scholarly journals An Improved 3D Shape Recognition Method Based on Panoramic View

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qiang Zheng ◽  
Jian Sun ◽  
Le Zhang ◽  
Wei Chen ◽  
Huanhuan Fan
Keyword(s):  
Computer Vision ◽  
Shape Recognition ◽  
Three Dimensional ◽  
Panoramic View ◽  
Shape Representations ◽  
Practical Applications ◽  
3D Shape ◽  
Depth Sensors ◽  
Novel Method ◽  
3D Shapes

Recognition of three-dimensional (3D) shape is a remarkable subject in computer vision systems, because of the lack of excellent shape representations. With the development of 2.5D depth sensors, shape recognition is becoming more important in practical applications. Many methods have been proposed to preprocess 3D shapes, in order to get available input data. A common approach employs convolutional neural networks (CNNs), which have become a powerful tool to solve many problems in the field of computer vision. DeepPano, a variant of CNN, converts each 3D shape into a panoramic view and shows excellent performance. It is worth paying attention to the fact that both serious information loss and redundancy exist in the processing of DeepPano, which limits further improvement of its performance. In this work, we propose a more effective method to preprocess 3D shapes also based on a panoramic view, similar to DeepPano. We introduce a novel method to expand the training set and optimize the architecture of the network. The experimental results show that our approach outperforms DeepPano and can deal with more complex 3D shape recognition problems with a higher diversity of target orientation.

scholarly journals MeshNet: Mesh Neural Network for 3D Shape Representation

2019 ◽  
Vol 33 ◽  
pp. 8279-8286 ◽  
Author(s):  
Yutong Feng ◽  
Yifan Feng ◽  
Haoxuan You ◽  
Xibin Zhao ◽  
Yue Gao
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Point Cloud ◽  
State Of The Art ◽  
Shape Representation ◽  
Shape Classification ◽  
Retrieval Performance ◽  
3D Shape ◽  
General Architecture ◽  
3D Shapes

Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation.

scholarly journals PVRNet: Point-View Relation Neural Network for 3D Shape Recognition

2019 ◽  
Vol 33 ◽  
pp. 9119-9126 ◽  
Author(s):  
Haoxuan You ◽  
Yifan Feng ◽  
Xibin Zhao ◽  
Changqing Zou ◽  
Rongrong Ji ◽  
...  
Keyword(s):  
Point Cloud ◽  
Shape Recognition ◽  
Three Dimensional ◽  
Feature Representation ◽  
Research Attention ◽  
Single View ◽  
3D Shape ◽  
3D Data ◽  
Effective Network ◽  
Fusion Feature

Three-dimensional (3D) shape recognition has drawn much research attention in the field of computer vision. The advances of deep learning encourage various deep models for 3D feature representation. For point cloud and multi-view data, two popular 3D data modalities, different models are proposed with remarkable performance. However the relation between point cloud and views has been rarely investigated. In this paper, we introduce Point-View Relation Network (PVRNet), an effective network designed to well fuse the view features and the point cloud feature with a proposed relation score module. More specifically, based on the relation score module, the point-single-view fusion feature is first extracted by fusing the point cloud feature and each single view feature with point-singe-view relation, then the pointmulti- view fusion feature is extracted by fusing the point cloud feature and the features of different number of views with point-multi-view relation. Finally, the point-single-view fusion feature and point-multi-view fusion feature are further combined together to achieve a unified representation for a 3D shape. Our proposed PVRNet has been evaluated on ModelNet40 dataset for 3D shape classification and retrieval. Experimental results indicate our model can achieve significant performance improvement compared with the state-of-the-art models.

scholarly journals Plant Leaf Position Estimation with Computer Vision

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5933
Author(s):  
James Beadle ◽  
C. James Taylor ◽  
Kirsti Ashworth ◽  
David Cheneler
Keyword(s):  
Computer Vision ◽  
Three Dimensional ◽  
Digital Camera ◽  
Position Estimation ◽  
Infrared Light ◽  
Depth Sensors ◽  
Plant Canopies ◽  
Sensor Platforms ◽  
Trained Neural Network ◽  
Infrared Methods

Autonomous analysis of plants, such as for phenotyping and health monitoring etc., often requires the reliable identification and localization of single leaves, a task complicated by their complex and variable shape. Robotic sensor platforms commonly use depth sensors that rely on either infrared light or ultrasound, in addition to imaging. However, infrared methods have the disadvantage of being affected by the presence of ambient light, and ultrasound methods generally have too wide a field of view, making them ineffective for measuring complex and intricate structures. Alternatives may include stereoscopic or structured light scanners, but these can be costly and overly complex to implement. This article presents a fully computer-vision based solution capable of estimating the three-dimensional location of all leaves of a subject plant with the use of a single digital camera autonomously positioned by a three-axis linear robot. A custom trained neural network was used to classify leaves captured in multiple images taken of a subject plant. Parallax calculations were applied to predict leaf depth, and from this, the three-dimensional position. This article demonstrates proof of concept of the method, and initial tests with positioned leaves suggest an expected error of 20 mm. Future modifications are identified to further improve accuracy and utility across different plant canopies.

