Corners and Edges of an Image yield an interesting features of the image content that can be used in image retrieval and object detection or scene recognition tasks. So, they be called “Local invariant features“.
In this context we want to explain Moravec Corner Detection Method Which is a simple corner detection method and we write It ‘s Python Code. This method proposed by Hans Moravec who is an adjunct faculty member at the Robotics Institute of Carnegie Mellon University.
Hans Moravec (source: Carnegie Mellon University website)
The key idea behind the concept is simple, first look at 4 boxes in image below (Image 1). In box 1 (Left box) you see a black box with a blue window on it, that contains some data of black box in it, suppose this black box is an Image, if you move this blue window horizontally or vertically, data in the window won’t change, so, this is a flat surface. In the 2nd box, if you move the blue window vertically, content of the blue window won’t change. but, if move window in horizontal direction, content of the blue window will change (white pixels will be more or less), so, It means that a vertical edge is in the blue window. In 3rd box, only moving vertically will change the data in the blue window, that means a horizontal edge is here. But, in 4th image moving horizontally and vertically will change the data in the blue window, so, in 4th image content of the blue window is a corner.
Image 1 : Image Edge and Corner
As you see here, Moravec method is a very simple method; however, It is useful, too. In the moravec paper we only move the blue window horizontally or vertically. though, others extend this method to move blue window in all directions for example if blue window is 3×3 box, the blue window can move in 8 directions.(Image below)
Image 2: Pixel Direction with a 
window
In a nutshell :
So, to find all corners in an image first we should find grayscale of the image, then we find the difference of intensity between each box and it’s 8 close boxes.
To find the difference 3 main methods in machine learning usually used. Those are:
- Subtract of values: That is not suitable for us, for instance, in 2 images with matrix below The intensity differences for pixels is 0 that can be confuse us to say that there is no change.
- Absolute value of differences. This is not suitable for optimization problems because this is not Differentiable.
- Square of the Difference (SD). This method does not have issues above so It is a useful method to calculate intensity differences. Mathematical definition of this method is below.
shows x and y position of 2 close pixels and 
is Intensity of pixel in 
Now, we know how moravec method works, but in real world senarios pixels are in 3 channels (Red, Green, Blue) and many of the pixels has Sqaure of Differences bigger than 0 
in all directions but all the time these pixels with 
are not Corner. Therefore, the practical method is to Compute Sum of Square of the Differences (SSD) and suppose a thereshold, if 
then, It shows a corner.
in this formula 
and 
must have values to cover 8 pixel around the pixel that we want to see if it is a corner(Pixel A in Image below), so and is a member of set 
to cover all the 8 pixels around A.
There are many method to find a suitable threshold, Usually Machine Leaning method works good.
Now it’s time to see the Python code of the more of it corner detection method.
Disadvantages if Moravec method:
- Scale Invariant
- Strong response to edge points (can be sensitive to noise)
- Finding a good size for moving window is Experimental.
- Moravec corner detector only concerns eight principle directions which will have a poor repeatability rate.
from PIL import Image
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
import cv2
from tqdm import tqdm
I_org = cv2.imread(r"Images/pexels-mak-jp-11504568.jpg")
I = cv2.cvtColor(I_org,cv2.COLOR_RGB2GRAY)
numRows, numCols = I.shape[0], I.shape[1] #h,w
directions = {
'left':(0,-1),
'right':(0,1),
'up':(-1,0),
'down':(1,0),
'up_left':(-1,-1),
'up_right':(-1,1),
'down_left':(1,-1),
'down_right':(1,1),
}
C = np.zeros(I.shape)
for i in tqdm(range(2,numRows-2)) :
for j in range(2,numCols-2) :
minSSD = -1
for d in directions.values():
u, v = d[0], d[1]
P1 = I[i-1:i+2, j-1:j+2]
P2 = I[i+u-1:i+u+2, j+v-1:j+v+2]
ssd = np.sum((P1 - P2)**2)
if minSSD == -1 :
minSSD = ssd
elif ssd < minSSD :
minSSD = ssd
C[i,j] = minSSD
th = C.mean()+2*C.std()
C_image = C > th
I2 = I_org#[:,:,::-1]
idx = np.argwhere(C_image)
r,c = idx[:,0],idx[:,1]
for i in tqdm(range(len(r))):
I2 = cv2.circle(I2,(c[i],r[i]), radius=1,color=(0,0,255),thickness=-3)
cv2.imshow("Result Image", I2)
cv2.imwrite("Result.jpg", I2)
cv2.waitKey()
to describe the problem of scale invariant of moravec algorithm, see image below, It can be seen that a corner can be detected as edge in a different scale.
