scholarly journals Target Matching Recognition for Satellite Images Based on the Improved FREAK Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Yantong Chen ◽  
Wei Xu ◽  
Yongjie Piao
Keyword(s):  
Satellite Remote Sensing ◽  
Large Data ◽  
Scale Space ◽  
Feature Descriptor ◽  
Test Results ◽  
Feature Detector ◽  
Simulation Test ◽  
Extraction Algorithm ◽  
New Feature ◽  
Target Matching

Satellite remote sensing image target matching recognition exhibits poor robustness and accuracy because of the unfit feature extractor and large data quantity. To address this problem, we propose a new feature extraction algorithm for fast target matching recognition that comprises an improved feature from accelerated segment test (FAST) feature detector and a binary fast retina key point (FREAK) feature descriptor. To improve robustness, we extend the FAST feature detector by applying scale space theory and then transform the feature vector acquired by the FREAK descriptor from decimal into binary. We reduce the quantity of data in the computer and improve matching accuracy by using the binary space. Simulation test results show that our algorithm outperforms other relevant methods in terms of robustness and accuracy.

scholarly journals Dihedral Group D4—A New Feature Extraction Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 548
Author(s):  
Puneet Sharma
Keyword(s):  
Dihedral Group ◽  
Feature Vector ◽  
Feature Descriptor ◽  
Image Region ◽  
Histograms Of Oriented Gradients ◽  
Wide Range ◽  
Extraction Algorithm ◽  
Complete Set ◽  
New Feature ◽  
Group D

In this paper, we propose a new feature descriptor for images that is based on the dihedral group D 4 , the symmetry group of the square. The group action of the D 4 elements on a square image region is used to create a vector space that forms the basis for the feature vector. For the evaluation, we employed the Error-Correcting Output Coding (ECOC) algorithm and tested our model with four diverse datasets. The results from the four databases used in this paper indicate that the feature vectors obtained from our proposed D 4 algorithm are comparable in performance to that of Histograms of Oriented Gradients (HOG) model. Furthermore, as the D 4 model encapsulates a complete set of orientations pertaining to the D 4 group, it enables its generalization to a wide range of image classification applications.

scholarly journals A SIFT-Like Feature Detector and Descriptor for Multibeam Sonar Imaging

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Wanyuan Zhang ◽  
Tian Zhou ◽  
Chao Xu ◽  
Meiqin Liu
Keyword(s):  
Speckle Noise ◽  
Scale Space ◽  
Feature Descriptor ◽  
Feature Detector ◽  
Interest Points ◽  
Scale Invariant ◽  
Multibeam Sonar ◽  
Sonar Images ◽  
Imaging Sonar ◽  
Scale Invariant Feature

Multibeam imaging sonar has become an increasingly important tool in the field of underwater object detection and description. In recent years, the scale-invariant feature transform (SIFT) algorithm has been widely adopted to obtain stable features of objects in sonar images but does not perform well on multibeam sonar images due to its sensitivity to speckle noise. In this paper, we introduce MBS-SIFT, a SIFT-like feature detector and descriptor for multibeam sonar images. This algorithm contains a feature detector followed by a local feature descriptor. A new gradient definition robust to speckle noise is presented to detect extrema in scale space, and then, interest points are filtered and located. It is also used to assign orientation and generate descriptors of interest points. Simulations and experiments demonstrate that the proposed method can capture features of underwater objects more accurately than existing approaches.

EVALUASI ANR PADA TRANSMISI DATA NETWORK TERHADAP WP-REST API DALAM APLIKASI ANDROID

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Agung Riyadi
Keyword(s):  
Large Data ◽  
Data Retrieval ◽  
Test Results ◽  
Android Application ◽  
Retrieval Process ◽  
Request Queue ◽  
Plant Data ◽  
Android Development ◽  
Rest Api ◽  
The One

The One of many way to connect to the database through the android application is using volleyball and RESTAPI. By using RestAPI, the android application does not directly connect to the database but there is an intermediary in the form of an API. In android development, Android-volley has the disadvantage of making requests from large and large data, so an evaluation is needed to test the capabilities of the Android volley. This research was conducted to test android-volley to retrieve data through RESTAPI presented in the form of an application to retrieve medicinal plant data. From the test results can be used by volley an error occurs when the back button is pressed, in this case another process is carried out if the previous volley has not been loaded. This error occurred on several android versions such as lollipops and marshmallows also on some brands of devices. So that in using android-volley developer need to check the request queue process that is carried out by the user, if the data retrieval process by volley has not been completed, it is necessary to stop the process to download data using volley so that there is no Android Not Responding (ANR) error.Keywords: Android, Volley, WP REST API, ANR Error

