3d Major Final Ppt New Fnl
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Transcript of 3d Major Final Ppt New Fnl
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Project Guide : Asst. Prof. Shabin P.
Jansher K.M
Kiran Surendran A
Midhul Manoj Kumar
Mithun Binny
Mohd. Jasheer T.P
Submitted by,
3D MODELLING OF OBJECTS FOR BETTERROBOTIC VISION
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Overview
One of the method of path planning ofautomated robots is image processing
2D images make path prediction almostimpossible
2D to 3D conversion helps path prediction
Matlab programming is used for 2D to 3Dconversion
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INTRODUCTION
Visual perception is the ability to interpretinformation and surroundings from the effects ofvisible light reaching the eye
Robots uses cameras for vision which gives 2Dimage
Visual perception of robots cannot be done using
2D images; need ofcomputer vision
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Contd
2D positions converted to 3D depth through stereomatching
Stereo matching is the process of taking two ormore images and estimating a 3D model of thescene by finding matching pixels in the images andconverting their 2D positions into 3D depths
The depth information is obtained as a 3D model ofthe scene
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OBJECTIVE
Produce a 3D model of a scene
Using
Stereo images from a single camera
Pixel wise disparity estimation
Applying it to corresponding equation to getthe depth information
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DESIGNImage 1 Image
2
Disparity estimation
Z co-ordinate
generation
3D reconstruction
3D image
Camera
parameters
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DIFFERENT BLOCKS
1. Obtaining stereo image pair
2. Disparity estimation
3. Camera parameters
3.1 Intrinsic parameters
3.2 Extrinsic parameters
4. z- coordinate generation
5. 3D - reconstruction
6. 3D - image
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1. Obtaining stereo imagepair
closely resemble images
Images taken at a time with same cameraparameter
or
Single camera kept at 2 position in
same horizontal plane butdiffering by small angle variation
To MATLAB via IMAQ tool
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Pixel wise Comparison
Relative disparityplotting
Convert Image to GreyScale
2. Disparity estimation
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Disparity Map
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Disparity estimation is the process to locatecorresponding points in the images
Gray scale digital image is an image in which thevalue of each pixel is a single sample, that is, itcarries only intensity information.
Gray scale consists of only 1 channel, which helpsin better analysis
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3. Camera parameters
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3.1 Intrinsic parameters
Intrinsic matrix
Five intrinsic parameters
Focal length
Image format
Principle point
Scale factor
Skew coefficient
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3.2 Extrinsic parameters
R,T are the extrinsic parameters
3D world coordinates 3Dcamera coordinates
Defines the position of camera center
& cameras heading in worldcoordinates
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4. z- coordinate generation
Camera intrinsic matrix
foca l_ leng th_x skew _x cam era_cen te r_x ;
K = 0 foca l_ leng th_y cam era_cen te r_y ;
0 0 1
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Disparity measurement of the stereo depth as:
Zworld = f (1+B/d)
B baseline distance between the two cameras
f focal length of the camera.
d disparity : difference in image location of an object seen by left
and right images
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5. 3D - reconstruction
Parallel stereo camera geometry
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Every physical point M yields a pair of 2Dprojections m1 and m2 on two images
Depth of a point M can be simply calculated by
B baseline distance between the two cameras
f focal length of the camera.
d disparity : difference in image location ofan object seen by left and right images
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Depth is inversely proportional to disparity
As the distance between cameras and an object
increases, depth errors, caused by disparity errors,are increasingly amplified since the depth isinversely proportional to the disparity.
As a result, the accuracy of depth reconstructiondepends on the distance of the object as well as theaccuracy of the disparity fields.
However, this can be overcome by adjustingcamera parameters
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3D image
3D image the final product
ContainsX,Y,Z coordinate information
3D images can be improved by
Disparity regulation
Dynamic programming
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Flow chart
N
Y
N
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Matlab
High-level language for technical
computing
Tools for building custom graphical user
interfaces
Provides interactive environment foralgorithm development, data visualization,data analysis, and numeric computation
SOFTWARE USED
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Toolbox
Image processing toolbox
Image acquisition toolbox
Camera calibration toolbox
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LEFT IMAGE
RIGHT IMAGE
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Pixel wise disparity estimation is relatively less time
consuming
Left image is taken as reference
Each pixel is compared with pixels in 2nd image of same
row
Minimum disparity found
Relative disparity :Near points have more disparity and
far points have less disparity
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Disparity mapMerged Image
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3D model
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FUTURE WORKS
Block matching algorithm for better disparity.
Dynamic programming and image pyramiding
for smooth disparity map.
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APPLICATION
Path planning of automated robots
Space missions
Explorations
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ADVANTAGES
Easier
Less time consuming method
Less hardware
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THANK YOU
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R 3X3 rotation matrix
t 3X1 translation vector.
Reconstruct the 3D plotting(X,Y,Z)
Extrinsicmatrix
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Apply in for eachpixel.
Obtain camera parameters using cameracalibration toolbox
P camera matrix
K camera intrinsic matrix
, scale factors=focal length
(x0, y0) coordinate of the principal point
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REAL TIME CONSIDERATIONS
Rectified images
Have to perform image rectification