Computer Vision with OpenCV: HOG Feature Extraction

Full course:

In this excerpt from “Autonomous Cars: Deep Learning and Computer Vision with Python, ” Dr. Ryan Ahmed covers the Histogram of Gradients technique, and how OpenCV can use it to extract features from images using Python.

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  1. One would think step 2 lead to the loss of information. When you bin the angle values and their intensities you discard their positions. Images are highly spatial data hence ignoring the placement of the gradients and only vectoring their angles and magnitudes seems like a way to cut corners. It must negatively impact the accuracy of neural networks being trained on these images.

  2. Each 8×8 cell is represented as a 1×9 HOG vector. How are all these vectors fed to the SVM? Do we add them up and feed the aggregate 9d vector or are the vectors appended? Thanks!

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