The scale σ is defined to be the width of the Gaussian window within which the gradient vectors of the image are pooled.
Many models use the principle of "optic flow", this being the 2D projection of the flow vectors onto the image plane relative to the observer, instead of the detection of the 3D motion in space (see Fig. 1).
In our case, images are the 2D functions being compared, and r⃗ and s⃗ are vectors in the image plane.
It uses a technique assuming that an object in a 3D scene is located on a vector between the image of the object in the camera and the object itself.
The histogram z is then used as the feature vector of the image and used to train a classifier.
Figure 7c shows the plot of the HOG feature vector of the image window shown in Figure 7a.
For image contours, μ 1 > > μ 2 > > 0 and λ 2 > λ 1 > 0, diffusion tensor is anisotropic and mainly directed by the tangent vector of the image.
After LBP is obtained for each pixel, in this work, a histogram is built in order to describe the whole image using P+2 bins, yielding the feature vector of the image.
Let the center of projection be the point C=(X C,Z C,1 T, expressed in homogeneous coordinates, and let l= a,b,c)T be the homogeneous vector representing the image line.
