Detection and matching of object using proposed signature - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 496

of objects' signatures (saved ones and input). Firstly, as shown in Figure 1 , not all objects in
the example image are in the same size, orientation, or even shape, and afterwards, their data
should not be equal in length or characteristics. For that reason and more in checking accuracy,
some preprocesses of matching have carried out; one is sorting all the data of all signatures,
and then computing the variability in the dataset by calculating the average of the absolute
deviations of data points from their mean. The equation for average deviation is
(4)
For all i i's are the number of objects in the image and j i's are the number of object's signature
data, x ij represents the number of signature's data points, is their mean, and n i is the number
of signature data rows. Secondly, the results of Equation (4) have applied on all input and
saved objects to make a comparison between them for get the exact matching by least error.
Equation (5) introduces a method for calculate differences between the results of Equation (4) :
(5)
The components of Equation (5) are the absolute value of the difference between the two
results of Equation (4) related to saved and input object signatures. The decision of matching
based on the least value of DIF, which is given the exact matched object.
5 Experimental results
The experimental results are divided into two parts; one is representing the objects and their
signatures in images and the second is showing the results of matching and comparing a pro-
posed algorithm with SURF methods.
Figure 2 presents a sample of experimentally clear and unclear images, which contain some
standard geometrical shapes in (a), some kinds of objects varying in shapes, and luminance
intensity in (b)-(f). Sequentially, the signatures have been constructed for the most distinct
mentioned objects.