Digital image processing
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$30.00
$30.00
🌸🌸All applications are solved using matlab💐💐
Digital image processing (DIP) contains wide scope for researchers and scientists to work on various areas of science and engineering. Several algorithms have already been proposed and developed. This section makes a brief discussion on previous works and applications of image processing. Digital image processing is helpful for many applications and their analysis, which can be used in different applications like in vehicle detection from an image using aerial camera. One such application by using this concept can be applied in keyboard industry where poorly manufactured keyboards can be detected at manufacturing stage. In this type of applications an input image of the manufactured keyboard is fed to detect the missing key or damaged key. Similar concept has been used in Face Recognition, Facial Expression Recognition. Further with the advancement of image enhancement techniques, a precise extraction of particular feature has become possible like number plate recognition from the detected vehicle and eyes, nose, ears, lip gesture from recognized face. Digital Image Processing is applied in the fields of Computer vision, Face detection, Feature detection, Lane departure warning system, Non-photorealistic rendering, Medical image processing, Microscope image processing Morphological image processing, Remote sensing, etc.
This book includes: Introduction
1- Histogram 2- Histogram equalization 3- Matlab examples
Images types
Image Arithmetic 1- Addition/Subtraction 2- Multiplication and division 3- Gamma correction
4- Logical operations 5- Image resizing and cropping
Pixel Neighbors
1- Pixels connectivity 2- Pixels Adjacency 3- Path 4- Pixel distance
Image filtering
1- Convolution 2- Correlation 3- Image filtering
Edge detection
1- Definition of edges 2- Edge detection 3- Detecting lines and isolated points
Image Morphology
1- Structuring element 2- Dilation 3- Erosion 4- Opening and closing
5- Top hat operation 6- Bothat operation 7- Hit and miss
Image Cryptography
1- Introduction 2- Encryption-decryption using XORING 3- Encryption-decryption using matrix multiplication 4- Blocking and image encryption-decryption 5- Cryptography and selective noise
6- Protecting digital color image applying double phase encryption
Image Steganography
1- Introduction 2- Least significan bit method 3- LSB2 method of data steganography
4- Message encoding 5- Increasing the security of LSB and LSB2 6- PVD method
7- Comparative analysis of methods used in steganography
Image Compression Decompression
1- Run length coding method
2- LZW method
3- Huffman compression-decompression
Digital image features extraction 1- Introduction
2- Statistical method
3- Local binary pattern method (LBP) 4- Features extraction using k_mean clustering
5- Features extraction using image decomposition
6- Analysis of methods used for image feature extraction
Introduction to Artificial Neural Networks (ANN)
Steps to create and run ANN
Digital Image Recognition
1- Introduction
2- Building image recognition system
3- Practical application
Fingerprint Recognition
1. Introduction
2. Fingerprint structure
3. Fingerprint features extraction
Speech Recognition
1- Introduction
2- Building speech recognition system
3-Features extraction methods
Digital image processing (DIP) contains wide scope for researchers and scientists to work on various areas of science and engineering. Several algorithms have already been proposed and developed. This section makes a brief discussion on previous works and applications of image processing. Digital image processing is helpful for many applications and their analysis, which can be used in different applications like in vehicle detection from an image using aerial camera. One such application by using this concept can be applied in keyboard industry where poorly manufactured keyboards can be detected at manufacturing stage. In this type of applications an input image of the manufactured keyboard is fed to detect the missing key or damaged key. Similar concept has been used in Face Recognition, Facial Expression Recognition. Further with the advancement of image enhancement techniques, a precise extraction of particular feature has become possible like number plate recognition from the detected vehicle and eyes, nose, ears, lip gesture from recognized face. Digital Image Processing is applied in the fields of Computer vision, Face detection, Feature detection, Lane departure warning system, Non-photorealistic rendering, Medical image processing, Microscope image processing Morphological image processing, Remote sensing, etc.
This book includes: Introduction
1- Histogram 2- Histogram equalization 3- Matlab examples
Images types
Image Arithmetic 1- Addition/Subtraction 2- Multiplication and division 3- Gamma correction
4- Logical operations 5- Image resizing and cropping
Pixel Neighbors
1- Pixels connectivity 2- Pixels Adjacency 3- Path 4- Pixel distance
Image filtering
1- Convolution 2- Correlation 3- Image filtering
Edge detection
1- Definition of edges 2- Edge detection 3- Detecting lines and isolated points
Image Morphology
1- Structuring element 2- Dilation 3- Erosion 4- Opening and closing
5- Top hat operation 6- Bothat operation 7- Hit and miss
Image Cryptography
1- Introduction 2- Encryption-decryption using XORING 3- Encryption-decryption using matrix multiplication 4- Blocking and image encryption-decryption 5- Cryptography and selective noise
6- Protecting digital color image applying double phase encryption
Image Steganography
1- Introduction 2- Least significan bit method 3- LSB2 method of data steganography
4- Message encoding 5- Increasing the security of LSB and LSB2 6- PVD method
7- Comparative analysis of methods used in steganography
Image Compression Decompression
1- Run length coding method
2- LZW method
3- Huffman compression-decompression
Digital image features extraction 1- Introduction
2- Statistical method
3- Local binary pattern method (LBP) 4- Features extraction using k_mean clustering
5- Features extraction using image decomposition
6- Analysis of methods used for image feature extraction
Introduction to Artificial Neural Networks (ANN)
Steps to create and run ANN
Digital Image Recognition
1- Introduction
2- Building image recognition system
3- Practical application
Fingerprint Recognition
1. Introduction
2. Fingerprint structure
3. Fingerprint features extraction
Speech Recognition
1- Introduction
2- Building speech recognition system
3-Features extraction methods