scholarly journals Development of Speed Up Robust Feature Algorithm for aerial image feature extraction

2019 ◽  
Vol 11 (4) ◽  
pp. 49-60
Author(s):  
Saikat BANERJEE ◽  
Sudhir Kumar CHATURVEDI ◽  
Surya Prakash TIWARI
Keyword(s):  
Feature Extraction ◽  
Aerial Imagery ◽  
Image Feature ◽  
Aerial Image ◽  
Feature Points ◽  
Image Feature Extraction ◽  
Surface Models ◽  
Speed Up ◽  
Fine Structures ◽  
Speed Up Robust Feature

Speed Up Robust Feature Algorithm (SURF) has been a very useful technique in the advancement of image feature algorithm. The strategy offers an extremely decent agreement between the runtime and accuracy, especially at object borders and fine structures. It has a wide scope of applications in remote sensing like getting computerized surface models from UAV and satellite images. In this paper, SURF algorithm has been discussed in details to enhance the capability of the system for image feature extraction technique to detect and obtain the maximum feature points from aerial imagery. The algorithms are developed depending upon such phenomena in which a maximum result can be obtained in very less time.

Study of Fingerprint Image Feature Extraction Algorithm

2014 ◽  
Vol 519-520 ◽  
pp. 577-580
Author(s):  
Shuai Yuan ◽  
Guo Yun Zhang ◽  
Jian Hui Wu ◽  
Long Yuan Guo
Keyword(s):  
Feature Extraction ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Image Feature ◽  
Feature Points ◽  
Orientation Field ◽  
Fingerprint Image ◽  
Image Feature Extraction ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm

Fingerprint image feature extraction is a critical step to fingerprint recognition system, which studies topological structure, mathematical model and extraction algorithm of fingerprint feature. This paper presents system design and realization of feature extraction algorithm for fingerprint image. On the basis of fingerprint skeleton image, feature points including ending points, bifurcation points and singular points are extracted at first. Then false feature points are detected and eliminated by the violent changes of ambient orientation field. True feature points are marked at last. Test result shows that the method presented has good accuracy, quick speed and strong robustness for realtime application.

scholarly journals Writer Identification Based on Arabic Handwriting Recognition by using Speed Up Robust Feature and K- Nearest Neighbor Classification

2019 ◽  
Vol 27 (1) ◽  
pp. 1-10
Author(s):  
Alia Karim Abdul Hassan ◽  
Bashar Saadoon Mahdi ◽  
Asmaa Abdullah Mohammed
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Image Feature ◽  
Writer Identification ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Speed Up ◽  
Speed Up Robust Feature ◽  
Neighbor Classification

In a writer recognition system, the system performs a “one-to-many” search in a large database with handwriting samples of known authors and returns a possible candidate list. This paper proposes method for writer identification handwritten Arabic word without segmentation to sub letters based on feature extraction speed up robust feature transform (SURF) and K nearest neighbor classification (KNN) to enhance the writer's  identification accuracy. After feature extraction, it can be cluster by K-means algorithm to standardize the number of features. The feature extraction and feature clustering called to gather Bag of Word (BOW); it converts arbitrary number of image feature to uniform length feature vector. The proposed method experimented using (IFN/ENIT) database. The recognition rate of experiment result is (96.666).

Image feature extraction algorithm in big data environment

2020 ◽  
Vol 39 (4) ◽  
pp. 5109-5118
Author(s):  
Yubao Zhang
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Image Matching ◽  
Image Database ◽  
Scale Space ◽  
Image Feature ◽  
Aerial Image ◽  
Image Feature Extraction ◽  
Matching Algorithm ◽  
Improved Algorithm

The purpose of this article is to explore effective image feature extraction algorithms in the context of big data, and to mine their potential information from complex image data. Based on the BRISK and SIFT algorithms, this paper proposes an image feature extraction and matching algorithm based on BRISK corner points. By combining the SIFT scale space and the BRISK algorithm, a new scale space construction method is proposed. The BRISK algorithm extracts the corner invariant features. Then, by using the improved feature matching method and eliminating the mismatching algorithm, the exact matching of the images is realized. A large number of experimental verifications were performed in the standard test Mikolajczyk image database and aerial image database. The experimental results show that the improved algorithm in this paper is an effective image matching algorithm. The highest accuracy of actual aerial image matching can reach 85.19%, and it can realize the actual aerial image matching that BRISK and SIFT algorithms cannot complete. The improved algorithm in this paper has the advantages of higher matching accuracy and strong robustness.

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