Oriented

Understanding of Histogram-of-Oriented-Gradients

Understanding of Histogram-of-Oriented-Gradients

The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image.

  1. How Histogram of Oriented Gradients HOG works?
  2. Is Histogram of Oriented Gradients an algorithm?
  3. What is HOG used for?

How Histogram of Oriented Gradients HOG works?

HOG decomposes an image into small squared cells, computes an histogram of oriented gradients in each cell, normalizes the result using a block-wise pattern, and return a descriptor for each cell.

Is Histogram of Oriented Gradients an algorithm?

Histogram of Oriented Gradients(HOG), one of the well-known image processing algorithms, is a feature descriptor that is used for extracting essential features and shapes of a particular object within an image such as edges and textures.

What is HOG used for?

HOG, or Histogram of Oriented Gradients, is a feature descriptor that is often used to extract features from image data. It is widely used in computer vision tasks for object detection.

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