Data-Driven Image Processing for Onboard Optical Navigation Around a Binary Asteroid

2022 ◽  
pp. 1-17
Author(s):  
Mattia Pugliatti ◽  
Vittorio Franzese ◽  
Francesco Topputo
Keyword(s):  
Image Processing ◽  
Data Driven ◽  
Optical Navigation ◽  
Binary Asteroid

scholarly journals Two-spacecraft cooperative optical navigation for binary-asteroid exploration

2021 ◽  
Vol 52 (1) ◽  
pp. 214506
Author(s):  
ShengYing ZHU ◽  
Yi XIU ◽  
DongChen LIU ◽  
Ning ZHANG ◽  
Rui XU
Keyword(s):  
Optical Navigation ◽  
Binary Asteroid ◽  
Asteroid Exploration

scholarly journals Generative and discriminative model-based approaches to microscopic image restoration and segmentation

Microscopy ◽  
2020 ◽  
Vol 69 (2) ◽  
pp. 79-91 ◽  
Author(s):  
Shin Ishii ◽  
Sehyung Lee ◽  
Hidetoshi Urakubo ◽  
Hideaki Kume ◽  
Haruo Kasai
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Image Restoration ◽  
Object Segmentation ◽  
Super Resolution ◽  
Data Driven ◽  
Microscopic Image ◽  
Microscopic Images ◽  
Model Based ◽  
Discriminative Model

Abstract Image processing is one of the most important applications of recent machine learning (ML) technologies. Convolutional neural networks (CNNs), a popular deep learning-based ML architecture, have been developed for image processing applications. However, the application of ML to microscopic images is limited as microscopic images are often 3D/4D, that is, the image sizes can be very large, and the images may suffer from serious noise generated due to optics. In this review, three types of feature reconstruction applications to microscopic images are discussed, which fully utilize the recent advancements in ML technologies. First, multi-frame super-resolution is introduced, based on the formulation of statistical generative model-based techniques such as Bayesian inference. Second, data-driven image restoration is introduced, based on supervised discriminative model-based ML technique. In this application, CNNs are demonstrated to exhibit preferable restoration performance. Third, image segmentation based on data-driven CNNs is introduced. Image segmentation has become immensely popular in object segmentation based on electron microscopy (EM); therefore, we focus on EM image processing.

scholarly journals Thermal Infrared Sensing for Near Real-Time Data-Driven Fire Detection and Monitoring Systems

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6803
Author(s):  
Maria João Sousa ◽  
Alexandra Moutinho ◽  
Miguel Almeida
Keyword(s):  
Image Processing ◽  
Situation Awareness ◽  
Thermal Imaging ◽  
Mobile Robotics ◽  
Fire Detection ◽  
Data Driven ◽  
Time Data ◽  
Depth Analysis ◽  
Robotic Perception ◽  
Thermal Cameras

With the increasing interest in leveraging mobile robotics for fire detection and monitoring arises the need to design recognition technology systems for these extreme environments. This work focuses on evaluating the sensing capabilities and image processing pipeline of thermal imaging sensors for fire detection applications, paving the way for the development of autonomous systems for early warning and monitoring of fire events. The contributions of this work are threefold. First, we overview image processing algorithms used in thermal imaging regarding data compression and image enhancement. Second, we present a method for data-driven thermal imaging analysis designed for fire situation awareness in robotic perception. A study is undertaken to test the behavior of the thermal cameras in controlled fire scenarios, followed by an in-depth analysis of the experimental data, which reveals the inner workings of these sensors. Third, we discuss key takeaways for the integration of thermal cameras in robotic perception pipelines for autonomous unmanned aerial vehicle (UAV)-based fire surveillance.

