3d major final ppt new fnl

7/29/2019 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

3d major final ppt new fnl

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