Detection and matching of object using proposed signature - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 498

input object has been completely different in its position and orientation; however, the pro-
posed matching algorithm is overcome that and succeeded. In fact, all other objects have been
matched by the same way and have achieved 100% for that image.
Table 1
The Matching Process of Objects' Signatures
Table 2 presents error values for another image in the matching process based on objects'
signatures, which are applied on unclear image in Figure 2(d) . As in Table 1 , all cell's values
represent the DIF of Equation (5) , and the least value indicates to exact match of objects in an
image and the input one. Clearly, as seen one mismatching is found in second row column
two; however, this mismatching is acceptable because the objects in second and third columns
are so close to each other in shape.
Table 2
The Matching Process of Objects' Signatures
As the same way in Tables 1 and 2 , all other objects have been selected based on their signa-
tures and have achieved 96% in the matching process. On the other hand, by applying SURF
on the same image with different input objects, some mismatching is found if the input ob-
ject has changed in his position or orientation; even so, this mismatching has not happened
with the proposed algorithm under the same constraints. Figure 4 illustrates SURF Work in
an example for this mismatching with the second object in second column of Table 1 by 100
strongest feature points.
FIGURE 4 The SURF example with the mismatch an object. (a) The original image; (b) the
detected object; (c) the mismatch with the exact object in the image.