The book "Optimization Techniques in Computer Vision" provides a comprehensive introduction to practical optimization techniques used in image processing and computer vision problems. It addresses ill-posed problems and illustrates how these are linked to typical challenges in image processing through examples. A central aspect of the book is unconstrained optimization, which aims to find the best possible solution by numerically minimizing a single scalar objective or cost function. While unconstrained optimization problems have been extensively researched, most practical optimization problems arise under a set of constraints. These constraints may include predefined pixel intensity ranges, smoothness or correlation with neighboring information, as well as statistical or spectral properties of the solution. The book also covers regularized optimization, a specialized method for solving a class of constrained optimization problems that is significant in many application areas such as image restoration and optical flow estimation.