Understanding HOG Features
HOG features were first introduced by Dalal and Triggs in 2005 as a robust feature extraction method for pedestrian detection. The core idea behind HOG is to capture the distribution of gradient orientations in an image, which can be used to describe the shape and appearance of objects. HOG features are computed by dividing an image into small cells, calculating the gradient orientations within each cell, and then aggregating these orientations into a histogram. This histogram represents the distribution of gradient orientations, which can be used as a feature vector for object detection.
HOG Feature Visualization in Python Using skimage
Object detection is a fundamental task in computer vision, where the goal is to identify and locate objects within images or videos. However, this task can be challenging due to the complexity of real-world images, which often contain varying lighting conditions, occlusions, and cluttered backgrounds. Traditional approaches to object detection rely on handcrafted features, which can be time-consuming and may not generalize well to new scenarios. One popular method for feature extraction is the Histogram of Oriented Gradients (HOG) technique.
In this article, we will understand and implement examples of visualizing HOG feature arrays using skimage.
Table of Content
- Understanding HOG Features
- Advantages of HOG Feature
- Visualizing HOG Features with Python and skimage
- Customizing HOG Feature Visualization with skimage