PGNet: Progressive Feature Guide Learning Network for Three-dimensional Shape Recognition

2021 ◽  
Vol 17 (3) ◽  
pp. 1-17
Author(s):  
Jie Nie ◽  
Zhi-Qiang Wei ◽  
Weizhi Nie ◽  
An-An Liu
Keyword(s):  
Deep Learning ◽  
Information Fusion ◽  
Visual Information ◽  
Feature Fusion ◽  
Shape Representation ◽  
Shape Recognition ◽  
Three Dimensional ◽  
Learning Network ◽  
3D Shape ◽  
Fusion Scheme

Three-dimensional (3D) shape recognition is a popular topic and has potential application value in the field of computer vision. With the recent proliferation of deep learning, various deep learning models have achieved state-of-the-art performance. Among them, multiview-based 3D shape representation has received increased attention in recent years, and related approaches have shown significant improvement in 3D shape recognition. However, these methods focus on feature learning based on the design of the network and ignore the correlation among views. In this article, we propose a novel progressive feature guide learning network (PGNet) that focuses on the correlation among multiple views and integrates multiple modalities for 3D shape recognition. In particular, we propose two information fusion schemes from visual and feature aspects. The visual fusion scheme focuses on the view level and employs the soft-attention model to define the weights of views for visual information fusion. The feature fusion scheme focuses on the feature dimension information and employs the quantified feature as the mask to further optimize the feature. These two schemes jointly construct a PGNet for 3D shape representation. The classic ModelNet40 and ShapeNetCore55 datasets are applied to demonstrate the performance of our approach. The corresponding experiment also demonstrates the superiority of our approach.

Practical electron tomography

1995 ◽  
Vol 53 ◽  
pp. 742-743
Author(s):  
C.L. Woodcock
Keyword(s):  
Electron Tomography ◽  
Tomographic Reconstruction ◽  
Three Dimensional ◽  
3D Shape ◽  
Computational Resources ◽  
Shape Of Particles

Despite the potential of the technique, electron tomography has yet to be widely used by biologists. This is in part related to the rather daunting list of equipment and expertise that are required. Thanks to continuing advances in theory and instrumentation, tomography is now more feasible for the non-specialist. One barrier that has essentially disappeared is the expense of computational resources. In view of this progress, it is time to give more attention to practical issues that need to be considered when embarking on a tomographic project. The following recommendations and comments are derived from experience gained during two long-term collaborative projects.Tomographic reconstruction results in a three dimensional description of an individual EM specimen, most commonly a section, and is therefore applicable to problems in which ultrastructural details within the thickness of the specimen are obscured in single micrographs. Information that can be recovered using tomography includes the 3D shape of particles, and the arrangement and dispostion of overlapping fibrous and membranous structures.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

scholarly journals Homogeneous 2D and 3D alignment of cardiomyocyte in dilated cardiomyopathy revealed by intravital heart imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kiyoshi Masuyama ◽  
Tomoaki Higo ◽  
Jong-Kook Lee ◽  
Ryohei Matsuura ◽  
Ian Jones ◽  
...  
Keyword(s):  
Heart Failure ◽  
Dilated Cardiomyopathy ◽  
Cardiac Dysfunction ◽  
Three Dimensional ◽  
Single Layer ◽  
Specific Pattern ◽  
Two Dimensional ◽  
Heart Imaging ◽  
Novel Method ◽  
2D And 3D

AbstractIn contrast to hypertrophic cardiomyopathy, there has been reported no specific pattern of cardiomyocyte array in dilated cardiomyopathy (DCM), partially because lack of alignment assessment in a three-dimensional (3D) manner. Here we have established a novel method to evaluate cardiomyocyte alignment in 3D using intravital heart imaging and demonstrated homogeneous alignment in DCM mice. Whilst cardiomyocytes of control mice changed their alignment by every layer in 3D and position twistedly even in a single layer, termed myocyte twist, cardiomyocytes of DCM mice aligned homogeneously both in two-dimensional (2D) and in 3D and lost myocyte twist. Manipulation of cultured cardiomyocyte toward homogeneously aligned increased their contractility, suggesting that homogeneous alignment in DCM mice is due to a sort of alignment remodelling as a way to compensate cardiac dysfunction. Our findings provide the first intravital evidence of cardiomyocyte alignment and will bring new insights into understanding the mechanism of heart failure.

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