A Demodulation Algorithm for Periodically In-Plane Vibrating MEMS Based on a Stroboscopic Micro-Visual System

2021 ◽  
pp. 1-7
Author(s):  
Minhui Yu ◽  
Mei Sang ◽  
Cheng Guo ◽  
Ruifeng Zhang ◽  
Fan Zhao ◽  
...  
Keyword(s):  
Visual System ◽  
Template Matching ◽  
Micro Electro Mechanical System ◽  
Plane Motion ◽  
Image Sequences ◽  
Mems Devices ◽  
Point Matching ◽  
Speeded Up Robust Features ◽  
Frame Difference ◽  
Feature Point Matching

Abstract A high-frequency short-pulsed stroboscopic micro-visual system was employed to capture the transient image sequences of a periodically in-plane working micro-electro-mechanical system (MEMS) devices. To demodulate the motion parameters of the devices from the images, we developed the feature point matching (FPM) algorithm based on Speeded-Up Robust Features (SURF). A MEMS gyroscope, vibrating at a frequency of 8.189 kHz, was used as a testing sample to evaluate the performance of the proposed algorithm. Within the same processing time, the SURF-based FPM method demodulated the velocity of the in-plane motion with a precision of 10−5 pixels of the image, which was two orders of magnitude higher than the template-matching and frame-difference algorithms.

Feature point matching in image sequences

1988 ◽  
Vol 7 (2) ◽  
pp. 113-121 ◽  
Author(s):  
I.K. Sethi ◽  
V. Salari ◽  
S. Vemuri
Keyword(s):  
Image Sequences ◽  
Feature Point ◽  
Point Matching ◽  
Feature Point Matching

Unusual Event Detection using Mean Feature Point Matching Algorithm

2016 ◽  
Vol 6 (4) ◽  
pp. 1595 ◽  
Author(s):  
Chitra Hegde ◽  
Shakti Singh Chundawat ◽  
Divya S N
Keyword(s):  
Input Image ◽  
Feature Point ◽  
Experimental Result ◽  
Feature Points ◽  
Surveillance Video ◽  
Point Matching ◽  
Public And Private ◽  
Unusual Event ◽  
Speeded Up Robust Features ◽  
Feature Point Matching

Analysis and detection of unusual events in public and private surveillance system is a complex task. Detecting unusual events in surveillance video requires the appropriate definition of similarity between events. The key goal of the proposed system is to detect behaviours or actions that can be considered as anomalies. Since suspicious events differ from domain to domain, it remains a challenge to detect those events in major domains such as airport, super malls, educational institutions etc. The proposed Mean Feature Point Matching (MFPM) algorithm is used for detecting unusual events. The Speeded-Up Robust Features (SURF) method is used for feature extraction. The MFPM algorithm compares the feature points of the input image with the mean feature points of trained dataset. The experimental result shows that the proposed system is efficient and accurate for wide variety of surveillance videos.

scholarly journals Unusual Event Detection using Mean Feature Point Matching Algorithm

2016 ◽  
Vol 6 (4) ◽  
pp. 1595
Author(s):  
Chitra Hegde ◽  
Shakti Singh Chundawat ◽  
Divya S N
Keyword(s):  
Input Image ◽  
Feature Point ◽  
Experimental Result ◽  
Feature Points ◽  
Surveillance Video ◽  
Point Matching ◽  
Public And Private ◽  
Unusual Event ◽  
Speeded Up Robust Features ◽  
Feature Point Matching

Analysis and detection of unusual events in public and private surveillance system is a complex task. Detecting unusual events in surveillance video requires the appropriate definition of similarity between events. The key goal of the proposed system is to detect behaviours or actions that can be considered as anomalies. Since suspicious events differ from domain to domain, it remains a challenge to detect those events in major domains such as airport, super malls, educational institutions etc. The proposed Mean Feature Point Matching (MFPM) algorithm is used for detecting unusual events. The Speeded-Up Robust Features (SURF) method is used for feature extraction. The MFPM algorithm compares the feature points of the input image with the mean feature points of trained dataset. The experimental result shows that the proposed system is efficient and accurate for wide variety of surveillance videos.

Analysis of Fabric Shape Style Evaluation Method

2014 ◽  
Vol 1048 ◽  
pp. 173-177 ◽  
Author(s):  
Ying Mei Wang ◽  
Yan Mei Li ◽  
Wan Yue Hu
Keyword(s):  
Image Processing ◽  
High Speed ◽  
Evaluation Method ◽  
Contact Method ◽  
Ccd Cameras ◽  
Point Matching ◽  
Feature Point Matching ◽  
Motion Parameters ◽  
Measurement And Evaluation ◽  
Main Factor

Fabric shape style is one of the most important conditions in textile appearance evaluation, and also the main factor influences customer purchasing psychology. At first, the previous fabric shape style evaluation methods are classified and summarized, measurement and evaluation method discussed from tactic and dynamic aspects. Then, companied with computer vision principle, a non-contact method for measuring fabric shape style was put forward. In this method, two high-speed CCD cameras were used to capture fabric movement dynamically, fabric sequences image were obtained in this process. Used the image processing technology include pretreatment and feature point matching to get 3D motion parameters, it can provide data supports for shape style evaluation.

scholarly journals Scene Wireframes Sketching for Drones

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1193
Author(s):  
Roi Santos ◽  
Xose Pardo ◽  
Xose Fdez-Vidal
Keyword(s):  
State Of The Art ◽  
Point Clouds ◽  
Outdoor Environment ◽  
Point Matching ◽  
3D Point Clouds ◽  
Complementary Approach ◽  
Straight Line ◽  
Scene Representation ◽  
Feature Point Matching ◽  
3D Scene

The increasing use of autonomous UAVs inside buildings and around human-made structures demands new accurate and comprehensive representation of their operation environments. Most of the 3D scene abstraction methods use invariant feature point matching, nevertheless some sparse 3D point clouds do not concisely represent the structure of the environment. Likewise, line clouds constructed by short and redundant segments with inaccurate directions limit the understanding of scenes as those that include environments with poor texture, or whose texture resembles a repetitive pattern. The presented approach is based on observation and representation models using the straight line segments, whose resemble the limits of an urban indoor or outdoor environment. The goal of the work is to get a full method based on the matching of lines that provides a complementary approach to state-of-the-art methods when facing 3D scene representation of poor texture environments for future autonomous UAV.

Unsupervised multi-spectral image feature point matching under different lighting scenes

2021 ◽  
Author(s):  
Junchong Huang ◽  
Wei Tian ◽  
Yongkun Wen ◽  
Zhan Chen ◽  
Yuyao Huang
Keyword(s):  
Feature Point ◽  
Image Feature ◽  
Spectral Image ◽  
Point Matching ◽  
Feature Point Matching
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