For optimal computer vision outcomes, attention to image pre-processing is required so that one can improve image features by eliminating unwanted falsification. This book emphasizes various image pre-processing methods which are necessary for early extraction of features from the image. Effective use of image pre-processing can offer advantages and resolve complications that finally results in improved detection of local and global features. Different approaches for image enrichments and improvements are conferred in this book that will affect the feature analysis depending on how the procedures are employed.
Key Features
- Describes the methods used to prepare images for further analysis which includes noise removal, enhancement, segmentation, local, and global feature description
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- Includes image data pre-processing for neural networks and deep learning
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- Covers geometric, pixel brightness, filtering, mathematical morphology transformation, and segmentation pre-processing techniques
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- Illustrates a combination of basic and advanced pre-processing techniques essential to computer vision pipeline
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- Details complications to resolve using image pre-processing
Table of Contents:
Chapter 1: Perspective of Image Preprocessing on Image Processing
1.1 Introduction to Image Preprocessing
1.2 Complications to resolve using Image Preprocessing
1.3 Effect of Image Preprocessing on Image Recognition
1.4 Summary
1.5 References
Chapter 2: Pixel Brightness Transformation Techniques
2.1 Position Dependent Brightness Correction
2.2 Grayscale Transformations
2.3 Summary
2.4 References
Chapter 3: Geometric Transformation Techniques
3.1 Pixel Coordinate Transformation or Spatial Transformation
3.2 Brightness Interpolation
3.3 Summary
3.4 References
Chapter 4: Filtering Techniques
4.1 Spatial filter
4.2 Frequency Filter
4.3 Summary
4.4 References
Chapter 5: Segmentation Techniques
5.1 Thresholding
5.2 Edge Based Segmentation
5.3 Region-Based Segmentation
5.4 Summary
5.5 References
Chapter 6: Mathematical Morphology Techniques
6.1 Binary Morphology
6.2 Grayscale Morphology
6.3 Summary
6.4 References
Chapter 7: Other Applications of Image Preprocessing
7.1 Preprocessing of Color Images
7.2 Image preprocessing for Neural Networks and Deep learning
7.3 Summary
7.4 References
