scholarly journals A New Skeleton Feature Extraction Method for Terrain Model Using Profile Recognition and Morphological Simplification

2013 ◽  
Vol 2013 ◽  
pp. 1-16
Author(s):  
Huijie Zhang ◽  
Zhiqiang Ma ◽  
Yaxin Liu ◽  
Xinting He ◽  
Yun Ma
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Feature Points ◽  
Feature Extraction Method ◽  
Terrain Model ◽  
Noise Point ◽  
Key Factor ◽  
Morphological Theory ◽  
Negative Effect ◽  
Real Experimental Data

It is always difficul to reserve rings and main truck lines in the real engineering of feature extraction for terrain model. In this paper, a new skeleton feature extraction method is proposed to solve these problems, which put forward a simplification algorithm based on morphological theory to eliminate the noise points of the target points produced by classical profile recognition. As well all know, noise point is the key factor to influence the accuracy and efficiency of feature extraction. Our method connected the optimized feature points subset after morphological simplification; therefore, the efficiency of ring process and pruning has been improved markedly, and the accuracy has been enhanced without the negative effect of noisy points. An outbranching concept is defined, and the related algorithms are proposed to extract sufficient long trucks, which is capable of being consistent with real terrain skeleton. All of algorithms are conducted on many real experimental data, including GTOPO30 and benchmark data provided by PPA to verify the performance and accuracy of our method. The results showed that our method precedes PPA as a whole.

A Sum-of-Squares Polynomial Approach for Road Anomaly Detection Using Vehicle Sensor Measurements

2017 ◽  
Author(s):  
Dule Shu ◽  
Constantino Lagoa ◽  
Timothy Cleary
Keyword(s):  
Feature Extraction ◽  
Anomaly Detection ◽  
Extraction Method ◽  
Sum Of Squares ◽  
New Method ◽  
Feature Points ◽  
Feature Extraction Method ◽  
Different Types ◽  
Simulation Results ◽  
Vehicle Sensor

This paper presents a new method for road anomaly detection. The existence of road anomalies is determined by the behaviors of vehicles. A special polynomial named Sum-of-Squares (SOS) polynomial is used as a metric to evaluate the normality of vehicle behaviors. The method can process multiple types of sensor measurements. A feature extraction method is used to obtain concise representations of the sensor measurements. These representations, called feature points, are used to calculate the value of the SOS polynomial. Simulation results have been shown to demonstrate that the proposed method can effectively detect different types of road anomalies.

scholarly journals Research on Feature Extraction Method of Indoor Visual Positioning Image Based on Area Division of Foreground and Background

2021 ◽  
Vol 10 (6) ◽  
pp. 402
Author(s):  
Ping Zheng ◽  
Danyang Qin ◽  
Bing Han ◽  
Lin Ma ◽  
Teklu Merhawit Berhane
Keyword(s):  
Feature Extraction ◽  
Open Source Software ◽  
Extraction Method ◽  
Feature Matching ◽  
Physical World ◽  
Feature Points ◽  
Feature Extraction Method ◽  
Visual Positioning ◽  
Extraction Algorithm ◽  
Better Than

In the process of indoor visual positioning and navigation, difficult points often exist in corridors, stairwells, and other scenes that contain large areas of white walls, strong consistent background, and sparse feature points. Aiming at the problem of positioning and navigation in the real physical world where the walls with sparse feature points are difficult to be filled with pictures, this paper designs a feature extraction method, ARAC (Adaptive Region Adjustment based on Consistency) using Free and Open-Source Software and tools. It divides the image into foreground and background and extracts their features respectively, to achieve not only retain positioning information but also focus more energy on the foreground area which is favourable for navigation. In the test phase, under the combined conditions of illumination, scale and affine changes, the feature matching maps by the feature extraction algorithm proposed in this paper are compared with those by SIFT and SURF. Experiments show that the number of correctly matched feature pairs obtained by ARAC is better than SIFT and SURF, and whose time of feature extraction and matching is comparable to SURF, which verifies the accuracy and efficiency of the ARAC feature extraction method.

scholarly journals Photovoltaic Power Station Electromagnetic Environment Complexity Evaluation Utilizing Logarithmic Morphological Gradient Spectrum

2021 ◽  
Vol 9 ◽  
Author(s):  
Hua-chen Xi ◽  
Bing Li ◽  
Wen-hui Mai ◽  
Xiong Xu ◽  
Ya Wang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Electromagnetic Environment ◽  
Power Station ◽  
Feature Extraction Method ◽  
Morphological Gradient ◽  
Photovoltaic Power ◽  
Morphological Theory ◽  
Extraction Scheme ◽  
The Impact

In this paper, a feature extraction method for evaluating the complexity of the Electromagnetic Environment (EME) of the photovoltaic power station is presented by using logarithmic morphological gradient spectrum (LMGS) based on the mathematical morphological theory. We use LMGS to evaluate electromagnetic environment signals. We also explored the impact of structure element (SE) on the MS, MGS, and LMGS. Three types of SE, mean the line SE, square SE and diamond SE, are utilized and compared for computing the LMGS. EME signals with four complexity degrees are simulated to evaluate the effectiveness of the presented method. The experimental results have shown that the feature extraction scheme proposed in this paper is a reasonable method to classify the complexity of EME.

A Novel Prediction of Quaternary Structural Type of Proteins with Gene Ontology

2020 ◽  
Vol 27 (4) ◽  
pp. 313-320 ◽  
Author(s):  
Xuan Xiao ◽  
Wei-Jie Chen ◽  
Wang-Ren Qiu
Keyword(s):  
Gene Ontology ◽  
Feature Extraction ◽  
Extraction Method ◽  
Quaternary Structure ◽  
Structural Type ◽  
Sequence Information ◽  
Prediction System ◽  
Data Set ◽  
Feature Extraction Method ◽  
Prediction Rate

Background: The information of quaternary structure attributes of proteins is very important because it is closely related to the biological functions of proteins. With the rapid development of new generation sequencing technology, we are facing a challenge: how to automatically identify the four-level attributes of new polypeptide chains according to their sequence information (i.e., whether they are formed as just as a monomer, or as a hetero-oligomer, or a homo-oligomer). Objective: In this article, our goal is to find a new way to represent protein sequences, thereby improving the prediction rate of protein quaternary structure. Methods: In this article, we developed a prediction system for protein quaternary structural type in which a protein sequence was expressed by combining the Pfam functional-domain and gene ontology. turn protein features into digital sequences, and complete the prediction of quaternary structure through specific machine learning algorithms and verification algorithm. Results: Our data set contains 5495 protein samples. Through the method provided in this paper, we classify proteins into monomer, or as a hetero-oligomer, or a homo-oligomer, and the prediction rate is 74.38%, which is 3.24% higher than that of previous studies. Through this new feature extraction method, we can further classify the four-level structure of proteins, and the results are also correspondingly improved. Conclusion: After the applying the new prediction system, compared with the previous results, we have successfully improved the prediction rate. We have reason to believe that the feature extraction method in this paper has better practicability and can be used as a reference for other protein classification problems.

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