  
赵中阳 Zhao Zhongyang ◽  
程英蕾 Cheng Yinglei ◽  
释小松 Shi Xiaosong ◽  
秦先祥 Qin Xianxiang ◽  
李鑫 Li Xin
Keyword(s):  
Point Cloud ◽  
Terrain Classification ◽  
Multi Scale
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