scholarly journals Laser Spot Center Location Method for Chinese Spaceborne GF-7 Footprint Camera

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2319
Author(s):  
Chaofeng Ren ◽  
Junfeng Xie ◽  
Xiaodong Zhi ◽  
Yun Yang ◽  
Shuai Yang
Keyword(s):  
Template Matching ◽  
Feature Matching ◽  
Image Data ◽  
Stereo Image ◽  
Laser Altimeter ◽  
Spot Center ◽  
Brightness Difference ◽  
Least Squares Adjustment ◽  
Spot Image ◽  
Image Position

The Gaofen-7 (GF-7) satellite is equipped with two area array sensor footprint cameras to capture the laser altimeter spot. In order to establish a direct correspondence between the laser data and the stereo image data, a new method is proposed to fit the center of the spot using the brightness difference between the spot image and the footprint image. First, the geometric registration between the spot image and the footprint image is completed based on feature matching or template matching. Then, the brightness values between the two images are extracted from the corresponding image position to form a measurement, and the least squares adjustment method is used to calculate the parameters of the brightness conversion model between the spot image and the footprint image. Finally, according to the registration relationship, the center of the identified spots is respectively positioned in the footprint images, so that the laser spots are accurately identified in the along-track stereo footprint images. The experimental results show that the spot error of this method is less than 0.7 pixel, which has higher reliability and stability, and can be used for a GF-7 satellite footprint camera.

scholarly journals Target tracking accuracy and latency with different 4D ultrasound systems – a robotic phantom study

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Svenja Ipsen ◽  
Sven Böttger ◽  
Holger Schwegmann ◽  
Floris Ernst
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Template Matching ◽  
Image Data ◽  
Distinct Advantage ◽  
Reference Trajectory ◽  
Data Streaming ◽  
Tracking Accuracy ◽  
Time Data ◽  
4D Ultrasound

AbstractUltrasound (US) imaging, in contrast to other image guidance techniques, offers the distinct advantage of providing volumetric image data in real-time (4D) without using ionizing radiation. The goal of this study was to perform the first quantitative comparison of three different 4D US systems with fast matrix array probes and real-time data streaming regarding their target tracking accuracy and system latency. Sinusoidal motion of varying amplitudes and frequencies was used to simulate breathing motion with a robotic arm and a static US phantom. US volumes and robot positions were acquired online and stored for retrospective analysis. A template matching approach was used for target localization in the US data. Target motion measured in US was compared to the reference trajectory performed by the robot to determine localization accuracy and system latency. Using the robotic setup, all investigated 4D US systems could detect a moving target with sub-millimeter accuracy. However, especially high system latency increased tracking errors substantially and should be compensated with prediction algorithms for respiratory motion compensation.

Two-Dimensional Image Based Product Connector Recognition for Robotic Disassembly in Remanufacturing

2021 ◽  
Author(s):  
Yang Hu ◽  
Yalin Wang ◽  
Feng Xu ◽  
Bitao Yao ◽  
Wenjun Xu ◽  
...  
Keyword(s):  
Template Matching ◽  
Search Strategy ◽  
Feature Matching ◽  
Resource Conservation ◽  
Recognition Algorithm ◽  
Two Dimensional ◽  
Crucial Step ◽  
Two Dimensional Image ◽  
Disassembly Process ◽  
Robotic Disassembly

Abstract Remanufacturing has received increasing attention for environmental protection and resource conservation considerations. Disassembly is a crucial step in remanufacturing, is always done manually which is inefficient while robotic disassembly can improve the efficiency of the disassembly. Aiming at the problem of product connector recognition during the robotic disassembly process, we analyze the template matching and feature matching principles based on two-dimensional images. To reduce the computational complexity of traditional template matching, a stepwise search strategy combining coarse and fine is proposed. Based on this a product connector recognition algorithm based on fast template matching and a product connector recognition algorithm based on feature matching is designed. Taking bolts and hexagon nuts as examples, the recognition effects of the two algorithms are compared and analyzed.

