scholarly journals Real-time Multispectral Image Processing and Registration on 3D Point Cloud for Vineyard Analysis

2021 ◽  
Author(s):  
Thibault Clamens ◽  
Georgios Alexakis ◽  
Raphaël Duverne ◽  
Ralph Seulin ◽  
Eric Fauvet ◽  
...  
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Point Cloud ◽  
Multispectral Image ◽  
3D Point Cloud ◽  
Multispectral Image Processing

Real-Time 3D object detection using improved convolutional neural network based on image-driven point cloud

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhiyong Gao ◽  
Jianhong Xiang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Point Cloud ◽  
3D Object ◽  
3D Object Detection ◽  
Instance Segmentation

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN is composed of the frustum sequence module, 3D instance segmentation module S-NET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module E-NET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms the state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved convolutional neural network (CNN) based on image-driven point clouds.

UAV-Based Crop Monitoring Through Multispectral Image Processing using Open-Source Tools

2020 ◽  
Author(s):  
Pedro Castro ◽  
João Borges ◽  
Sofia Klautau ◽  
Samya Pinheiro ◽  
Lucas Shibata ◽  
...  
Keyword(s):  
Image Processing ◽  
Open Source ◽  
Multispectral Image ◽  
Crop Monitoring ◽  
Multispectral Image Processing

scholarly journals Advanced multispectral image-processing endoscopy system for visualizing two-dimensional hemoglobin saturation and relative hemoglobin concentration

2019 ◽  
Vol 07 (11) ◽  
pp. E1442-E1447
Author(s):  
Toru Chiba ◽  
Yoshimi Obara ◽  
Masaharu Murata ◽  
Tomohiko Akahoshi
Keyword(s):  
Image Processing ◽  
Tumor Hypoxia ◽  
Small Animal ◽  
Multispectral Image ◽  
Frame Rate ◽  
Two Dimensional ◽  
Endoscope System ◽  
Hb Concentration ◽  
Multispectral Image Processing

Abstract Background and study aims The association of tumor hypoxia and vascularization with malignant progression is recognized, and detection by measuring tissue hemoglobin (Hb) saturation and concentration has attracted attention. In this study, we designed a simple algorithm and multispectral image-processing endoscopy system to map relative Hb concentration and Hb saturation for detection of tumors in small animal viscera in vivo. Materials and methods We designed and validated an optical filter-equipped endoscope system for two-dimensional visualization of Hb concentration and saturation maps and used it in a real-time video examination. A simplified method based on spectral data capture and analysis of defuse reflection of mucosa, including image capture and data processing of the spectral features of Hb oxygenation, was developed. Results An Hb saturation calibration curve was obtained. Then, differences in oxygenation levels between normal mucosa and in vivo tumors in a small animal model were determined by using the new method and endoscope system. Conclusions A multispectral image-processing endoscopic system with a mapping frame rate comparable to that of white light imaging systems (7.5 frames/second) was developed.

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