Doxygen tutorials: python basic

doc/py_tutorials/py_feature2d/py_features_meaning/py_features_meaning.markdown

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+Understanding Features {#tutorial_py_features_meaning}
+======================
+
+Goal
+----
+
+In this chapter, we will just try to understand what are features, why are they important, why
+corners are important etc.
+
+Explanation
+-----------
+
+Most of you will have played the jigsaw puzzle games. You get a lot of small pieces of a images,
+where you need to assemble them correctly to form a big real image. **The question is, how you do
+it?** What about the projecting the same theory to a computer program so that computer can play
+jigsaw puzzles? If the computer can play jigsaw puzzles, why can't we give a lot of real-life images
+of a good natural scenery to computer and tell it to stitch all those images to a big single image?
+If the computer can stitch several natural images to one, what about giving a lot of pictures of a
+building or any structure and tell computer to create a 3D model out of it?
+
+Well, the questions and imaginations continue. But it all depends on the most basic question: How do
+you play jigsaw puzzles? How do you arrange lots of scrambled image pieces into a big single image?
+How can you stitch a lot of natural images to a single image?
+
+The answer is, we are looking for specific patterns or specific features which are unique, which can
+be easily tracked, which can be easily compared. If we go for a definition of such a feature, we may
+find it difficult to express it in words, but we know what are they. If some one asks you to point
+out one good feature which can be compared across several images, you can point out one. That is
+why, even small children can simply play these games. We search for these features in an image, we
+find them, we find the same features in other images, we align them. That's it. (In jigsaw puzzle,
+we look more into continuity of different images). All these abilities are present in us inherently.
+
+So our one basic question expands to more in number, but becomes more specific. **What are these
+features?**. *(The answer should be understandable to a computer also.)*
+
+Well, it is difficult to say how humans find these features. It is already programmed in our brain.
+But if we look deep into some pictures and search for different patterns, we will find something
+interesting. For example, take below image:
+
+![image](images/feature_building.jpg)
+
+Image is very simple. At the top of image, six small image patches are given. Question for you is to
+find the exact location of these patches in the original image. How many correct results you can
+find ?
+
+A and B are flat surfaces, and they are spread in a lot of area. It is difficult to find the exact
+location of these patches.
+
+C and D are much more simpler. They are edges of the building. You can find an approximate location,
+but exact location is still difficult. It is because, along the edge, it is same everywhere. Normal
+to the edge, it is different. So edge is a much better feature compared to flat area, but not good
+enough (It is good in jigsaw puzzle for comparing continuity of edges).
+
+Finally, E and F are some corners of the building. And they can be easily found out. Because at
+corners, wherever you move this patch, it will look different. So they can be considered as a good
+feature. So now we move into more simpler (and widely used image) for better understanding.
+
+![image](images/feature_simple.png)
+
+Just like above, blue patch is flat area and difficult to find and track. Wherever you move the blue
+patch, it looks the same. For black patch, it is an edge. If you move it in vertical direction (i.e.
+along the gradient) it changes. Put along the edge (parallel to edge), it looks the same. And for
+red patch, it is a corner. Wherever you move the patch, it looks different, means it is unique. So
+basically, corners are considered to be good features in an image. (Not just corners, in some cases
+blobs are considered good features).
+
+So now we answered our question, "what are these features?". But next question arises. How do we
+find them? Or how do we find the corners?. That also we answered in an intuitive way, i.e., look for
+the regions in images which have maximum variation when moved (by a small amount) in all regions
+around it. This would be projected into computer language in coming chapters. So finding these image
+features is called **Feature Detection**.
+
+So we found the features in image (Assume you did it). Once you found it, you should find the same
+in the other images. What we do? We take a region around the feature, we explain it in our own
+words, like "upper part is blue sky, lower part is building region, on that building there are some
+glasses etc" and you search for the same area in other images. Basically, you are describing the
+feature. Similar way, computer also should describe the region around the feature so that it can
+find it in other images. So called description is called **Feature Description**. Once you have the
+features and its description, you can find same features in all images and align them, stitch them
+or do whatever you want.
+
+So in this module, we are looking to different algorithms in OpenCV to find features, describe them,
+match them etc.
+
+Additional Resources
+--------------------
+
+Exercises
+---------