Feature Point Tracking
Feature Point Tracking - Related Videos
07:21
Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
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Cited by 186 •
2011
An accurate and practical method to measure parameters like strain in myocardial tissue is of great clinical value, since it has been shown, that strain is a more sensitive and earlier marker for contractile dysfunction than the frequently used parameter EF.
08:04
Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)
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Cited by 6 •
2020
We describe detailed protocols for using FLLIT, a fully automated machine learning method for leg claw movement tracking in freely moving Drosophila melanogaster and other insects. These protocols can be used to quantitatively measure subtle walking gait movements in wild type flies, mutant flies and fly models of neurodegeneration.
05:42
Visual Search for Features and Conjunctions
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University How do people find objects in cluttered visual scenes? Think, for example, of looking for keys on a messy desk, finding the ripest-looking fruit at the grocery store, locating your car when you can’t quite remember where you parked it, or finding an old friend at an airport exit gate. Clearly, an understanding of visual perception is going to play a role in any answers, and more specifically, an understanding of visual attention...
15:48
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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Cited by 28 •
2014
We describe how to obtain parametric and vector maps of the diffusion tensor of the breast using magnetic resonance imaging. The protocol and final output following imaging processing are tailored for tracking breast architectural features and detecting breast malignancy.
07:36
C. elegans Tracking and Behavioral Measurement
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Cited by 14 •
2012
We have developed a video-rate tracking microscope system that can record and quantify C. elegans behavior at high resolution and high speeds. We have also developed computational methods to reduce the dimensionality of the worm images to a fundamental set of measurements that completely describe the shape of the worm.
FAQs
Reliable correspondence begins with image locations that remain distinctive across frames. Corners and textured regions provide stronger tracking targets because their local intensity patterns can be represented and compared. This selection helps preserve feature identity as objects or cameras move, enabling trajectories that are useful for downstream motion analysis.
Optical flow and descriptor similarity provide two ways to associate features between frames. Optical flow follows image motion, whereas descriptor similarity compares local feature representations. The choice connects observed image changes to trajectories, while the shared goal is maintaining correspondence during object or camera movement.
Rejecting inconsistent matches protects the trajectory from incorrect correspondences. A feature match that does not agree with the observed image change can distort the measured motion and weaken later analysis. Consistency checking therefore helps convert frame-to-frame associations into more trustworthy trajectories for engineering tasks such as motion assessment and stabilization.
Tracking quality depends strongly on whether sequential frames contain distinctive corners or textured regions that can be represented consistently. The motion of the object or camera also affects the observed image changes and the resulting correspondences. When these image locations remain identifiable, the method can produce clearer trajectories for noncontact motion analysis.
A practical workflow starts by locating salient corners or textured regions in the image sequence. The selected points are represented through local intensity patterns or descriptors, then associated across frames using optical flow or descriptor similarity. Inconsistent associations are rejected before the remaining correspondences are converted into trajectories for motion measurement.
In visual odometry and simultaneous localization and mapping, tracked image correspondences provide trajectories that describe how visual features change across sequential frames. Those trajectories supply motion-related information without requiring direct contact with the observed system. This makes the technique useful when engineering designs need camera-based motion information for navigation or spatial analysis.
For structural deformation measurement, feature trajectories reveal image changes across a structure without requiring direct sensors at every measurement location. In video stabilization, the same trajectory information helps characterize unwanted camera motion so image sequences can be steadied. These applications extend the method from point matching to practical noncontact assessment of system behavior.