Object detection through search with a foveated visual system
Fig 3
Histogram of oriented gradients (HoG) of a sample image.
Left: input image, right: HoG result. First, the input image is convolved with two 1-D filters, namely [+ 1 0 −1] and its transpose. The gradient magnitude and orientation at each pixel are estimated from the convolution results. Then, the image is divided into small, square bins. In each bin, an orientation histogram is computed, which shows the (relative) total gradient magnitude per orientation. Finally, the histogram in each bin is normalized by the total “energy” (e.g. ℓ2 norm) of a 2x2 block containing the bin akin to divisive local contrast normalization. This final step is known as block normalization. On the right, each HoG bin is represented with short, oriented line segments where brightness encodes the magnitude of the associated orientation. Due to the block normalization, in homogeneous areas (e.g. top-right) all orientations have high and similar magnitudes. (Image source statement: the original picture on the left was taken by the first author.)