A Large Margin Learning Method for Matching Images of Natural Objects with Different Dimensions

2015 ◽  
pp. 323-341
Author(s):  
Haoyi Zhou ◽  
Jun Zhou ◽  
Haichuan Yang ◽  
Cheng Yan ◽  
Xiao Bai ◽  
...  
Keyword(s):  
Feature Matching ◽  
Hyperspectral Image ◽  
Graph Matching ◽  
Graph Model ◽  
Image Data ◽  
Weighted Graph ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Natural Objects ◽  
Different Dimensions

Imaging devices are of increasing use in environmental research requiring an urgent need to deal with such issues as image data, feature matching over different dimensions. Among them, matching hyperspectral image with other types of images is challenging due to the high dimensional nature of hyperspectral data. This chapter addresses this problem by investigating structured support vector machines to construct and learn a graph-based model for each type of image. The graph model incorporates both low-level features and stable correspondences within images. The inherent characteristics are depicted by using a graph matching algorithm on extracted weighted graph models. The effectiveness of this method is demonstrated through experiments on matching hyperspectral images to RGB images, and hyperspectral images with different dimensions on images of natural objects.

scholarly journals Remote-sensing science and technology for studying glacier processes in high Asia

2000 ◽  
Vol 31 ◽  
pp. 164-170 ◽  
Author(s):  
Michael P. Bishop ◽  
Jeffrey S. Kargel ◽  
Hugh H. Kieffer ◽  
David J. MacKinnon ◽  
Bruce H. Raup ◽  
...  
Keyword(s):  
Image Data ◽  
Stereo Image ◽  
Accurate Information ◽  
Glacier Mass Balance ◽  
Digital Elevation ◽  
High Asia ◽  
Global Land ◽  
Space Project ◽  
Sediment Transfer ◽  
Elevation Model

AbstractA large number of multispectral and stereo-image data are expected to become available as part of the Global Land Ice Measurements from Space project. We investigate digital elevation model extraction, anisotropic reflectance correction and selected glacier analysis tasks that must be developed to achieve full utility of these new data. Results indicate that glaciers in the Karakoram and Nanga Parbat Himalaya, northern Pakistan, exhibit unique spectral, spatial and geomorphometric patterns that can be exploited by various models and algorithms to produce accurate information regarding glacier extent, supraglacial features and glacier geomorphology The integration of spectral, spatial and geomorphometric features, coupled with approaches for advanced pattern recognition, can help geoscientists study glacier mass balance, glacier erosion, sediment-transfer efficiency and landscape evolution.

scholarly journals Tourist Behavior Recognition Through Scenic Spot Image Retrieval Based on Image Processing

2020 ◽  
Vol 37 (4) ◽  
pp. 619-626
Author(s):  
Shizhen Bai ◽  
Fuli Han
Keyword(s):  
Image Processing ◽  
Image Retrieval ◽  
Visual Information ◽  
Feature Matching ◽  
Scale Invariant ◽  
Behavior Recognition ◽  
Processing Technologies ◽  
Tourist Behavior ◽  
Scenic Spot ◽  
Spot Image

The monitoring of tourist behaviors, coupled with the recognition of scenic spots, greatly improves the quality and safety of travel. The visual information is the underlying features of scenic spot images, but the semantics of the information have not been satisfactorily classified or described. Based on image processing technologies, this paper presents a novel method for scenic spot retrieval and tourist behavior recognition. Firstly, the framework of scenic spot image retrieval was constructed, followed by a detailed introduction to the extraction of scale invariant feature transform (SIFT) features. The SIFT feature extraction includes five steps: scale space construction, local space extreme point detection, precise positioning of key points, determination of key point size and direction, and generation of SIFT descriptor. Next, multiple correlated images were mined for the target scenic spot image, and the feature matching method between the target image and the set of scenic spot images was introduced in details. On this basis, a tourist behavior recognition method was designed based on temporal and spatial consistency. The proposed method was proved effective through experiments. The research results provide theoretical reference for image retrieval and behavior recognition in many other fields.

scholarly journals Congruence analysis of point clouds from unstable stereo image sequences

