Content-based 3D image retrieval using point cloud library a novel approach for the retrieval of 3D images

2017 ◽  
Author(s):  
G. Aiswarya ◽  
Nanditha Valsaraj ◽  
M. Vaishak ◽  
Thanthu U. Nair ◽  
V. Karthik ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Point Cloud ◽  
3D Image ◽  
3D Images ◽  
Novel Approach

Full-parallax 3D display using time-multiplexing projection technology

2020 ◽  
Vol 2020 (2) ◽  
pp. 100-1-100-6
Author(s):  
Takuya Omura ◽  
Hayato Watanabe ◽  
Naoto Okaichi ◽  
Hisayuki Sasaki ◽  
Masahiro Kawakita
Keyword(s):  
Incident Light ◽  
Polarization State ◽  
3D Image ◽  
3D Display ◽  
Time Division Multiplexing ◽  
3D Images ◽  
Polarization Gratings ◽  
Light Rays ◽  
Time Division

We enhanced the resolution characteristics of a threedimensional (3D) image using time-division multiplexing methods in a full-parallax multi-view 3D display. A time-division light-ray shifting (TDLS) method is proposed that uses two polarization gratings (PGs). As PG changes the diffraction direction of light rays according to the polarization state of the incident light, this method can shift light rays approximately 7 mm in a diagonal direction by switching the polarization state of incident light and adjusting the distance between the PGs. We verified the effect on the characteristics of 3D images based on the extent of the shift. As a result, the resolution of a 3D image with depth is improved by shifting half a pitch of a multi-view image using the TDLS method, and the resolution of the image displayed near the screen is improved by shifting half a pixel of each viewpoint image with a wobbling method. These methods can easily enhance 3D characteristics with a small number of projectors.

Spatial feature-based prioritization for transmission of point cloud data in 3D-image sensor networks

2021 ◽  
pp. 1-1
Author(s):  
Masamichi Oka ◽  
Ryoichi Shinkuma ◽  
Takehiro Sato ◽  
Eiji Oki ◽  
Takanori Iwai ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Point Cloud ◽  
Image Sensor ◽  
Point Cloud Data ◽  
3D Image ◽  
Spatial Feature ◽  
Cloud Data ◽  
Feature Based

scholarly journals Performance of Polar-Coded 3D Image Transmission over Fading Channel

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jianjun Hao ◽  
Luyao Liu ◽  
Wei Chen
Keyword(s):  
White Noise ◽  
Data Transmission ◽  
Fading Channel ◽  
Point Cloud ◽  
Gaussian White Noise ◽  
Image Data ◽  
Image Transmission ◽  
Transmission System ◽  
3D Point Cloud ◽  
3D Image

Any signal transmitted over an air-to-ground channel is corrupted by fading, noise, and interference. In this paper, a Polar-coded 3D point cloud image transmission system with fading channel is modeled, and also the simulation is performed to verify its performance in terms of 3D point cloud image data transmission over Rician channel with Gaussian white noise and overlap of Gaussian white noise + periodic pulse jamming separately. The comparison of Polar-coded scheme with RS-coded scheme in the same scenario indicates that Polar-coded system gives far better performance against AWGN noise and fading than the RS-coded system does in the case of short block length. But RS-coded scheme shows better performance on antipulse jamming than that of Polar-coded scheme, while there is no interleaving between codewords.

scholarly journals A ROBUST ESTIMATION TECHNIQUE FOR 3D POINT CLOUD REGISTRATION

2016 ◽  
Vol 35 (1) ◽  
pp. 15 ◽  
Author(s):  
Dhanya S Pankaj ◽  
Rama Rao Nidamanuri
Keyword(s):  
Robust Estimation ◽  
Point Cloud ◽  
Feature Matching ◽  
Computational Time ◽  
Model Estimation ◽  
3D Registration ◽  
3D Images ◽  
Data Points ◽  
Pairwise Registration ◽  
3D Scans

The 3D modeling pipeline involves registration of partially overlapping 3D scans of an object. The automatic pairwise coarse alignment of partially overlapping 3D images is generally performed using 3D feature matching. The transformation estimation from matched features generally requires robust estimation due to the presence of outliers. RANSAC is a method of choice in problems where model estimation is to be done from data samples containing outliers. The number of RANSAC iterations depends on the number of data points and inliers to the model. Convergence of RANSAC can be very slow in the case of large number of outliers. This paper presents a novel algorithm for the 3D registration task which provides more accurate results in lesser computational time compared to RANSAC. The proposed algorithm is also compared against the existing modifications of RANSAC for 3D pairwise registration. The results indicate that the proposed algorithm tends to obtain the best 3D transformation matrix in lesser time compared to the other algorithms.

