scholarly journals Improved ORB-SLAM2 Algorithm Based on Information Entropy and Image Sharpening Adjustment

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Kaiqing Luo ◽  
Manling Lin ◽  
Pengcheng Wang ◽  
Siwei Zhou ◽  
Dan Yin ◽  
...  
Keyword(s):  
Information Entropy ◽  
Large Angle ◽  
Image Sequences ◽  
Image Texture ◽  
Image Block ◽  
Trajectory Error ◽  
Texture Information ◽  
Localization And Mapping ◽  
Angle Rotation ◽  
Relative Trajectory

Simultaneous Localization and Mapping (SLAM) has become a research hotspot in the field of robots in recent years. However, most visual SLAM systems are based on static assumptions which ignored motion effects. If image sequences are not rich in texture information or the camera rotates at a large angle, SLAM system will fail to locate and map. To solve these problems, this paper proposes an improved ORB-SLAM2 algorithm based on information entropy and sharpening processing. The information entropy corresponding to the segmented image block is calculated, and the entropy threshold is determined by the adaptive algorithm of image entropy threshold, and then the image block which is smaller than the information entropy threshold is sharpened. The experimental results show that compared with the ORB-SLAM2 system, the relative trajectory error decreases by 36.1% and the absolute trajectory error decreases by 45.1% compared with ORB-SLAM2. Although these indicators are greatly improved, the processing time is not greatly increased. To some extent, the algorithm solves the problem of system localization and mapping failure caused by camera large angle rotation and insufficient image texture information.

scholarly journals Extract enclosure culture in lakes based on remote sensing image texture information

2006 ◽  
Vol 18 (4) ◽  
pp. 337-342 ◽  
Author(s):  
LI Junjie ◽  
◽  
HE Longhua ◽  
DAI Jingfang ◽  
LI Jinlian
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Image Texture ◽  
Texture Information

scholarly journals A Visual SLAM Robust against Dynamic Objects Based on Hybrid Semantic-Geometry Information

2021 ◽  
Vol 10 (10) ◽  
pp. 673
Author(s):  
Sheng Miao ◽  
Xiaoxiong Liu ◽  
Dazheng Wei ◽  
Changze Li
Keyword(s):  
Moving Objects ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Visual Localization ◽  
Epipolar Constraint ◽  
Trajectory Error ◽  
Localization And Mapping ◽  
Dynamic Objects ◽  
Public Datasets ◽  
Localization Approach

A visual localization approach for dynamic objects based on hybrid semantic-geometry information is presented. Due to the interference of moving objects in the real environment, the traditional simultaneous localization and mapping (SLAM) system can be corrupted. To address this problem, we propose a method for static/dynamic image segmentation that leverages semantic and geometric modules, including optical flow residual clustering, epipolar constraint checks, semantic segmentation, and outlier elimination. We integrated the proposed approach into the state-of-the-art ORB-SLAM2 and evaluated its performance on both public datasets and a quadcopter platform. Experimental results demonstrated that the root-mean-square error of the absolute trajectory error improved, on average, by 93.63% in highly dynamic benchmarks when compared with ORB-SLAM2. Thus, the proposed method can improve the performance of state-of-the-art SLAM systems in challenging scenarios.

scholarly journals A high-efficiency, information-based exploration path planning method for active simultaneous localization and mapping

2020 ◽  
Vol 17 (1) ◽  
pp. 172988142090320
Author(s):  
Peng Li ◽  
Cai-yun Yang ◽  
Rui Wang ◽  
Shuo Wang
Keyword(s):  
Path Planning ◽  
Information Entropy ◽  
High Efficiency ◽  
Dead Reckoning ◽  
Simultaneous Localization And Mapping ◽  
Planning Method ◽  
Coverage Area ◽  
High Coverage ◽  
Full Coverage ◽  
Localization And Mapping

The efficiency of exploration in an unknown scene and full coverage of the scene are essential for a robot to complete simultaneous localization and mapping actively. However, it is challenging for a robot to explore an unknown environment with high efficiency and full coverage autonomously. In this article, we propose a novel exploration path planning method based on information entropy. An information entropy map is first constructed, and its boundary features are extracted. Then a Dijkstra-based algorithm is applied to generate candidate exploration paths based on the boundary features. The dead-reckoning algorithm is used to predict the uncertainty of the robot’s pose along each candidate path. The exploration path is selected based on exploration efficiency and/or high coverage. Simulations and experiments are conducted to evaluate the proposed method’s effectiveness. The results demonstrated that the proposed method achieved not only higher exploration efficiency but also a larger coverage area.

Aerial Image Texture Information in the Estimation of Northern Deciduous and Mixed Wood Forest Leaf Area Index (LAI)

1998 ◽  
Vol 64 (1) ◽  
pp. 64-76 ◽  
Author(s):  
Mike A. Wulder ◽  
Ellsworth F. LeDrew ◽  
Steven E. Franklin ◽  
Mike B. Lavigne
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Image Texture ◽  
Aerial Image ◽  
Area Index ◽  
Texture Information

CLUSTERING MULTIPLE IMAGE SEQUENCES WITH A SEQUENCE-TO-SEQUENCE SIMILARITY MEASURE

2005 ◽  
Vol 19 (04) ◽  
pp. 551-564
Author(s):  
YING SHAN ◽  
HARPREET S. SAWHNEY ◽  
ART POPE
Keyword(s):  
Similarity Measure ◽  
Selection Process ◽  
Sequence Similarity ◽  
Image Sequences ◽  
Image Texture ◽  
Sequence Matching ◽  
Matching Problem ◽  
Texture Recognition ◽  
Background Clutter ◽  
Similar Pair

We propose a novel similarity measure of two image sequences based on shapeme histograms. The idea of shapeme histogram has been used for single image/texture recognition, but is used here to solve the sequence-to-sequence matching problem. We develop techniques to represent each sequence as a set of shapeme histograms, which captures different variations of the object appearances within the sequence. These shapeme histograms are computed from the set of 2D invariant features that are stable across multiple images in the sequence, and therefore minimizes the effect of both background clutter, and 2D pose variations. We define sequence similarity measure as the similarity of the most similar pair of images from both sequences. This definition maximizes the chance of matching between two sequences of the same object, because it requires only part of the sequences being similar. We also introduce a weighting scheme to conduct an implicit feature selection process during the matching of two shapeme histograms. Experiments on clustering image sequences of tracked objects demonstrate the efficacy of the proposed method.

