scholarly journals An Image Similarity Acceleration Detection Algorithm Based on Sparse Coding

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Luan Xidao ◽  
Xie Yuxiang ◽  
Zhang Lili ◽  
Zhang Xin ◽  
Li Chen ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Feature Detection ◽  
Feature Vector ◽  
Detection Efficiency ◽  
Detection Algorithm ◽  
Local Feature ◽  
Image Similarity ◽  
Sift Feature ◽  
Similarity Detection ◽  
Overcomplete Dictionary

Aiming at the problem that the image similarity detection efficiency is low based on local feature, an algorithm called ScSIFT for image similarity acceleration detection based on sparse coding is proposed. The algorithm improves the image similarity matching speed by sparse coding and indexing the extracted local features. Firstly, the SIFT feature of the image is extracted as a training sample to complete the overcomplete dictionary, and a set of overcomplete bases is obtained. The SIFT feature vector of the image is sparse-coded with the overcomplete dictionary, and the sparse feature vector is used to build an index. The image similarity detection result is obtained by comparing the sparse coefficients. The experimental results show that the proposed algorithm can significantly improve the detection speed compared with the traditional algorithm based on local feature detection under the premise of guaranteeing the accuracy of algorithm detection.

OPTIMIZED QUANTITATIVE BILATERAL SYMMETRY DETECTION

2005 ◽  
Vol 02 (03) ◽  
pp. 241-249 ◽  
Author(s):  
KAI HUEBNER ◽  
DANIEL WESTHOFF ◽  
JIANWEI ZHANG
Keyword(s):  
Dynamic Programming ◽  
Motion Tracking ◽  
Feature Detection ◽  
Feature Vector ◽  
Bilateral Symmetry ◽  
Detection Algorithm ◽  
Symmetry Detection ◽  
Invariant Features ◽  
Novel Method ◽  
Novel Algorithm

In [Westhoff et al., 2005], we proposed a novel method to determine illumination-invariant features in images. The quantitative bilateral symmetry of a given scene is computed using dynamic programming before applying the resulting symmetry image and feature vector signatures to an omnidirectional motion tracking task. Compared to other approaches to symmetry detection in computer vision, the results of our novel algorithm describe symmetry in terms of an absolute region instead of a relative degree. In this paper, we describe further optimizations and details of the proposed Dynamic Programming Symmetry feature detection algorithm (DPS algorithm).

scholarly journals Efficient feature-based motion estimation in neurosurgery using non-maximum suppression

2018 ◽  
Vol 4 (1) ◽  
pp. 555-558 ◽  
Author(s):  
Fang Chen ◽  
Jan Müller ◽  
Jens Müller ◽  
Ronald Tetzlaff
Keyword(s):  
Motion Estimation ◽  
Thermal Imaging ◽  
Motion Correction ◽  
Processing Time ◽  
Feature Detection ◽  
Detection Efficiency ◽  
Adaptive Method ◽  
Time Performance ◽  
Key Points ◽  
Feature Based

AbstractIn this contribution we propose a feature-based method for motion estimation and correction in intraoperative thermal imaging during brain surgery. The motion is estimated from co-registered white-light images in order to perform a robust motion correction on the thermographic data. To ensure real-time performance of an intraoperative application, we optimise the processing time which essentially depends on the number of key points found by our algorithm. For this purpose we evaluate the effect of applying an non-maximum suppression (NMS) to improve the feature detection efficiency. Furthermore we propose an adaptive method to determine the size of the suppression area, resulting in a trade-off between accuracy and processing time.

scholarly journals Improving Lidar Windshear Detection Efficiency by Removal of “Gentle Ramps”

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1539
Author(s):  
Kai Kwong Hon ◽  
Pak Wai Chan
Keyword(s):  
Detection Efficiency ◽  
State Of The Art ◽  
Detection Algorithm ◽  
Doppler Lidar ◽  
Hit Rate ◽  
Glide Path ◽  
The World ◽  
Alerting System ◽  
The Difference ◽  
Hong Kong International Airport

The Doppler Lidar windshear alerting system at the Hong Kong International Airport (HKIA), the first of its kind in the world, has been in operation since 2006. This paper reports on an enhancement to the automatic windshear detection algorithm at HKIA, which aims at filtering out alerts associated with smoother headwind changes spread over longer distances along the aircraft glide path (called “gentle ramps”) which may nonetheless exceed the well-established alerting threshold. Real-time statistics are examined over a 46-month study period between March 2016 and December 2019, covering a total of 2,017,440 min and over 1500 quality-controlled pilot reports of windshear (PIREP). The “gentle ramp removal” (GRR) function is able to effectively cut down the alert duration over the 5 major runway corridors, inclusive of both landing and take-off, which together account for over 98% of the PIREP received at HKIA during the study period. In all 5 runway corridors this is achieved with a proportionately smaller decrease—even with no changes in 2 cases—in the hit rate, highlighting the efficiency of the GRR function. The difference in statistical behaviour across the runway corridors also echo literature findings about the differences in length scale of wind disturbances at different locations within HKIA. This study serves as a unique documentation of the state-of-the-art in operational Lidar windshear detection and can provide useful reference to airports and aviation meteorologists around the world.

scholarly journals A Novel Feature Detection Algorithm Based on Improved 2DPCA- SIFT

2015 ◽  
Vol 8 (6) ◽  
pp. 299-304
Author(s):  
Aili Wang ◽  
Yangyang Zhao ◽  
Jiaying Zhao ◽  
Yuji Iwahori ◽  
Xinyuan Wang
Keyword(s):  
Feature Detection ◽  
Detection Algorithm

scholarly journals Natural Feature Detection on Mobile Phones with 3D FAST

2010 ◽  
Vol 9 (4) ◽  
pp. 29-34 ◽  
Author(s):  
Achim Weimert ◽  
Xueting Tan ◽  
Xubo Yang
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
Hessian Matrix ◽  
Detection Algorithm ◽  
Scale Invariant ◽  
Natural Feature ◽  
Detection Approach ◽  
Scale Levels ◽  
Speed Up ◽  
Low Performance

In this paper, we present a novel feature detection approach designed for mobile devices, showing optimized solutions for both detection and description. It is based on FAST (Features from Accelerated Segment Test) and named 3D FAST. Being robust, scale-invariant and easy to compute, it is a candidate for augmented reality (AR) applications running on low performance platforms. Using simple calculations and machine learning, FAST is a feature detection algorithm known to be efficient but not very robust in addition to its lack of scale information. Our approach relies on gradient images calculated for different scale levels on which a modified9 FAST algorithm operates to obtain the values of the corner response function. We combine the detection with an adapted version of SURF (Speed Up Robust Features) descriptors, providing a system with all means to implement feature matching and object detection. Experimental evaluation on a Symbian OS device using a standard image set and comparison with SURF using Hessian matrix-based detector is included in this paper, showing improvements in speed (compared to SURF) and robustness (compared to FAST)

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