scholarly journals DeepCCFV: Camera Constraint-Free Multi-View Convolutional Neural Network for 3D Object Retrieval

2019 ◽  
Vol 33 ◽  
pp. 8505-8512 ◽  
Author(s):  
Zhengyue Huang ◽  
Zhehui Zhao ◽  
Hengguang Zhou ◽  
Xibin Zhao ◽  
Yue Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Rapid Development ◽  
Basic Feature ◽  
3D Object Retrieval ◽  
Object Retrieval ◽  
3D Vision ◽  
3D Object ◽  
Practical Applications ◽  
3D Objects

3D object retrieval has a compelling demand in the field of computer vision with the rapid development of 3D vision technology and increasing applications of 3D objects. 3D objects can be described in different ways such as voxel, point cloud, and multi-view. Among them, multi-view based approaches proposed in recent years show promising results. Most of them require a fixed predefined camera position setting which provides a complete and uniform sampling of views for objects in the training stage. However, this causes heavy over-fitting problems which make the models failed to generalize well in free camera setting applications, particularly when insufficient views are provided. Experiments show the performance drastically drops when the number of views reduces, hindering these methods from practical applications. In this paper, we investigate the over-fitting issue and remove the constraint of the camera setting. First, two basic feature augmentation strategies Dropout and Dropview are introduced to solve the over-fitting issue, and a more precise and more efficient method named DropMax is proposed after analyzing the drawback of the basic ones. Then, by reducing the over-fitting issue, a camera constraint-free multi-view convolutional neural network named DeepCCFV is constructed. Extensive experiments on both single-modal and cross-modal cases demonstrate the effectiveness of the proposed method in free camera settings comparing with existing state-of-theart 3D object retrieval methods.

Semantic Enabled 3D Object Retrieval

2010 ◽  
Vol 159 ◽  
pp. 128-131
Author(s):  
Jiang Zhou ◽  
Xin Yu Ma
Keyword(s):  
Domain Knowledge ◽  
Regions Of Interest ◽  
3D Object Retrieval ◽  
3D Shape Retrieval ◽  
Object Retrieval ◽  
3D Object ◽  
3D Objects ◽  
Retrieval Systems ◽  
3D Shapes ◽  
Spatial Semantics

In the case of traditional 3D shape retrieval systems, the objects retrieved are based mainly on the computation of low-level features that are used to detect so-called regions-of-interest. This paper focuses on obtaining the retrieved objects in a machine understandable and intelligent manner. We explore the different kinds of semantic descriptions for retrieval of 3D shapes. Based on ontology technology, we decompose a 3D objects into meaningful parts semi-automatically. Each part can be regarded as a 3D object, and further be semantically annotated according to ontology vocabulary from the Chinese cultural relics. Three kinds of semantic models such as description semantics of domain knowledge, spatial semantics and scenario semantics, are presented for describing semantic annotations from different viewpoints. These annotated semantics can accurately grasp complete semantic descriptions of 3D shapes.

Segmentation and Semantic Annotation of 3D Objects

2010 ◽  
Vol 159 ◽  
pp. 124-127
Author(s):  
Jiang Zhou ◽  
Xin Yu Ma
Keyword(s):  
Object Segmentation ◽  
Semantic Annotation ◽  
3D Object Retrieval ◽  
Object Retrieval ◽  
3D Object ◽  
3D Objects ◽  
The Future ◽  
3D Shapes ◽  
Cultural Relics

Recently, semantic based 3D object retrieval has been paid more attention to because it focuses on obtaining the retrieved objects in a machine understandable and intelligent manner. In this paper, we propose an approach for semantic based annotation of 3D shapes. To enable semantic based annotation, the method for object segmentation decomposes 3D objects into meaningful parts semi-automatically. Furthermore, each part can be regarded as a 3D object, and further be semantically annotated according to ontology vocabulary from the Chinese cultural relics. Such a segmentation and annotation provide the premise for the future retrieval of 3D shapes.

scholarly journals Comparison of cephalometric measurements between conventional and automatic cephalometric analysis using convolutional neural network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Sangmin Jeon ◽  
Kyungmin Clara Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Rapid Development ◽  
Cephalometric Analysis ◽  
Anatomical Landmarks ◽  
Limits Of Agreement ◽  
Mandibular Incisor ◽  
Lateral Cephalograms ◽  
Dental Measurements ◽  
Cephalometric Measurements

Abstract Objective The rapid development of artificial intelligence technologies for medical imaging has recently enabled automatic identification of anatomical landmarks on radiographs. The purpose of this study was to compare the results of an automatic cephalometric analysis using convolutional neural network with those obtained by a conventional cephalometric approach. Material and methods Cephalometric measurements of lateral cephalograms from 35 patients were obtained using an automatic program and a conventional program. Fifteen skeletal cephalometric measurements, nine dental cephalometric measurements, and two soft tissue cephalometric measurements obtained by the two methods were compared using paired t test and Bland-Altman plots. Results A comparison between the measurements from the automatic and conventional cephalometric analyses in terms of the paired t test confirmed that the saddle angle, linear measurements of maxillary incisor to NA line, and mandibular incisor to NB line showed statistically significant differences. All measurements were within the limits of agreement based on the Bland-Altman plots. The widths of limits of agreement were wider in dental measurements than those in the skeletal measurements. Conclusions Automatic cephalometric analyses based on convolutional neural network may offer clinically acceptable diagnostic performance. Careful consideration and additional manual adjustment are needed for dental measurements regarding tooth structures for higher accuracy and better performance.

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

3D object retrieval system based on grid D2

2005 ◽  
Vol 41 (4) ◽  
pp. 179 ◽  
Author(s):  
J.-L. Shih ◽  
C.-H. Lee ◽  
J.T. Wang
Keyword(s):  
Retrieval System ◽  
3D Object Retrieval ◽  
Object Retrieval ◽  
3D Object
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