Camera System for Movement Analysis

Základní informace

Název práce: Camera System for Movement Analysis

Typ práce: Diplomová práce

Rozsah práce: 143 stran

Jazyk práce: Angličtina

Autor práce: Bc. Ondřej Rozinek

Datum obhajoby: 2009

Hodnocení od vedoucího práce: Výborně

Hodnocení od oponenta práce: Výborně

Hodnocení od odborné komise: Výborně

Stažení práce

Získejte práci prostřednictvím internetového bankovnictví a dopřejte autorovi co nejvyšší podíl ze zisku za jeho práci. Zašlete 400 Kč na č.ú. 1041664027 / 3030, do pole variabilní symbol uveďte číslo 0252 pro odlišení zvolené práce a do textového pole (např. do pole „Popis příkazce“) napište Vaší emailovou adresu. Práce Vám bude zaslána do 24 hodin od doručení požadované částky.

Souhrn

The goal of this work is to create an application for 3D motion analysis by using two cameras in the MATLAB software environment. Two synchronized video sequences are obtained from the two cameras with high definition (full HD) resolution, in which anatomically significant points have been labeled by blu markers. These video sequences are used for off-line processing by the means of computer vision, imag processing and pattern recognition. The application consists of a modular system, which contains color calibration, camera calibration, marker detection, stereo calibration, 3D reconstruction and motion analysis. Color calibration transforms (using multiple linear regression, linear and Hermite interpolating) the uncalibrated image to the RGB standard. Marker detection detects the coordinates of the markers using methods such as region prediction, multispectral thresholding, iterative thresholding, minimum error thresholding, Hough transform, unsupervised learning with Expectation-Maximization algorithm and K-means. Camera calibration, stereo calibration and 3D reconstruction result in obtaining the intrinsic and extrinsic camera parameters for the projection from 2D to 3D space. Motion analysis provides the basic characteristics of motion.

The goal of this work is to use MATLAB to create a functional application that can analyze 3D motion with the help of two cameras. Complete systems for 3D motion analysis, such as those produced by the firms Vicon and Qualisys, are expensive and use infrared cameras (up to 10) with high resolutions (up to 16 Megapixels) and reflexive markers. The camera system developed here uses two ordinary cameras with high definition (full HD) resolution. The video sequences from the cameras are used for off-line processing utilizing approaches of computer vison, image processing and pattern recognition. This system is sufficient for analyzing motion in clinical, and is specifically able to analyze hand motion of patients suffering from Parkinson’s disease.

Parkinson's disease (PD) is a progressive degenerative disorder of the central nervous system that often impairs the sufferer's motor skills and causes specific disturbances of hand motor function. Tremors, rigidity, poor balance and difficulty walking are characteristic primary symptoms of PD. Lesions in different structures of the central and peripheral nervous systems cause specific disturbances of hand function in the resting position (e.g. resting tremor and dystonia in PD), when movement is initiated (PD), in reaching a target (cerebellar disturbance), etc. It can be assumed that different lesions would also influence the various phases of a manual transport movement such as: forming a grip on an object, establishing the grip, lifting the object, the transport phases, and placing the object on the target point. A method based on a simple manual transport act could therefore be useful for an objective description and quantification of certain hand movement disturbances. The successive steps for motion analysis are suggested in the flowchart in Fig. 1.2. and the proposed modular camera system is shown in Fig. 1.3:. The parts of the camera system are described in the following chapters. In the next chapter the state of the art of similar camera systems and their approaches are discussed. In chapter 3 image preprocessing consisting of deinterlacing methods, motion blurring and color calibration is introduced. Basic deinterlacing methods for preprocessing videos and use of deinterlacing methods as well as results are discussed in regards to the camera system. Color calibration, including used methods, experiments and results, is described in its own subchapter. The parts of fundamental theory and results for the camera calibration, stereo calibration, and 3D reconstruction are mentioned in chapters 4 and 6 with more references for future study. Marker detection is introduced in chapter 5, which describes the methods used for marker segmentation. Many algorithms based on different approaches are tested and subjected to experiment. Algorithms such as multispectral thresholding, Hough transform, Expectation-Maximization (EM) algorithm and K-means are compared. K-means algorithm is described as a relation to the EM algorithm. Other techniques are discussed, which can be used for applications such as iterative thresholding, minimum error thresholding based on the Gaussian approximation of an image histogram and so on. In chapter 7 basic characterization of motion is discussed. The conclusion in chapter 8 contains a summarization of the implemented algorithms, achieved experiments and results. In the appendix it is possible to see some interesting results considered by the author (unsupervised learning process with EM algorithm, some results of color calibration, global Hough transform for circles, evaluated normal, circle and jerky motion and so on).

