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Principles of Digital Image Processing by Wilhelm Burger , Mark J. Burge Book Details
| Book Name | Principles of Digital Image Processing |
| Author | Wilhelm Burger , Mark J. Burge |
| Category | Electronics & Communication Engineering, Education Books |
| Book Language | English |
| Pages | 274 |
| ISBN | 9781848001916 |
| Country | India |
| Book Size | 12 MB |
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About Principles of Digital Image Processing by Wilhelm Burger , Mark J. Burge Book
Principles of digital image processing involve the fundamental concepts, techniques, and methodologies used to manipulate and analyze digital images. Digital image processing is a field that deals with the manipulation of images using computer algorithms to enhance, transform, or extract information from visual data. It has a wide range of applications in various fields, including photography, medical imaging, remote sensing, computer vision, and more. Here are some key principles of digital image processing:
1. Image Representation:
- Digital images are represented as a matrix of pixel values, where each pixel corresponds to a small portion of the image.
- Grayscale images use intensity values to represent brightness levels, while color images use multiple channels (such as red, green, and blue) to represent colors.
2. Image Enhancement:
- Image enhancement techniques aim to improve the visual quality of an image by adjusting its contrast, brightness, sharpness, or other visual attributes.
- Histogram equalization, contrast stretching, and gamma correction are common enhancement methods.
3. Filtering and Convolution:
- Filtering involves applying a convolution operation to an image using a filter kernel. Filters can enhance or suppress certain features in an image.
- Common filters include blurring (low-pass), sharpening (high-pass), edge detection, and noise reduction filters.
4. Image Restoration:
- Image restoration techniques aim to recover or improve the quality of a degraded image caused by factors like noise, motion blur, or sensor limitations.
- Inverse filtering, Wiener filtering, and blind deconvolution are used for image restoration.
5. Image Segmentation:
- Image segmentation divides an image into meaningful regions or objects based on pixel characteristics.
- Thresholding, region growing, and edge-based methods are used for segmentation.
6. Feature Extraction:
- Feature extraction involves identifying and extracting significant patterns or features from an image.
- Texture analysis, edge detection, and corner detection are common feature extraction techniques.
7. Image Compression:
- Image compression reduces the storage or transmission requirements of an image while maintaining perceptual quality.
- Lossless and lossy compression methods, such as JPEG and PNG, are widely used.
8. Morphological Image Processing:
- Morphological operations involve the analysis and manipulation of image structures using set theory concepts (dilation, erosion, opening, closing).
9. Image Registration:
- Image registration aligns multiple images of the same scene to enable comparisons or analyses.
- Medical imaging and remote sensing often use registration techniques.
10. Image Analysis and Computer Vision:
- Image analysis involves extracting meaningful information from images to make decisions or perform tasks.
- Computer vision applications include object detection, recognition, tracking, and 3D reconstruction.
11. Deep Learning and Neural Networks:
- Recent advancements in deep learning have revolutionized image processing with techniques like convolutional neural networks (CNNs) for image recognition and segmentation.
Principles of digital image processing are essential for understanding the underlying concepts and techniques used to manipulate and analyze visual data in the digital domain. These principles form the foundation for a wide range of applications that impact various industries and disciplines.
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