Let’s see how these techniques will help us in using image recognition. These techniques are discussed in detail so we will have a clear vision of them.
Photo-matching Techniques
This technique is to match photos with each other. It is the simplest method of image recognition. Two photos that are of the same object are compared with each other.
The similarity of the photos with each other is by the process of cross-correlation. This process helps us to measure how much two images resemble each other.
In this method, we need the two images to recognize an object in one image from another image.
To compare two images, we need to use a cross-correlation technique. In this technique, a certain amount of a gray-scale image is less than a second gray-scale image.
Then a sum of these values is taken to get a correlation value. This correlation value will help us in determining how similar these two images are.
Correlation values can be positive or negative values or zero values can. Also, be obtained if there is no similarity between these images.
This technique works best when the difference between two images is small. There are limited colors used in these images.
If there are many colors used in these images. Thus it will be very difficult for this technique to work because there will be a lot of false matches between these images.
Patch-matching Techniques
This technique is to compare different areas or patches in two images. It is very easy to implement this technique compared to other techniques.
As it uses small pieces of images to compare them with each other. Thus it becomes very easy to implement this technique.
For example, we can compare the image of the arm with the image of the shirt. Or can be any part like shoulder or hand or any part of the body.
This means that we do not need to use entire images for this process. We can use any part of these images to identify an object in this image.
This technique is fast and efficient when compared to other techniques. Thus it becomes popular among these image recognition techniques.
There are two types of patch-matching techniques available. They are global and local patch-matching techniques. Both these techniques are discussed below.
3. Global Patch-Matching Techniques
The global patch-matching technique is when an entire image is for matching purposes with another. The entire image divides into parts called patches of tiles according to their size and shape.
So that we can have a good spatial resolution. This means that the amount of information in each tile should be high enough.
So that it will be easy for us to measure the distance. Between objects in each tile with high accuracy. This way, if two objects are found far away from each other.
Then it will be easy for us to identify them.
In conclusion
Thus this technique is used when we need to match or recognize an entire scene from another scene.
To use this technique, we need to have very large storage space. It is because these images will be large. After all, they are of the entire image.
