Motion Estimation: Advancements and Applications in Computer Vision

In computer vision and image processing, motion estimation is the process of determining motion vectors that describe the transformation from one 2D image to another; usually from adjacent frames in a video sequence. It is an ill-posed problem as the motion happens in three dimensions (3D) but the images are a projection of the 3D scene onto a 2D plane. The motion vectors may relate to the whole image or specific parts, such as rectangular blocks, arbitrary shaped patches or even per pixel. The motion vectors may be represented by a translational model or many other models that can approximate the motion of a real video camera, such as rotation and translation in all three dimensions and zoom.

How you will benefit

(I) Insights, and validations about the following topics:

Chapter 1: Motion_estimation

Chapter 2: Motion_compensation

Chapter 3: Block-matching_algorithm

Chapter 4: H.261

Chapter 5: H.262/MPEG-2_Part_2

Chapter 6: Advanced_Video_Coding

Chapter 7: Global_motion_compensation

Chapter 8: Block-matching_and_3D_filtering

Chapter 9: Video_compression_picture_types

Chapter 10: Video_super-resolution

(II) Answering the public top questions about motion estimation.

(III) Real world examples for the usage of motion estimation 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 Motion Estimation.