scholarly journals Efficient Implementation Method Of Depth Image Segmentation In SoC System

2016 ◽  
Vol 19 (2) ◽  
pp. 122-127
Author(s):  
Jimok Sung ◽  
Bongsung Kim ◽  
Bongsoon Kang
Keyword(s):  
Image Segmentation ◽  
Efficient Implementation ◽  
Depth Image ◽  
Implementation Method

Fracture Extraction from Smooth Rock Surfaces Using Depth Image Segmentation

2021 ◽  
Author(s):  
Yudi Tang ◽  
Lei He ◽  
Huaiguang Xiao ◽  
Ruihua Wang ◽  
Wei Lu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Depth Image

scholarly journals Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4395
Author(s):  
Miloš Antić ◽  
Andrej Zdešar ◽  
Igor Škrjanc
Keyword(s):  
Image Segmentation ◽  
High Performance ◽  
Average Rate ◽  
Principal Component ◽  
Point Clouds ◽  
Data Locality ◽  
Noise Model ◽  
Depth Image ◽  
Indoor Environments ◽  
Depth Sensors

This paper presents an approach of depth image segmentation based on the Evolving Principal Component Clustering (EPCC) method, which exploits data locality in an ordered data stream. The parameters of linear prototypes, which are used to describe different clusters, are estimated in a recursive manner. The main contribution of this work is the extension and application of the EPCC to 3D space for recursive and real-time detection of flat connected surfaces based on linear segments, which are all detected in an evolving way. To obtain optimal results when processing homogeneous surfaces, we introduced two-step filtering for outlier detection within a clustering framework and considered the noise model, which allowed for the compensation of characteristic uncertainties that are introduced into the measurements of depth sensors. The developed algorithm was compared with well-known methods for point cloud segmentation. The proposed approach achieves better segmentation results over longer distances for which the signal-to-noise ratio is low, without prior filtering of the data. On the given database, an average rate higher than 90% was obtained for successfully detected flat surfaces, which indicates high performance when processing huge point clouds in a non-iterative manner.

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