A FEATURE BASED MOTION ESTIMATION FOR VEHICLE GUIDANCE

2006 ◽  
pp. 703-708
Author(s):  
Onkar Ambekar ◽  
Ernani Fernandes ◽  
Dieter Hoepfel
Keyword(s):  
Motion Estimation ◽  
Vehicle Guidance ◽  
Feature Based

scholarly journals Non-rigid image registration using a modified fuzzy feature-based inference system for 3D cardiac motion estimation

2021 ◽  
Vol 205 ◽  
pp. 106085
Author(s):  
Monire Sheikh Hosseini ◽  
Mahammad Hassan Moradi ◽  
Mahdi Tabassian ◽  
Jan D'hooge
Keyword(s):  
Image Registration ◽  
Motion Estimation ◽  
Cardiac Motion ◽  
Inference System ◽  
Feature Based

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.

Feature-Based Affine Motion Estimation for Superresolution of a Region of Interest

2013 ◽  
pp. 118-137
Author(s):  
Sung Hyun Kim ◽  
Rae-Hong Park ◽  
Seungjoon Yang ◽  
Hwa-Young Kim
Keyword(s):  
Motion Estimation ◽  
Convex Sets ◽  
Interpolation Method ◽  
Digital Camera ◽  
Region Of Interest ◽  
Noise Model ◽  
Reference Image ◽  
Multiple Images ◽  
Affine Motion ◽  
Feature Based

This chapter presents an interpolation method of low-computation for a Region Of Interest (ROI) using multiple low-resolution images of the same scene. Interpolation methods using multiple images require the accurate motion information between the reference image of interpolation and the other images. Sometimes complex local motions applied to the entire images are estimated incorrectly, yielding seriously degraded interpolation results. The authors apply the proposed Superresolution (SR) method, which employs a simple global motion model, only to the ROI that contains important information of the scene. The ROIs extracted from multiple images are assumed to have simple global motions. At first, using a mean absolute difference measure, they extract the regions from the multiple images that are similar to the selected ROI in the reference image of interpolation and use feature points to estimate the affine motion parameters. The authors apply the Projection Onto Convex Sets (POCS)-based method to the ROI using the estimated motion, simplify the iterative computation of the whole system, and use an edge-preserving smoothing filter to reduce the distortion caused by additive noise. In experiments, they acquire test image sets with a hand-held digital camera and use a Gaussian noise model. Experimental results show that the feature-based Motion Estimation (ME) is accurate and reducing the computational load of the ME step is efficient in terms of the computational complexity. It is also shown that the SR results using the proposed method are remarkable even when input images contain complex motions and a large amount of noise. The proposed POCS-based SR algorithm can be applied to digital cameras, portable camcorders, and so on.

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