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What is Image Segmentation
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.
How you will benefit
(I) Insights, and validations about the following topics:
Chapter 1: Image segmentation
Chapter 2: Edge detection
Chapter 3: Scale-invariant feature transform
Chapter 4: Thresholding (image processing)
Chapter 5: Otsu's method
Chapter 6: Corner detection
Chapter 7: Graph cuts in computer vision
Chapter 8: Mean shift
Chapter 9: Range segmentation
Chapter 10: Watershed (image processing)
(II) Answering the public top questions about image segmentation.
(III) Real world examples for the usage of image segmentation in many fields.
Who this book is for
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Image Segmentation.
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.
How you will benefit
(I) Insights, and validations about the following topics:
Chapter 1: Image segmentation
Chapter 2: Edge detection
Chapter 3: Scale-invariant feature transform
Chapter 4: Thresholding (image processing)
Chapter 5: Otsu's method
Chapter 6: Corner detection
Chapter 7: Graph cuts in computer vision
Chapter 8: Mean shift
Chapter 9: Range segmentation
Chapter 10: Watershed (image processing)
(II) Answering the public top questions about image segmentation.
(III) Real world examples for the usage of image segmentation in many fields.
Who this book is for
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Image Segmentation.
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