SIFT matching method based on K nearest neighbor support feature points

2016 ◽  
Author(s):  
An Yong ◽  
Zheng Hong
Keyword(s):  
Nearest Neighbor ◽  
Feature Points ◽  
K Nearest Neighbor ◽  
Matching Method

Implementation of Verification and Matching E-KTP with Faster R-CNN and ORB

2021 ◽  
Vol 5 (4) ◽  
pp. 783-793
Author(s):  
Muhammad Muttabi Hudaya ◽  
Siti Saadah ◽  
Hendy Irawan
Keyword(s):  
Image Matching ◽  
Nearest Neighbor ◽  
Feature Matching ◽  
Image Feature ◽  
Brute Force ◽  
K Nearest Neighbor ◽  
Matching Method ◽  
Average Precision ◽  
Detection Model ◽  
Matching Process

needs a solid validation that has verification and matching uploaded images. To solve this problem, this paper implementing a detection model using Faster R-CNN and a matching method using ORB (Oriented FAST and Rotated BRIEF) and KNN-BFM (K-Nearest Neighbor Brute Force Matcher). The goal of the implementations is to reach both an 80% mark of accuracy and prove matching using ORB only can be a replaced OCR technique. The implementation accuracy results in the detection model reach mAP (Mean Average Precision) of 94%. But, the matching process only achieves an accuracy of 43,46%. The matching process using only image feature matching underperforms the previous OCR technique but improves processing time from 4510ms to 60m). Image matching accuracy has proven to increase by using a high-quality dan high quantity dataset, extracting features on the important area of EKTP card images.

scholarly journals An Efficient Policy User Web Page Correspondence Searching for Support Query Rewriting

2019 ◽  
Vol 8 (12) ◽  
pp. 5472-5474
Keyword(s):  
Nearest Neighbor ◽  
Natural World ◽  
Query Rewriting ◽  
Second Phase ◽  
K Nearest Neighbor ◽  
Matching Method ◽  
Database Queries ◽  
Different Types ◽  
Nearest Neighbor Queries ◽  
Marketing Approach

Nowadays, Social media positions remain a favorite combination as clients studying to distribute their occurrences, activities on the network. These websites receive large quantities of user-supplied elements during the vast difference natural world results of various varieties, reach. An effective advertisement marketing approach is designed by using the products that contain the information, additionally client inclinations and conclusions on information about products. By this kind of similarity between concepts of profile, a precise matching method is developed to match the profile of the Web services and user. In this work, the approach is developed to search the concept similarity in the second phase of the process of query rewriting is performed after extraction queries. Similarity measure techniques are very much useful in processing database queries such as top-k queries, reverse top-k queries, k-nearest neighbor queries and other different types of queries related to trading sales activities

Automatic Measurement of Traffic State Parameters Based on Computer Vision for Intelligent Transportation Surveillance

2017 ◽  
Vol 32 (04) ◽  
pp. 1855003 ◽  
Author(s):  
Jianqiang Ren ◽  
Chunhong Zhang ◽  
Lingjuan Zhang ◽  
Ning Wang ◽  
Yue Feng
Keyword(s):  
Computer Vision ◽  
Traffic Congestion ◽  
Nearest Neighbor ◽  
Automatic Measurement ◽  
Intelligent Transportation ◽  
Vehicle Classification ◽  
Feature Points ◽  
K Nearest Neighbor ◽  
Spatial Image ◽  
Traffic State

Online automatic measurement of traffic state parameters has important significance for intelligent transportation surveillance. The video-based monitoring technology is widely studied today but the existing methods are not satisfactory at processing speed or accuracy, especially for traffic scenes with traffic congestion or complex road environments. Based on technologies of computer vision and pattern recognition, this paper proposes a novel measurement method that can detect multiple parameters of traffic flow and identify vehicle types from video sequence rapidly and accurately by combining feature points detection with foreground temporal-spatial image (FTSI) analysis. In this method, two virtual detection lines (VDLs) are first set in frame images. During working, vehicular feature points are extracted via the upstream-VDL and grouped in unit of vehicle based on their movement differences. Then, FTSI is accumulated from video frames via the downstream-VDL, and adhesive blobs of occlusion vehicles in FTSI are separated effectively based on feature point groups and projection histogram of blob pixels. At regular intervals, traffic parameters are calculated via statistical analysis of blobs and vehicles are classified via a K-nearest neighbor (KNN) classifier based on geometrical characteristics of their blobs. For vehicle classification, the distorted blobs of temporary stopped vehicles are corrected accurately based on the vehicular instantaneous speed on the downstream-VDL. Experiments show that the proposed method is efficient and practicable.

scholarly journals IMAGE MOSAIC ALGORITHM BASED ON PCA-ORB FEATURE MATCHING

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 83-89
Author(s):  
J. T. Zhu ◽  
C. F. Gong ◽  
M. X. Zhao ◽  
L. Wang ◽  
Y. Luo
Keyword(s):  
Principal Component Analysis ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Descriptor ◽  
Image Stitching ◽  
Feature Points ◽  
K Nearest Neighbor ◽  
Fusion Algorithm

Abstract. In the process of image stitching, the ORB (Oriented FAST and Rotated BRIEF) algorithm lacks the characteristics of scale invariance and high mismatch rate. A principal component invariant feature transform (PCA-ORB, Principal Component Analysis- Oriented) is proposed. FAST and Rotated BRIEF) image stitching method. Firstly, the ORB algorithm is used to optimize the feature points to obtain the feature points with uniform distribution. Secondly, the principal component analysis (PCA) method can reduce the dimension of the traditional ORB feature descriptor and reduce the complexity of the feature point descriptor data. Thirdly, KNN (K-Nearest Neighbor) is used, and the k-nearest neighbor algorithm performs roughly matching on the feature points after dimensionality reduction. Then the random matching consistency algorithm (RANSAC, Random Sample Consensus) is used to remove the mismatched points. Finally, the fading and fading fusion algorithm is used to fuse the images. In 8 sets of simulation experiments, the image stitching speed is improved relative to the PCA-SIFT algorithm. The experimental results show that the proposed algorithm improves the image stitching speed under the premise of ensuring the quality of stitching, and can play a role in fast, real-time and large-scale applications, which are conducive to image fusion.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

2020 ◽  
Vol 17 (1) ◽  
pp. 319-328
Author(s):  
Ade Muchlis Maulana Anwar ◽  
Prihastuti Harsani ◽  
Aries Maesya
Keyword(s):  
Nearest Neighbor ◽  
Information Gain ◽  
Birth Certificate ◽  
Population Data ◽  
Community Services ◽  
Birth Certificates ◽  
Similar Data ◽  
K Nearest Neighbor ◽  
Civil Registration ◽  
The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

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