What is Oriented Gradients Histogram

In the fields of computer vision and image processing, the histogram of oriented gradients (HOG) is a feature descriptor that is utilized for the purpose of object detection. This technique is used to count the number of instances of gradient orientation that occur in specific regions of an image. This technique is comparable to edge orientation histograms, scale-invariant feature transform descriptors, and shape contexts; however, it varies from those methods in that it is computed on a dense grid of evenly spaced cells and employs overlapping local contrast normalization with the purpose of achieving a higher level of accuracy.

How you will benefit

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

Chapter 1: Histogram of oriented gradients

Chapter 2: Edge detection

Chapter 3: Scale-invariant feature transform

Chapter 4: Speeded up robust features

Chapter 5: GLOH

Chapter 6: Local binary patterns

Chapter 7: Oriented FAST and rotated BRIEF

Chapter 8: Boosting (machine learning)

Chapter 9: Image segmentation

Chapter 10: Object detection

(II) Answering the public top questions about oriented gradients histogram.

(III) Real world examples for the usage of oriented gradients histogram 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 Oriented Gradients Histogram.