Category Archives: Image Feature Descriptors

Pedestrian Detection using Histogram of Gradients (HoG) and a Random Forests Classifier

This entry is part 1 of 1 in the series Pedestrian Detection

1,178 64×128 sized images of pedestrians (positive samples) and 4,530 of the same sized negative samples were extracted from the INRIA dataset and divided randomly into two to give a training set and a validation set. HoG features on the … Continue reading

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SURF Feature Descriptors on Python OpenCV

This entry is part 1 of 1 in the series Image Feature Descriptors

This is a test of SURF features (http://en.wikipedia.org/wiki/SURF, claimed to be faster than the original SIFT feature descriptors) on Python OpenCV. Parameters used were: (0, 300, 3, 4) 0: (basic, 64-element feature descriptors) hessian threshold: 300 number of octaves: 3 … Continue reading

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