3d Confocal Microscopy and Image Analysis for Measurement of Genetic Instability

1999 ◽  
Vol 5 (S2) ◽  
pp. 1022-1023
Author(s):  
C. Ortiz de Solorzano ◽  
K. Chin ◽  
D. Knowles ◽  
A. Jones ◽  
E. Garcia ◽  
...  
Keyword(s):  
Image Analysis ◽  
Confocal Microscopy ◽  
Genetic Instability ◽  
Copy Number ◽  
3D Image ◽  
3D Images ◽  
Tissue Sections ◽  
Total Dna ◽  
Natural Tissue

Solid tumors frequently contain cells that are heterogeneous in the copy number of DNA loci. This fact implies the existence of genetic instability, which may be associated with disease aggressiveness. Accurate measurement of this phenomenon requires analysis of intact nuclei within their natural tissue context. We perform these measurements by preparing >30μm thick tissue sections, labeling them with fluorescent labels for total DNA and for specific DNA loci using fluorescence in situ hybridization (FISH) which retain the transparency of the tissue and acquiring 3D images of the tissue using confocal microscopy (figure 1). In this study, we combined automated 3D image analysis (IA) algorithms for segmenting individual nuclei based on the total DNA stain1 and for segmenting the punctuate FISH signals of DNA loci. This enables us to efficiently enumerate the copy number of specific DNA loci in individual cells and as a function of the cell's location in the tissue.

Trademark Image Retrieval

2011 ◽  
pp. 82-114
Author(s):  
Wing-Yin Chau ◽  
Chia-Hung Wei ◽  
Yue Li
Keyword(s):  
Image Retrieval ◽  
Scale Space ◽  
Zernike Moments ◽  
Registration System ◽  
Novel Approach ◽  
Centroid Distance ◽  
Curvature Scale Space ◽  
The World ◽  
Trademark Registration ◽  
Trademark Images

With the rapid increase in the amount of registered trademarks around the world, trademark image retrieval has been developed to deal with a vast amount of trademark images in a trademark registration system. Many different approaches have been developed throughout these years in an attempt to develop an effective TIR system. Some conventional approaches used in content-based image retrieval, such as moment invariants, Zernike moments, Fourier descriptors and curvature scale space descriptors, have also been widely used in TIR. These approaches, however, contain some major deficiencies when addressing the TIR problem. Therefore, this chapter proposes a novel approach in order to overcome the major deficiencies of the conventional approaches. The proposed approach combines the Zernike moments descriptors with the centroid distance representation and the curvature representation. The experimental results show that the proposed approach outperforms the conventional approaches in several circumstances. Details regarding to the proposed approach as well as the conventional approaches are presented in this chapter.

scholarly journals AE-GAN-Net: Learning Invariant Feature Descriptor to Match Ground Camera Images and a Large-Scale 3D Image-Based Point Cloud for Outdoor Augmented Reality

2019 ◽  
Vol 11 (19) ◽  
pp. 2243 ◽  
Author(s):  
Weiquan Liu ◽  
Cheng Wang ◽  
Xuesheng Bian ◽  
Shuting Chen ◽  
Wei Li ◽  
...  
Keyword(s):  
Image Matching ◽  
Point Cloud ◽  
Spatial Relationship ◽  
3D Image ◽  
Image Patch ◽  
3D Space ◽  
Cross Domain ◽  
Feature Descriptors ◽  
Outdoor Environments ◽  
2D And 3D

Establishing the spatial relationship between 2D images captured by real cameras and 3D models of the environment (2D and 3D space) is one way to achieve the virtual–real registration for Augmented Reality (AR) in outdoor environments. In this paper, we propose to match the 2D images captured by real cameras and the rendered images from the 3D image-based point cloud to indirectly establish the spatial relationship between 2D and 3D space. We call these two kinds of images as cross-domain images, because their imaging mechanisms and nature are quite different. However, unlike real camera images, the rendered images from the 3D image-based point cloud are inevitably contaminated with image distortion, blurred resolution, and obstructions, which makes image matching with the handcrafted descriptors or existing feature learning neural networks very challenging. Thus, we first propose a novel end-to-end network, AE-GAN-Net, consisting of two AutoEncoders (AEs) with Generative Adversarial Network (GAN) embedding, to learn invariant feature descriptors for cross-domain image matching. Second, a domain-consistent loss function, which balances image content and consistency of feature descriptors for cross-domain image pairs, is introduced to optimize AE-GAN-Net. AE-GAN-Net effectively captures domain-specific information, which is embedded into the learned feature descriptors, thus making the learned feature descriptors robust against image distortion, variations in viewpoints, spatial resolutions, rotation, and scaling. Experimental results show that AE-GAN-Net achieves state-of-the-art performance for image patch retrieval with the cross-domain image patch dataset, which is built from real camera images and the rendered images from 3D image-based point cloud. Finally, by evaluating virtual–real registration for AR on a campus by using the cross-domain image matching results, we demonstrate the feasibility of applying the proposed virtual–real registration to AR in outdoor environments.

Fast 3D Human Face Modeling Method Based on Multiple View 2D Images

2013 ◽  
Vol 273 ◽  
pp. 796-799
Author(s):  
Yong Sheng Wang
Keyword(s):  
Gabor Filter ◽  
Visual Features ◽  
Translation Vector ◽  
Human Face ◽  
Face Modeling ◽  
3D Images ◽  
Multiple View ◽  
Novel Approach ◽  
2D And 3D ◽  
2D Images

This paper presents a novel approach to model 3D human face from multiple view 2D images in a fast mode. Our proposed method mainly includes three steps: 1) Face Recognition from 2D images, 2) Converting 2D images to 3D images, 3) Modeling 3D human face. To extract visual features of both 2D and 3D images, visual features adopted in 3D are described by Point Signature, and visual features utilized in 2D is represented by Gabor filter responses. Afterwards, 3D model is obtained by combining multiple view 2D images through calculating projections vector and translation vector. Experimental results show that our method can model 3D human face with high accuracy and efficiency.

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