The Quick Method for Image Registration Combined with Information Entropy and Cross-Correlation Matrix

2014 ◽  
Vol 971-973 ◽  
pp. 1812-1815
Author(s):  
Yan Hai Wu ◽  
Fang Ni Zhang
Keyword(s):  
Image Registration ◽  
Information Entropy ◽  
Correlation Matrix ◽  
Cross Correlation ◽  
Image Block ◽  
Quick Method ◽  
Registration Method ◽  
Current Image ◽  
Level Information ◽  
Left Corner

The current image registration technique based on gray-level information has shortcomings on time and amount of computation brought by the whole image registration, this paper proposes a quick registration method, which makes a combination of image information entropy and cross-correlation matrix: First, segmenting the target image into blocks and calculating to get the maximum entropy image block, then using it as a template to calculate the cross-correlation matrix with floating image; Second, making the point where the maximum cross-correlation value ​​is as the upper left corner, grasping a same-size block with template on floating image; Finally, obtaining registration parameters through calculation for these two blocks to achieve the purpose of registration. Experimental results show that this method has less computational complexity with the similar registration results, and takes less time. It’s effective and feasible.

scholarly journals EXTRACTION OF SOLAR CELLS FROM UAV-BASED THERMAL IMAGE SEQUENCES

2018 ◽  
Vol IV-1 ◽  
pp. 69-75
Author(s):  
L. Hoegner ◽  
N. Pfaffenzeller ◽  
L. Wagner ◽  
U. Stilla
Keyword(s):  
Solar Cells ◽  
Local Coordinate System ◽  
Thermal Infrared ◽  
Automatic Generation ◽  
Image Sequences ◽  
Monte Carlo Algorithm ◽  
Image Block ◽  
Ground Control Points ◽  
Bundle Block Adjustment ◽  
Four Corners

<p><strong>Abstract.</strong> This paper discusses the automatic generation of thermal infrared ortho image mosaics and the extraction of solar cells from these ortho image mosaics. Image sequences are recorded by a thermal infrared (TIR) camera mounted on a remotely piloted aerial system (RPAS). The image block is relatively oriented doing a bundle block adjustment and transferred to a local coordinate system using ground control points. The resulting ortho image mosaic is searched for solar cells. A library of templates of solar cells from thermal images is used to learn an implicit shape model. The extraction of the single solar cells is done by estimating corners and centre points of cells using these shape models in a Markov-Chain-Monte-Carlo algorithm by combining four corners and a centre point. As for the limited geometric resolution and radiometric contrast, most of the cells are not directly detected. An iterative process based on the knowledge of the regular grid structure of a solar cell installation is used to predict further cells and verify their existence by repeating the corner extraction and grammar combination. Results show that this work flow is able to detect most of the solar cells under the condition that the cells have a more or less common radiometric behaviour and no reflections i.e. from the sun occur. The cells need a rectangular shape and have the same orientation so that the model of the grammar is applicable to the solar cells.</p>

Feature Extraction of Foam Nickel Surface Based on Multi-Scale Texture Analysis

2019 ◽  
Vol 23 (2) ◽  
pp. 175-182
Author(s):  
Jianqi Li ◽  
Binfang Cao ◽  
Fangyan Nie ◽  
Minhan Zhu ◽  
◽  
...  
Keyword(s):  
Texture Analysis ◽  
Surface Defects ◽  
Low Frequency ◽  
Image Texture ◽  
Nickel Surface ◽  
Multi Scale ◽  
Texture Information ◽  
Surface Image ◽  
Indentation Tests ◽  
Frequency Components

In the foam nickel process, texture is the indicator of foam nickel performance. In order to recognize foam nickel surface defects accurately and provide guidance for production operations, this paper proposes a method for extracting foam nickel image textures based on multi-scale texture analysis. First, nonsubsampled contourlet (NSCT) is used to carry out foam nickel image multi-scale decomposition, and the low-frequency and high-frequency components following decomposition are used to characterize different defect details. Then, the Haralick eigenvalue, which measures the foam nickel image texture information at each sub-band, is calculated. The KPCA and optimal DAG-SVM are adopted in order to reduce the parameter dimension and clarify defects. Tests are carried out on the foam nickel surface image samples, including crack, scratch, pollution, leakage, and indentation tests. The results indicate that the method proposed in this paper can extract different pieces of detailed texture information and can achieve a defect-identifying accuracy of up to 88.9%.

scholarly journals Research on 2F Optical Correlator Based on Neural Network Filter for Recognizing Large-Angle Rotation Distortion Target

2020 ◽  
Vol 12 (2) ◽  
pp. 1-10
Author(s):  
Tuo Yang ◽  
Minxin Chen ◽  
Yufei Xiao ◽  
Haidong Xu ◽  
Ping Xu
Keyword(s):  
Neural Network ◽  
Large Angle ◽  
Optical Correlator ◽  
Angle Rotation
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