This book discusses the use of digital image processing technologies in a wide range of industries. Using machine learning approaches, readers will get an understanding of the many technologies and tactics utilized in the digital image processing and the management of large amounts of data. Students and scholars in topics including engineering, agricultural, and medical image processing would find this book useful in further developing their expertise in these areas. This work is aimed at students with some background in mathematics or computer science, and it uses examples written in the programming language Matlab to introduce and clarify some of the fundamental ideas behind contemporary pattern recognition and image processing techniques. The focus is on application rather than theory, and the book provides a context for understanding the ideas through a sequence of carefully selected examples, activities, and computer experiments that rely on real-world applications in the fields of science, healthcare, and engineering. This book covers important topic like Understanding an image, Resolution and quantization, Bit-plane splicing, Pixels, Image restoration, Blind deconvolution, Shape transformation and homogeneous coordinates, Morphological processing, Dilation, erosion and structuring elements within Matlab, The hit-or-miss transformation, Image segmentation and many more.