A beginner's guide to image pre-processing techniques

Description

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 Includes image data pre-processing for neural networks and deep learning Covers geometric, pixel brightness, filtering, mathematical morphology transformation, and segmentation pre-processing techniques Illustrates a combination of basic and advanced pre-processing techniques essential to computer vision pipeline Details complications to resolve using image pre-processing  Read more... Abstract: 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 Includes image data pre-processing for neural networks and deep learning Covers geometric, pixel brightness, filtering, mathematical morphology transformation, and segmentation pre-processing techniques Illustrates a combination of basic and advanced pre-processing techniques essential to computer vision pipeline Details complications to resolve using image pre-processing Content: Chapter 1: Perspective of Image Preprocessing on Image Processing1.1 Introduction to Image Preprocessing1.2 Complications to resolve using Image Preprocessing1.3 Effect of Image Preprocessing on Image Recognition1.4 Summary1.5 ReferencesChapter 2: Pixel Brightness Transformation Techniques2.1 Position Dependent Brightness Correction2.2 Grayscale Transformations2.3 Summary2.4 ReferencesChapter 3: Geometric Transformation Techniques3.1 Pixel Coordinate Transformation or Spatial Transformation3.2 Brightness Interpolation3.3 Summary3.4 ReferencesChapter 4: Filtering Techniques4.1 Spatial filter4.2 Frequency Filter4.3 Summary4.4 References Chapter 5: Segmentation Techniques5.1 Thresholding5.2 Edge Based Segmentation5.3 Region-Based Segmentation5.4 Summary5.5 ReferencesChapter 6: Mathematical Morphology Techniques6.1 Binary Morphology6.2 Grayscale Morphology6.3 Summary6.4 ReferencesChapter 7: Other Applications of Image Preprocessing7.1 Preprocessing of Color Images7.2 Image preprocessing for Neural Networks and Deep learning7.3 Summary7.4 References