Automated two-dimensional position measurements with computer vision

Bach, Roger; Trantham, Kenneth

doi:10.1119/1.2348892

Cited by 10 publications

(13 citation statements)

References 8 publications

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“…Despite the accuracy found in their works, the medical instrument has limited movement based on the reference used. [16] and [17] are good research studies on three-dimensional tracking objects in a stereo video sequence. There is also extensive literature on [9] and [15] to solve the correspondence problem in stereovision camera for the navigation systems.…”

Section: Related Workmentioning

confidence: 99%

Medical Navigation Based on Coloured Markers for Image-guided Surgery

Elsamnah

Sediono

2014

Procedia Computer Science

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Image-guided Surgery (IGS) is an advanced medical navigation technology that enhances surgical operations and outcomes. The infrared markers have been used in previous years as references to visualize a patient's body part, whether the markers are active or passive. This paper presents a new reference tool of tracking, and develops a new algorithm of tracking an object in real time. Three coloured markers have been used as references of visualizing a patient's body part by providing many advantages, such as an unbounded objects tracking, an inexpensive system compared to the IR-markers system, and an unrestricted working area. This paper shows that using a coloured marker as a reference, a good accuracy of visualization (RMS error 3.78 mm) at the distance of 1000 to 1300 mm can be generated. Image filtering techniques have been used to process the images captured from a stereo camera. As a result, medical navigation based on coloured markers contributes a promising result of Image-Guided Surgery. Moreover, a faster algorithm for a real time tracking of coloured markers has been developed.

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Section: Related Workmentioning

confidence: 99%

Medical Navigation Based on Coloured Markers for Image-guided Surgery

Elsamnah

Sediono

2014

Procedia Computer Science

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“…In an earlier work, Bach and Trantham used color discrimination for automating object tracking [22]. This was a welcome improvement since it obviated the need for human intervention at the scale of individual frames and instead let the computer perform the tracking.…”

Section: Color Discriminationmentioning

confidence: 99%

Video motion analysis with automated tracking: an insight

Usman¹,

Alam²,

Anwar³

2015

Eur. J. Phys.

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The article describes the use of elementary techniques in computer vision and motion photography for the analysis of well known experiments in interactive instructional physics laboratories. We describe a method for the automated tracking of the kinematics of physical objects which involves the subtraction of orthogonal colors in color space. The aim is to expose undergraduate students to image processing and its applications in video motion analysis. The straightforward technique is simple, results in computational speedup compared to an existing method, removes the need for a laborious repetitive and manual tagging of frames and is generally robust against color variations. Insight is also presented into the process of thresholding and selecting the correct region out of the several choice presented in the post-threshold frames. Finally, the approach is illustrated through a selection of well known mechanics experiments.

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“…Various methods exist for automated tracking of reference points on moving objects in a video clip: for example, they can be based on image recognition techniques and geometric properties of the objects of interest, 31,36 or they can be based on color discrimination. 37 The second method of point tracking has been implemented in coach as a transformation filter, which can be thought of as a program that connects a source, e.g., a video clip or a streaming video of a webcam, with the video tool window, while at the same time passing the recorded position data to the computer application. The algorithm used in the tracking filter is composed of two parts: (1) finding the best match of a given model template in a subsequent frame, i.e., locating the area that resembles most the specified area; and (2) limiting the search area in order to reduce computing time or to avoid ambiguity.…”

Section: Point Tracking In Video Clips and Image Sequencesmentioning

confidence: 99%

Giving students the run of sprinting models

Heck

Ellermeijer

2009

American Journal of Physics

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A biomechanical study of sprinting is an interesting and attractive practical investigation task for undergraduates and pre-university students who have already done a fair amount of mechanics and calculus. These students can work with authentic real data about sprinting and carry out practical investigations similar to the way sports scientists do research. Student research activities are viable when the students are familiar with tools to collect and work with data from sensors and video recordings, and with modeling tools for comparison of simulation results with experimental results. This article presents a multipurpose system, named coach, which is rather unique in the sense that it offers students and teachers a versatile integrated set of tools for learning, doing, and teaching mathematics and science in a computer-based inquiry approach. Automated tracking of reference points and correction of perspective distortion in video clips, state-of-the-art algorithms of data smoothing and numerical differentiation, and graphical system dynamics based modeling, are some of the built-in techniques that are very suitable for motion analysis. Their implementation in coach and their application in student activities involving mathematical models of running are discussed.

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Automated two-dimensional position measurements with computer vision

Cited by 10 publications

References 8 publications

Medical Navigation Based on Coloured Markers for Image-guided Surgery

Medical Navigation Based on Coloured Markers for Image-guided Surgery

Video motion analysis with automated tracking: an insight

Giving students the run of sprinting models

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