scholarly journals Motion Segmentation Based on Model Selection in Permutation Space for RGB Sensors

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2936 ◽  
Author(s):  
Xi Zhao ◽  
Qianqing Qin ◽  
Bin Luo
Keyword(s):  
Model Selection ◽  
Moving Objects ◽  
Motion Segmentation ◽  
Model Fitting ◽  
Selection Strategy ◽  
Camera Model ◽  
Clustering Problem ◽  
Point Trajectories ◽  
Real World Datasets ◽  
Low Dimensional

Motion segmentation is aimed at segmenting the feature point trajectories belonging to independently moving objects. Using the affine camera model, the motion segmentation problem can be viewed as a subspace clustering problem—clustering the data points drawn from a union of low-dimensional subspaces. In this paper, we propose a solution for motion segmentation that uses a multi-model fitting technique. We propose a data grouping method and a model selection strategy for obtaining more distinguishable data point permutation preferences, which significantly improves the clustering. We perform extensive testing on the Hopkins 155 dataset, and two real-world datasets. The experimental results illustrate that the proposed method can deal with incomplete trajectories and the perspective effect, comparing favorably with the current state of the art.

scholarly journals GLOBAL AND LOCAL SPARSE SUBSPACE OPTIMIZATION FOR MOTION SEGMENTATION

2015 ◽  
Vol II-3/W5 ◽  
pp. 475-482 ◽  
Author(s):  
M. Ying Yang ◽  
S. Feng ◽  
H. Ackermann ◽  
B. Rosenhahn
Keyword(s):  
Error Estimation ◽  
Moving Objects ◽  
Motion Segmentation ◽  
Computation Time ◽  
Subspace Optimization ◽  
Segmentation Methods ◽  
Feature Based ◽  
Subspace Estimation ◽  
Low Dimensional ◽  
Global And Local

In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model. Since the feature trajectories in practice are high-dimensional and contain a lot of noise, we firstly apply the sparse PCA to represent the original trajectories with a low-dimensional global subspace, which consists of the orthogonal sparse principal vectors. Subsequently, the local subspace separation will be achieved via automatically searching the sparse representation of the nearest neighbors for each projected data. In order to refine the local subspace estimation result, we propose an error estimation to encourage the projected data that span a same local subspace to be clustered together. In the end, the segmentation of different motions is achieved through the spectral clustering on an affinity matrix, which is constructed with both the error estimation and sparse neighbors optimization. We test our method extensively and compare it with state-of-the-art methods on the Hopkins 155 dataset. The results show that our method is comparable with the other motion segmentation methods, and in many cases exceed them in terms of precision and computation time.

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

scholarly journals Towards a Fully Automated 3D Reconstruction System Based on LiDAR and GNSS in Challenging Scenarios

2021 ◽  
Vol 13 (10) ◽  
pp. 1981
Author(s):  
Ruike Ren ◽  
Hao Fu ◽  
Hanzhang Xue ◽  
Zhenping Sun ◽  
Kai Ding ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
High Precision ◽  
Moving Objects ◽  
Autonomous Driving ◽  
Satellite System ◽  
Factor Graph ◽  
Mapping System ◽  
Global Navigation ◽  
Real World Datasets ◽  
Global Navigation Satellite

High-precision 3D maps play an important role in autonomous driving. The current mapping system performs well in most circumstances. However, it still encounters difficulties in the case of the Global Navigation Satellite System (GNSS) signal blockage, when surrounded by too many moving objects, or when mapping a featureless environment. In these challenging scenarios, either the global navigation approach or the local navigation approach will degenerate. With the aim of developing a degeneracy-aware robust mapping system, this paper analyzes the possible degeneration states for different navigation sources and proposes a new degeneration indicator for the point cloud registration algorithm. The proposed degeneracy indicator could then be seamlessly integrated into the factor graph-based mapping framework. Extensive experiments on real-world datasets demonstrate that the proposed 3D reconstruction system based on GNSS and Light Detection and Ranging (LiDAR) sensors can map challenging scenarios with high precision.

scholarly journals Sensor Fusion-Based Approach to Eliminating Moving Objects for SLAM in Dynamic Environments

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 230
Author(s):  
Xiangwei Dang ◽  
Zheng Rong ◽  
Xingdong Liang
Keyword(s):  
State Estimation ◽  
Autonomous Navigation ◽  
Moving Objects ◽  
Physical Simulation ◽  
Point Clouds ◽  
Dynamic Environments ◽  
Accurate Localization ◽  
Novel Approach ◽  
Localization And Mapping ◽  
Real World Datasets

