PYTHON WITH IMAGE PROCCESSING

PYTHON WITH IMAGE PROCCESSING

1 Abstract

A methodological study on significance of image processing and its applications in the field of computer vision is carried out here. During an image processing operation the input given is an image and its output is an enhanced high quality image as per the techniques used. Image processing usually referred as digital image processing. Our study provides a solid introduction to image processing along with segmentation techniques, computer vision fundamentals and its applied applications that will be of worth to the image processing and computer vision research communities.

2 Introductions

2.1 What is a Digital?

2.2 Image Processing

2.3 Internship Objectives

2.4 Project Results

3 Python

3.1 Denition

3.2

4 OpenCV

4.1 Installing Python and OpenCv

4.1.1 Download the Python 3 Installer

4.1.2 Installing OpenCV

4.1.3 Python Virtual Environments

4.2 Reading, displaying, and saving images

4.2.1 Reading image

4.2.2 Display an image

4.2.3 Saving Image

4.2.4 Color image space

4.2.5 Changing Color space

4.2.6 Cropping Image

4.2.7 Transformations of Images

4.3 Image Processing in OpenCV

4.3.1 2D convolution

4.3.2 Smoothing Image

4.3.3 Morphological Transformations

4.3.4 Edge detection

4.3.5 Image Thresholding and segmentation

4.3.6 Image Histogram

4.3.7 Template Matching with Multiple Objects

4.4 Feature Detection and Description

4.4.1 What are keypoints ?

4.4.2 Harris Corner Detection

4.4.3 Shi-Tomasi Corner Detector and Good Features to Track

4.5 Face Detection using Haar Cascades

4.5.1 Basic concept of HAAR cascade algorithm

4.5.2 Haar-cascade Detection in OpenCV

5 Linear Filters 54 5.1 Convolution

5.2 Low Pass Filter

5.2.1 Mean Filter

5.2.2 Gaussian filter

5.2.3 Binomial filter

5.3 High Pass Filte

5.3.1 DOG Filter

5.3.2 Laplacian of Gaussian (LoG)

6 Non Linear Filters 58 6.1 Median

6.2 Maximum Filter

6.3 Minimum Filter

7 Directional Filters

7.1 What is a Gradient of the image?

7.2 Laplacian

7.3 Filters

7.3.1 Directional Filter

7.3.2 Prewitt Operator

7.3.3 Robert Operator

7.3.4 Krish Filter

7.3.5 Sobel Filter

7.4 Gaussian Blur

7.4.1 Laplacien Filter

8 Image Restoration

8.1 Image deblurring

8.2 Image in-painting

8.3 Super resolution

8.4 The Wiener filter

8.5 The Perona-Malik equation