Klíčová slova

Motion capture system, camera system, movement analysis, motion analysis, image processing, pattern recognition, computer vision, color calibration, color correction, camera calibration, marker detection, Expectation-Maximization, EM algorithm, K-means, Hough transform, minimum error thresholding, iterative thresholding, multispectral thresholding, linear regression, Hermite interpolation, linear interpolation, direct linear transformation, closed-form solution, Parkinson’s disease, extrinsic camera parameters, intrinsic camera parameters

Obsah

1. INTRODUCTION
2. STATE OF THE ART
2.1 IMAGE BASED MOVEMENT ANALYSIS
2.2 MARKERS
2.3 DEVICES FOR MOVEMENT ANALYSIS
2.4 CHARACTERIZATION OF THE MOTOR FUNCTION OF PATIENTS
3. IMAGE PREPROCESSING
3.1 DEINTERLACING
3.2 MOTION BLURRING
3.3 COLOR CALIBRATION
3.3.2 Solution of the Color Calibration for Image Improvement
3.3.2.1 Color Calibration via Linear Interpolation
3.3.2.2 Color Calibration via Multiple Linear Regression Model
3.3.2.3 Color Calibration via Cubic Hermite Interpolating Polynomial
3.3.3 Results of Color Calibration
3.3.4 Discussion
3.3.5 Conclusion
3.3.6 Color Calibration Module
4. CAMERA CALIBRATION
4.1 CAMERA MODELS
4.2 RADIAL AND TANGENTIAL DISTORTIONS
4.3 SINGLE CAMERA CALIBRATION
4.3.2 Direct linear estimation of camera matrix
4.3.3 Closed-form solution of camera matrix estimation
4.4 CAMERA CALIBRATION MODULE
4.5 STEREO CAMERA CALIBRATION
4.5.2 Stereo Calibration Module
5. MARKER DETECTION
5.1 THRESHOLDING
5.1.1 Minimum Error Thresholding
5.1.1.1 Solution of Minimum Error Thresholding
5.1.2 Iterative Thresholding
5.1.2.1 Results of Iterative Thresholding
5.1.3 Multispectral Thresholding
5.1.3.1 Solution of Multispectral Thresholding
5.2 HOUGH TRANSFORM FOR CIRCLES
5.2.1 Solution of the Hough Transform for Circles
5.3 EXPECTATION-MAXIMIZATION ALGORITHM
5.3.1 Solution of the EM Algorithm
5.3.2 Discussion and Conclusion of the EM Algorithm
5.4 K-MEANS
5.4.1 Solution and Discussion of the K-means Algorithm
5.5 COMPARISON OF SEGMENTATION METHODS
5.6 DISCUSSION AND CONCLUSION
5.7 MARKER DETECTION MODULE
6. 3D RECONSTRUCTION
6.2 3D RECONSTRUCTION MODULE
7. MOTION ANALYSIS
7.1 MOTION ANALYSIS MODULE
8. CONCLUSION
9. REFERENCES
10. REFERENCES OF AUTHOR’S PUBLICATION
11. APPENDIX
11.1 RESULTS OF COLOR CALIBRATION
11.2 UNSUPERVISED LEARNING OF EXPECTATION-MAXIMIZATION (EM) ALGORITHM
11.3 RESULTS OF HOUGH TRANSFORM FOR CIRCLES
11.3.1 Global Hough Transform for Circles
11.4 RESULTS OF MOTION ANALYSIS
11.4.1 Simulated Normal Motion
11.4.2 Simulated Circle Motion
11.4.3 Simulated Jerky Motion


Stažení práce

Jako nejlevnější a nejbezpečnější možnost byla jednoznačně zvolena platba bankovním převodem.

Získejte práci prostřednictvím internetového bankovnictví. Zašlete 400 Kč na č.ú. 1041664027 / 3030, do pole variabilní symbol uveďte číslo 0252 pro odlišení zvolené práce a do textového pole (např. do pole „Popis příkazce“) napište Vaší emailovou adresu. Práce Vám bude zaslána do 24 hodin od doručení požadované částky.


Publikujte své vlastní práce a vydělejte si slušné peníze

Vaše studentské práce můžete vkládat zde


TOP Nabídka!

Potřebujete napsat referát, seminárku nebo diplomovou práci? Žádný problém!

Zpracujeme Vám kvalitní a originální podklady na míru.

Svěřte se do rukou profesionálů. Více informací zde

Prohledat práce

Kontakty a podpora

Provozní doba

Denně 8:00 - 0:00

Emailová adresa

info@diplomky.net
Phone +420607443173

Tip měsíce

 

Doporučte náš web

získejte až 300 Kč

za každou

vloženou práci!

Více informací zde

TOP Nabídka!

 

Nestíháte Vaše

studium?

Nezoufejte!

Vypracujeme Vám

podklady na míru.

Pro více informací

pokračujte zde