SURF :- In computer vision, speeded up robust features (SURF) is a patented local feature detector and descriptor. It can be used for tasks such as object recognition, image registration, classification or 3D reconstruction. It is partly inspired by the scale-invariant feature transform (SIFT) descriptor. We will see the basics of SURF; We will see SURF functionalities in OpenCV ... is not required, so no need of finding this orientation, which speeds up the process. ... SURF is good at handling images with blurring and rotation, 1:- Feature detection is the process where we automatically examine an image to extract features, that are unique to the objects in the image,

1. Under Supervision of :
Mr. Ramesh Chand Pandey
Submitted to :
Mr. Prince Rajpoot
Submitted by:
Adarsh Kumar Yadav
Roll No. : 1573713002

2.  What is Image processing
 Steps for Image processing
 Purpose of Image processing
 Applications
 Introduction of SURF
 Computational steps involved in SURF
 Tools used in SURF
 Comparison between (Scale Invariant Feature Transform)
SIFT and SURF
 Conclusion
 References

3.  A method to perform some operations on an image, to
get an enhanced image and extract useful information
from it.
 It is a type of signal processing in which input is an
image and output may be image or characteristics
associated with that image.
 One of rapidly growing technology.

4. Importing the image via image acquisition
tools
Analysing and manipulating the image
Report that is based on image analysis
Fig. 1 Steps in Image Processing

5.  Visualization - Observe the objects that are not visible.
 Image sharpening - To create a better image .
 Image retrieval - Seek for the image of interest.
 Measurement of pattern - Measures various objects in an
image.
 Image Recognition - Distinguish the objects in an image.

6.  Image sharpening
 Medical field
 Remote sensing
 Transmission and encoding
 Machine/Robot vision
 Pattern recognition
 Video processing
 Microscopic Imaging

7. Image Enhancement
Fig. 2 Image Enhancement

8.  SURF stands for Speeded up robust features.
 SURF was first presented by Herbert Bay, at the 2006
European Conference on Computer Vision.
 It is an algorithm to detect and describe local feature
of Images.
 Used for tasks such as object recognition, image
registration, classification or 3D reconstruction.

9.  To detect interest points, SURF uses an integer
approximation of the determinant of Hessian blob detector,
which can be computed with 3 integer operations using a
pre-computed integral image i.e.
g(x,y,t)= (1/2Πt) *e-(x*x + y*y)/2t
here t is scale
 Its feature descriptor is based on the sum of the Haar
wavelet response around the point of interest. These can
also be computed with the aid of the integral image.
Sum=I(A)+I(B)+I(C)+I(D)
A,B,C,D belongs to integral image I

10. Fig. 3 Difference of Gaussian

11. Flat Edge Corner
Fig. 4 Illumination Invariance

12. Characteristics Scale
Fig. 5 Feature Detection with Automatic Scale Selection

13.  MATLAB -high-performance language for technical
computing.
 Open CV -an open source computer vision and machine
learning software library.
 EMGU CV – cross platform that allows Open CV
functions to be called from .NET compatible languages
such as C#, VB, VC++.
 Scanner – to convert image from analog to digital form

14.  Stereo Vision
 Object Recognition
 Image stitching
 Image Registration
 Image Classification
 3-D Reconstruction

15. SIFT SURF
 Scale Invariant Feature Transform  Speeded Up Robust Features
 Slow, takes more time for
extracting interest points
 Faster, takes less time
 More no. of features can be
detected
 Less no. of features are detected
 Expensive  Cheaper

16. Fig. 6 Sample Image

17. Fig. 7 Interested point detection by SIFT

18. Fig. 8 Interested point detection by SURF

19.  SURF is faster than SIFT by 3 times
 SURF recall precision is not worse than SIFT
 SURF is good at handling images with blurring or
rotation
 SURF is poor at handling images with viewpoint

20.  https://www.tutorialspoint.com/dip/image_processing_introducti
on.htm
 Fig. 1
https://www.sciencedirect.com/science/article/pii/S03770427110
02834
 Fig. 2,6,7,8
http://slideplayer.com/slide/4839560/
 Fig. 3,4,5
https://www.slideshare.net/zukun/modern-featurespart1detectors
 https://en.wikipedia.org/wiki/Speeded_up_robust_features
 https://www.quora.com/How-would-you-convert-an-analog-
image-into-a-digital-image
 https://en.wikipedia.org/wiki/Scale-invariant_feature_transform

21. THANK YOU !