2014 ◽  
Vol XL-5 ◽  
pp. 301-306 ◽  
Author(s):  
C. Jepping ◽  
F. Bethmann ◽  
T. Luhmann
Keyword(s):  
High Speed ◽  
Image Data ◽  
Point Clouds ◽  
Image Sequences ◽  
Stereo Image ◽  
Free Form ◽  
Crash Test ◽  
3D Point Clouds ◽  
Congruence Analysis ◽  
Free Form Surfaces

This paper deals with the correction of exterior orientation parameters of stereo image sequences over deformed free-form surfaces without control points. Such imaging situation can occur, for example, during photogrammetric car crash test recordings where onboard high-speed stereo cameras are used to measure 3D surfaces. As a result of such measurements 3D point clouds of deformed surfaces are generated for a complete stereo sequence. The first objective of this research focusses on the development and investigation of methods for the detection of corresponding spatial and temporal tie points within the stereo image sequences (by stereo image matching and 3D point tracking) that are robust enough for a reliable handling of occlusions and other disturbances that may occur. The second objective of this research is the analysis of object deformations in order to detect stable areas (congruence analysis). For this purpose a RANSAC-based method for congruence analysis has been developed. This process is based on the sequential transformation of randomly selected point groups from one epoch to another by using a 3D similarity transformation. The paper gives a detailed description of the congruence analysis. The approach has been tested successfully on synthetic and real image data.

scholarly journals UAV AND LIDAR IMAGE REGISTRATION: A SURF-BASED APPROACH FOR GROUND CONTROL POINTS SELECTION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 413-418 ◽  
Author(s):  
B. Kalantar ◽  
N. Ueda ◽  
H. A. H. Al-Najjar ◽  
H. Moayedi ◽  
A. A. Halin ◽  
...  
Keyword(s):  
Image Registration ◽  
Water Resource Management ◽  
Feature Matching ◽  
Image Data ◽  
Ground Control ◽  
Control Points ◽  
Speeded Up Robust Features ◽  
Ground Control Points ◽  
Edge Detectors ◽  
Edge Features

<p><strong>Abstract.</strong> Multisource remote sensing image data provides synthesized information to support many applications including land cover mapping, urban planning, water resource management, and GIS modelling. Effectively utilizing such images however requires proper image registration, which in turn highly relies on accurate ground control points (GCP) selection. This study evaluates the performance of the interest point descriptor SURF (Speeded-Up Robust Features) for GCPs selection from UAV and LiDAR images. The main motivation for using SURF is due to it being invariant to scaling, blur and illumination, and partially invariant to rotation and view point changes. We also consider features generated by the Sobel and Canny edge detectors as complements to potentially increase the accuracy of feature matching between the UAV and LiDAR images. From our experiments, the red channel (Band-3) produces the most accurate and practical results in terms of registration, while adding the edge features seems to produce lacklustre results.</p>

scholarly journals Cooperative Cloud-Edge Feature Extraction Architecture for Mobile Image Retrieval

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Chao He ◽  
Gang Ma
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Feature Matching ◽  
Image Data ◽  
Projection Matrix ◽  
Data Set ◽  
Retrieval Accuracy ◽  
Cloud Server ◽  
Retrieval Scheme ◽  
Edge Feature

Mobile image retrieval greatly facilitates our lives and works by providing various retrieval services. The existing mobile image retrieval scheme is based on mobile cloud-edge computing architecture. That is, user equipment captures images and uploads the captured image data to the edge server. After preprocessing these captured image data and extracting features from these image data, the edge server uploads the extracted features to the cloud server. However, the feature extraction on the cloud server is noncooperative with the feature extraction on the edge server which cannot extract features effectively and has a lower image retrieval accuracy. For this, we propose a collaborative cloud-edge feature extraction architecture for mobile image retrieval. The cloud server generates the projection matrix from the image data set with a feature extraction algorithm, and the edge server extracts the feature from the uploaded image with the projection matrix. That is, the cloud server guides the edge server to perform feature extraction. This architecture can effectively extract the image data on the edge server, reduce network load, and save bandwidth. The experimental results indicate that this scheme can upload few features to get high retrieval accuracy and reduce the feature matching time by about 69.5% with similar retrieval accuracy.

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