scholarly journals ROOF RECONSTRUCTION FROM AIRBORNE LASER SCANNING DATA BASED ON IMAGE PROCESSING METHODS

2016 ◽  
Vol III-3 ◽  
pp. 407-414 ◽  
Author(s):  
S. Goebbels ◽  
R. Pohle-Fröhlich
Keyword(s):  
Image Processing ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Data Driven ◽  
Processing Methods ◽  
Cloud Data ◽  
Applied Model ◽  
Airborne Laser ◽  
Building Models

The paper presents a new data-driven approach to generate CityGML building models from airborne laser scanning data. The approach is based on image processing methods applied to an interpolated height map and avoids shortcomings of established methods for plane detection like Hough transform or RANSAC algorithms on point clouds. The improvement originates in an interpolation algorithm that generates a height map from sparse point cloud data by preserving ridge lines and step edges of roofs. Roof planes then are detected by clustering the height map’s gradient angles, parameterizations of planes are estimated and used to filter out noise around ridge lines. On that basis, a raster representation of roof facets is generated. Then roof polygons are determined from region outlines, connected to a roof boundary graph, and simplified. Whereas the method is not limited to churches, the method’s performance is primarily tested for church roofs of the German city of Krefeld because of their complexity. To eliminate inaccuracies of spires, contours of towers are detected additionally, and spires are rendered as solids of revolution. In our experiments, the new data-driven method lead to significantly better building models than the previously applied model-driven approach.

scholarly journals Power Pylon Reconstruction Based on Abstract Template Structures Using Airborne LiDAR Data

2019 ◽  
Vol 11 (13) ◽  
pp. 1579 ◽  
Author(s):  
Shichao Chen ◽  
Cheng Wang ◽  
Huayang Dai ◽  
Hebing Zhang ◽  
Feifei Pan ◽  
...  
Keyword(s):  
Image Processing ◽  
Three Dimensional ◽  
Airborne Lidar ◽  
Data Driven ◽  
Computational Time ◽  
Reconstruction Method ◽  
Triangular Pyramid ◽  
Template Structure ◽  
Corner Points ◽  
Pyramid Structures

As an important power facility for transmission corridors, automatic three-dimensional (3D) reconstruction of the pylon plays an important role in the development of smart grid. In this study, a novel three-dimensional reconstruction method using airborne LiDAR (Light Detection And Ranging) point cloud is developed and tested. First, a principal component analysis (PCA) algorithm is performed for pylon redirection based on the structural feature of the upper part of a pylon. Then, based on the structural similarity of a pylon, a pylon is divided into three parts that are inverted triangular pyramid lower structures, quadrangular frustum pyramid middle structures, and complex upper or lateral structures. The reconstruction of the inverted triangular pyramid structures and quadrangular frustum pyramid structures is based on prior knowledge and a data-driven strategy, where the 2D alpha shape algorithm is used to obtain contour points and 2D linear fitting is carried out based on the random sample consensus (RANSAC) method. Complex structures’ reconstruction is based on the priori abstract template structure and a data-driven strategy, where the abstract template structure is used to determine the topological relationship among corner points and the image processing method is used to extract corner points of the abstract template structure. The main advantages in the proposed method include: (1) Improving the accuracy of the pylon decomposition method through introducing a new feature to identify segmentation positions; (2) performing the internal structure of quadrangular frustum pyramids reconstruction; (3) establishing the abstract template structure and using image processing methods to improve computational efficiency of pylon reconstruction. Eight types of pylons are tested in this study, and the average error of pylon reconstruction is 0.32 m and the average of computational time is 0.8 s. These results provide evidence that the pylon reconstruction method developed in this study has high accuracy, efficiency, and applicability.

scholarly journals Development of data driven adaptive edge detectors for image processing

2021 ◽  
Vol 1187 (1) ◽  
pp. 012032
Author(s):  
H G Likhith ◽  
H L Nishanth ◽  
E Lohit ◽  
M S Rudramurthy ◽  
S A Sushma ◽  
...  
Keyword(s):  
Image Processing ◽  
Data Driven ◽  
Edge Detectors
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