Motion estimation of sequence image based on feature extraction of extended objects

2017 ◽  
Author(s):  
Yazhi Liu ◽  
Xinyang Li
Keyword(s):  
Feature Extraction ◽  
Motion Estimation

Real-Time and Robust Stereo Visual Navigation Localization Algorithm Based on ORB

2012 ◽  
Vol 241-244 ◽  
pp. 478-482 ◽  
Author(s):  
Yun Xi Xu ◽  
Fang Chen
Keyword(s):  
Feature Extraction ◽  
Motion Estimation ◽  
Real Time ◽  
Estimation Method ◽  
Visual Navigation ◽  
Localization Algorithm ◽  
Scale Invariant ◽  
Nonlinear Motion ◽  
Refinement Method ◽  
Matching Strategy

The biggest challenge of visual navigation localization is feature extraction and association. Currently, the most widely used method is simple corner feature and simple matching strategy based on SAD or NCC. Another option is scale invariant feature and rotation invariant descriptor, typically as SIFT, SURF. Feature extraction and matching methods based on the SIFT or SURF are accurate and robust. However, its computational complexity is too high and not suitable for the real-time navigation localization task. This paper presents a new fast, accurate, robust stereo vision navigation localization method, based on a new developed ORB feature and descriptor. First, we presented our matching method based on ORB. Then, we obtained matching inliers and an initial motion estimation parameters using RANSAC and three points motion estimation method. Finally, nonlinear motion refinement method was used to polish the solution. Experimental results show that our method is robust, accurate and real-time.

Fast motion estimation using feature extraction and XOR operations

1998 ◽  
Author(s):  
Thinh M. Le ◽  
W. M. Snelgrove ◽  
Sethuraman Panchanathan
Keyword(s):  
Feature Extraction ◽  
Motion Estimation ◽  
Fast Motion Estimation ◽  
Fast Motion

STEREO VISION-BASED ESTIMATION OF POSE AND MOTION FOR AUTONOMOUS LANDING OF AN UNMANNED HELICOPTER

2006 ◽  
Vol 03 (03) ◽  
pp. 181-190 ◽  
Author(s):  
CUI XU ◽  
MING LIU ◽  
BIN KONG ◽  
YUNJIAN GE
Keyword(s):  
Feature Extraction ◽  
Motion Estimation ◽  
Stereo Vision ◽  
Physical Simulation ◽  
Corner Detection ◽  
Unmanned Helicopter ◽  
Feature Points Matching ◽  
Extraction Algorithm ◽  
Estimation System ◽  
Vision Algorithm

In this paper, a real-time stereo vision based pose and motion estimation system is presented. It is used for landing an unmanned helicopter on a moving target such as a ship deck. The vision algorithm mainly consists of a feature extraction task and a pose and motion estimation task. By the specially designed pattern of the landing target, the feature extraction algorithm can simplify the step of feature points matching of stereo system. In the task of feature extraction, the step of accurate corner detection can get to the precision of sub-pixel, which helps improve the measurement precision in state estimation. We present results from semi-physical simulation which show that our vision algorithm is accurate and robust to allow our vision sensor to be placed in the control loop of unmanned helicopter management system.

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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