scholarly journals Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation

2019 ◽  
pp. 273-281
Author(s):  
Fabian Balsiger ◽  
Yannick Soom ◽  
Olivier Scheidegger ◽  
Mauricio Reyes
Keyword(s):  
Image Segmentation ◽  
Shape Representation ◽  
Point Clouds

scholarly journals Vegetation Removal on 3D Point Cloud Reconstruction of Cut-Slopes Using U-Net

2021 ◽  
Vol 12 (1) ◽  
pp. 395
Author(s):  
Ying Wang ◽  
Ki-Young Koo
Keyword(s):  
Image Segmentation ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Point Cloud ◽  
Cut Slope ◽  
Index Method ◽  
Vegetation Removal ◽  
3D Point Cloud Reconstruction ◽  
Cut Slopes ◽  
Projection Matrices

The 3D point cloud reconstruction from photos taken by an unmanned aerial vehicle (UAV) is a promising tool for monitoring and managing risks of cut-slopes. However, surface changes on cut-slopes are likely to be hidden by seasonal vegetation variations on the cut-slopes. This paper proposes a vegetation removal method for 3D reconstructed point clouds using (1) a 2D image segmentation deep learning model and (2) projection matrices available from photogrammetry. For a given point cloud, each 3D point of it is reprojected into the image coordinates by the projection matrices to determine if it belongs to vegetation or not using the 2D image segmentation model. The 3D points belonging to vegetation in the 2D images are deleted from the point cloud. The effort to build a 2D image segmentation model was significantly reduced by using U-Net with the dataset prepared by the colour index method complemented by manual trimming. The proposed method was applied to a cut-slope in Doam Dam in South Korea, and showed that vegetation from the two point clouds of the cut-slope at winter and summer was removed successfully. The M3C2 distance between the two vegetation-removed point clouds showed a feasibility of the proposed method as a tool to reveal actual change of cut-slopes without the effect of vegetation.

scholarly journals Recognition of Fingerspelling Sequences in Polish Sign Language Using Point Clouds Obtained from Depth Images

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1078 ◽  
Author(s):  
Dawid Warchoł ◽  
Tomasz Kapuściński ◽  
Marian Wysocki
Keyword(s):  
Point Cloud ◽  
Markov Models ◽  
Hidden Markov ◽  
Shape Representation ◽  
Depth Map ◽  
Point Clouds ◽  
Shape Functions ◽  
Depth Images ◽  
Hand Shape ◽  
Transition Models

The paper presents a method for recognizing sequences of static letters of the Polish finger alphabet using the point cloud descriptors: viewpoint feature histogram, eigenvalues-based descriptors, ensemble of shape functions, and global radius-based surface descriptor. Each sequence is understood as quick highly coarticulated motions, and the classification is performed by networks of hidden Markov models trained by transitions between postures corresponding to particular letters. Three kinds of the left-to-right Markov models of the transitions, two networks of the transition models—independent and dependent on a dictionary—as well as various combinations of point cloud descriptors are examined on a publicly available dataset of 4200 executions (registered as depth map sequences) prepared by the authors. The hand shape representation proposed in our method can also be applied for recognition of hand postures in single frames. We confirmed this using a known, challenging American finger alphabet dataset with about 60,000 depth images.

scholarly journals Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-Based Sequence to Sequence Network

2019 ◽  
Vol 33 ◽  
pp. 8778-8785 ◽  
Author(s):  
Xinhai Liu ◽  
Zhizhong Han ◽  
Yu-Shen Liu ◽  
Matthias Zwicker
Keyword(s):  
Shape Representation ◽  
Contextual Information ◽  
Point Clouds ◽  
Learning Model ◽  
Local Region ◽  
Discriminative Ability ◽  
Fine Grained ◽  
Multi Scale ◽  
3D Point Clouds ◽  
Deep Learning Model

Exploring contextual information in the local region is important for shape understanding and analysis. Existing studies often employ hand-crafted or explicit ways to encode contextual information of local regions. However, it is hard to capture fine-grained contextual information in hand-crafted or explicit manners, such as the correlation between different areas in a local region, which limits the discriminative ability of learned features. To resolve this issue, we propose a novel deep learning model for 3D point clouds, named Point2Sequence, to learn 3D shape features by capturing fine-grained contextual information in a novel implicit way. Point2Sequence employs a novel sequence learning model for point clouds to capture the correlations by aggregating multi-scale areas of each local region with attention. Specifically, Point2Sequence first learns the feature of each area scale in a local region. Then, it captures the correlation between area scales in the process of aggregating all area scales using a recurrent neural network (RNN) based encoder-decoder structure, where an attention mechanism is proposed to highlight the importance of different area scales. Experimental results show that Point2Sequence achieves state-of-the-art performance in shape classification and segmentation tasks.

Medical Image Segmentation Model Based on Local Sparse Shape Representation

2018 ◽  
Vol 55 (5) ◽  
pp. 051011
Author(s):  
姚红兵 Yao Hongbing ◽  
卞锦文 Bian Jinwen ◽  
丛嘉伟 Cong Jiawei ◽  
黄印 Huang Yin
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Shape Representation ◽  
Medical Image Segmentation ◽  
Model Based

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.

Determination of shear failure regions of rock joints based on point clouds and image segmentation

2019 ◽  
Vol 260 ◽  
pp. 105250 ◽  
Author(s):  
Yunfeng Ge ◽  
Zhiguo Xie ◽  
Huiming Tang ◽  
Hongzhi Chen ◽  
Zishan Lin ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Shear Failure ◽  
Point Clouds ◽  
Rock Joints

scholarly journals Strategies of shape representation in macaque visual area V2

2003 ◽  
Vol 20 (3) ◽  
pp. 313-328 ◽  
Author(s):  
JAY HEGDÉ ◽  
DAVID C. VAN ESSEN
Keyword(s):  
Image Segmentation ◽  
Principal Components ◽  
Visual Cues ◽  
Shape Representation ◽  
Strong Correlations ◽  
Shape Information ◽  
Object Shape ◽  
Surface Textures ◽  
Area V2 ◽  
Response Variation

Contours and surface textures provide powerful cues used in image segmentation and the analysis of object shape. To learn more about how the visual system extracts and represents these visual cues, we studied the responses of V2 neurons in awake, fixating monkeys to complex contour stimuli (angles, intersections, arcs, and circles) and texture patterns such as non-Cartesian gratings, along with conventional bars and sinusoidal gratings. Substantial proportions of V2 cells conveyed information about many contour and texture characteristics associated with our stimuli, including shape, size, orientation, and spatial frequency. However, the cells differed considerably in terms of their degree of selectivity for the various stimulus characteristics. On average, V2 cells responded better to grating stimuli but were more selective for contour stimuli. Metric multidimensional scaling and principal components analysis showed that, as a population, V2 cells show strong correlations in how they respond to different stimulus types. The first two and five principal components accounted for 69% and 85% of the overall response variation, respectively, suggesting that the response correlations simplified the population representation of shape information with relatively little loss of information. Moreover, smaller random subsets of the population carried response correlation patterns very similar to the population as a whole, indicating that the response correlations were a widespread property of V2 cells. Thus, V2 cells extract information about a number of higher order shape cues related to contours and surface textures and about similarities among many of these shape cues. This may reflect an efficient strategy of representing cues for image segmentation and object shape using finite neuronal resources.

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