Study on Impact of Vibration Direction on Linear Vibration Screening Effect

2012 ◽  
Vol 226-228 ◽  
pp. 186-190
Author(s):  
Yue Min Zhao ◽  
Ke Wang ◽  
Liang Dong ◽  
Bo Zhang ◽  
Xu Liang Yang ◽  
...  
Keyword(s):  
Sieve Plate ◽  
Test Results ◽  
Simulation Test ◽  
Screening Process ◽  
Vibration Direction ◽  
Screening Effect ◽  
Direction Angle ◽  
Movement Characteristics ◽  
Screening Efficiency ◽  
Dip Angle

Based on Hertz-Mindlin contact model in software EDEM by discrete element method, using linear vibrating screen 360 mm×600 mm, movement characteristics of particle group on sieve plate and law of particles going through sieve plate were studied in screening process of coal in certain conditions, which were as follows: dip angle was 0 °, amplitude was 5 mm, frequency was 11 Hz. The simulation test results show that there are important influences of vibration direction on screening process. And influence law of vibration direction on screening effect was revealed finally. The paper also gained mathematical model between particle group’s screening efficiency and vibration direction angle, and mathematical models of particles easy or hard to sieve and material between average movement speeds and vibration direction angle.

scholarly journals Performance Comparison of 2L-VSC, 3L-NPC, and 3L-MMC Converter Topologies for Interfacing Grid-Connected Systems

2021 ◽  
Author(s):  
Victor R. F. B. de Souza ◽  
Luciano S. Barros ◽  
Flavio B. Costa
Keyword(s):  
Reactive Power ◽  
Harmonic Distortion ◽  
Performance Comparison ◽  
Test Results ◽  
Multilevel Converter ◽  
Simulation Test ◽  
Comparative Performance ◽  
Higher Power ◽  
Converter Topologies ◽  
Level Converter

Nowadays, power converters play a fundamental role in the conditioning and processing of active and reactive power, and are directly related to power quality indexes. In this sense, new multi-level converter topologies have been integrated in order to provide higher power processing capacity with lower harmonic distortion, switch stress, heating, and losses. The use of these structures compared to conventional two-level converters is especially suitable for high power of the order of megawatt. Considering the relevance of this approach, this paper presents a comparative performance analysis among the conventional two-level topology (2L-VSC) and two multilevel topologies in a grid-connected system: neutral point clamped (NPC) and modular multilevel converter (MMC). Simulation test results present the impacts on voltages and currents for the switches and the whole system, as well as the evaluation of the total harmonic distortion (THD) in order to highlight the crucial points of each topology for this kind of application.

scholarly journals A New Feature Extraction Algorithm Based on Entropy Cloud Characteristics of Communication Signals

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Jingchao Li ◽  
Jian Guo
Keyword(s):  
Feature Extraction ◽  
Cloud Model ◽  
Recognition Rate ◽  
Signal Recognition ◽  
Communication Signals ◽  
Low Snr ◽  
Feature Extraction Algorithm ◽  
Cloud Characteristics ◽  
Extraction Algorithm ◽  
New Feature

Identifying communication signals under low SNR environment has become more difficult due to the increasingly complex communication environment. Most relevant literatures revolve around signal recognition under stable SNR, but not applicable under time-varying SNR environment. To solve this problem, we propose a new feature extraction method based on entropy cloud characteristics of communication modulation signals. The proposed algorithm extracts the Shannon entropy and index entropy characteristics of the signals first and then effectively combines the entropy theory and cloud model theory together. Compared with traditional feature extraction methods, instability distribution characteristics of the signals’ entropy characteristics can be further extracted from cloud model’s digital characteristics under low SNR environment by the proposed algorithm, which improves the signals’ recognition effects significantly. The results from the numerical simulations show that entropy cloud feature extraction algorithm can achieve better signal recognition effects, and even when the SNR is −11 dB, the signal recognition rate can still reach 100%.

A New Pedestrian Feature Description Method Named Neighborhood Descriptor of Oriented Gradients

2021 ◽  
Vol 16 (1) ◽  
pp. 23-55
Author(s):  
Qian Liu ◽  
Feng Yang ◽  
XiaoFen Tang
Keyword(s):  
Feature Vector ◽  
Pedestrian Detection ◽  
Weight Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Local Weight ◽  
Detection Approach ◽  
Training Samples ◽  
New Feature ◽  
Linear Svm

In view of the issue of the mechanism for enhancing the neighbourhood relationship of blocks of HOG, this paper proposes neighborhood descriptor of oriented gradients (NDOG), an improved feature descriptor based on HOG, for pedestrian detection. To obtain the NDOG feature vector, the algorithm calculates the local weight vector of the HOG feature descriptor, while integrating spatial correlation among blocks, concatenates this weight vector to the tail of the HOG feature descriptor, and uses the gradient norm to normalize this new feature vector. With the proposed NDOG feature vector along with a linear SVM classifier, this paper develops a complete pedestrian detection approach. Experimental results for the INRIA, Caltech-USA, and ETH pedestrian datasets show that the approach achieves a lower miss rate and a higher average precision compared with HOG and other advanced methods for pedestrian detection especially in the case of insufficient training samples.

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