Accurate localization and reliable mapping is essential for autonomous navigation of robots. As one of the core technologies for autonomous navigation, Simultaneous Localization and Mapping (SLAM) has attracted widespread attention in recent decades. Based on vision or LiDAR sensors, great efforts have been devoted to achieving real-time SLAM that can support a robot’s state estimation. However, most of the mature SLAM methods generally work under the assumption that the environment is static, while in dynamic environments they will yield degenerate performance or even fail. In this paper, first we quantitatively evaluate the performance of the state-of-the-art LiDAR-based SLAMs taking into account different pattens of moving objects in the environment. Through semi-physical simulation, we observed that the shape, size, and distribution of moving objects all can impact the performance of SLAM significantly, and obtained instructive investigation results by quantitative comparison between LOAM and LeGO-LOAM. Secondly, based on the above investigation, a novel approach named EMO to eliminating the moving objects for SLAM fusing LiDAR and mmW-radar is proposed, towards improving the accuracy and robustness of state estimation. The method fully uses the advantages of different characteristics of two sensors to realize the fusion of sensor information with two different resolutions. The moving objects can be efficiently detected based on Doppler effect by radar, accurately segmented and localized by LiDAR, then filtered out from the point clouds through data association and accurate synchronized in time and space. Finally, the point clouds representing the static environment are used as the input of SLAM. The proposed approach is evaluated through experiments using both semi-physical simulation and real-world datasets. The results demonstrate the effectiveness of the method at improving SLAM performance in accuracy (decrease by 30% at least in absolute position error) and robustness in dynamic environments.

A dynamic model selection strategy for support vector machine classifiers

2012 ◽  
Vol 12 (8) ◽  
pp. 2550-2565 ◽  
Author(s):  
Marcelo N. Kapp ◽  
Robert Sabourin ◽  
Patrick Maupin
Keyword(s):  
Support Vector Machine ◽  
Model Selection ◽  
Dynamic Model ◽  
Selection Strategy ◽  
Support Vector

scholarly journals Network Embedding via a Bi-Mode and Deep Neural Network Model

2017 ◽  
Author(s):  
Yang Fang ◽  
Xiang Zhao ◽  
Zhen Tan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Semantic Information ◽  
Dimensional Space ◽  
Relation Extraction ◽  
Network Embedding ◽  
Structure Information ◽  
Second Mode ◽  
Real World Datasets ◽  
Low Dimensional

Network Embedding (NE) is an important method to learn the representations of network via a low-dimensional space. Conventional NE models focus on capturing the structure information and semantic information of vertices while neglecting such information for edges. In this work, we propose a novel NE model named BimoNet to capture both the structure and semantic information of edges. BimoNet is composed of two parts, i.e., the bi-mode embedding part and the deep neural network part. For bi-mode embedding part, the first mode named add-mode is used to express the entity-shared features of edges and the second mode named subtract-mode is employed to represent the entity-specific features of edges. These features actually reflect the semantic information. For deep neural network part, we firstly regard the edges in a network as nodes, and the vertices as links, which will not change the overall structure of the whole network. Then we take the nodes' adjacent matrix as the input of the deep neural network as it can obtain similar representations for nodes with similar structure. Afterwards, by jointly optimizing the objective function of these two parts, BimoNet could preserve both the semantic and structure information of edges. In experiments, we evaluate BimoNet on three real-world datasets and task of relation extraction, and BimoNet is demonstrated to outperform state-of-the-art baseline models consistently and significantly.

A Decision Cluster Classification Model with Model Selection Strategy

2012 ◽  
Vol 4 (22) ◽  
pp. 1-9
Author(s):  
Zhaocai Sun ◽  
Yunming Ye ◽  
Zhexue Huang ◽  
Wu Chen ◽  
Chunshan Li
Keyword(s):  
Model Selection ◽  
Classification Model ◽  
Selection Strategy

scholarly journals Exponential Family Graph Embeddings

2020 ◽  
Vol 34 (04) ◽  
pp. 3357-3364
Author(s):  
Abdulkadir Celikkanat ◽  
Fragkiskos D. Malliaros
Keyword(s):  
Random Walk ◽  
Exponential Family ◽  
Representation Learning ◽  
Learning Problems ◽  
Interaction Patterns ◽  
Network Representation ◽  
Learning Tasks ◽  
Learning Techniques ◽  
Real World Datasets ◽  
Low Dimensional

Representing networks in a low dimensional latent space is a crucial task with many interesting applications in graph learning problems, such as link prediction and node classification. A widely applied network representation learning paradigm is based on the combination of random walks for sampling context nodes and the traditional Skip-Gram model to capture center-context node relationships. In this paper, we emphasize on exponential family distributions to capture rich interaction patterns between nodes in random walk sequences. We introduce the generic exponential family graph embedding model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions. We study three particular instances of this model, analyzing their properties and showing their relationship to existing unsupervised learning models. Our experimental evaluation on real-world datasets demonstrates that the proposed techniques outperform well-known baseline methods in two downstream machine learning tasks.

scholarly journals Relation Structure-Aware Heterogeneous Information Network Embedding

2019 ◽  
Vol 33 ◽  
pp. 4456-4463 ◽  
Author(s):  
Yuanfu Lu ◽  
Chuan Shi ◽  
Linmei Hu ◽  
Zhiyuan Liu
Keyword(s):  
Real World ◽  
Dimensional Space ◽  
Structural Characteristics ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Embedding Methods

Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough mathematical analysis, we present two structure-related measures which can consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive characteristics of relations, in our RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures and semantics of the networks. At last, we combine and optimize these models in a unified and elegant manner. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification.

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