Up till now, we have generated a scale space and used the scale space to calculate the Difference of Gaussians. Those are then used to calculate Laplacian of Gaussian approximations that is scale invariant. I told you that they produce great key points. Here’s how it’s done!

Finding key points is a two part process

  1. Locate maxima/minima in DoG images
  2. Find subpixel maxima/minima

Locate maxima/minima in DoG images

The first step is to coarsely locate the maxima and minima. This is simple. You iterate through each pixel and check all it’s neighbours. The check is done within the current image, and also the one above and below it. Something like this:

X marks the current pixel. The green circles mark the neighbours. This way, a total of 26 checks are made. X is marked as a “key point” if it is the greatest or least of all 26 neighbours.

Usually, a non-maxima or non-minima position won’t have to go through all 26 checks. A few initial checks will usually sufficient to discard it.

Note that keypoints are not detected in the lowermost and topmost scales. There simply aren’t enough neighbours to do the comparison. So simply skip them!

Once this is done, the marked points are the approximate maxima and minima. They are “approximate” because the maxima/minima almost never lies exactly on a pixel. It lies somewhere between the pixel. But we simply cannot access data “between” pixels. So, we must mathematically locate the subpixel location.

Here’s what I mean:

The red crosses mark pixels in the image. But the actual extreme point is the green one.

Find subpixel maxima/minima

Using the available pixel data, subpixel values are generated. This is done by the Taylor expansion of the image around the approximate key point.

Mathematically, it’s like this:

We can easily find the extreme points of this equation (differentiate and equate to zero). On solving, we’ll get subpixel key point locations. These subpixel values increase chances of matching and stability of the algorithm.


Here’s a result I got from the example image I’ve been using till now:

The author of SIFT recommends generating two such extrema images. So, you need exactly 4 DoG images. To generate 4 DoG images, you need 5 Gaussian blurred images. Hence the 5 level of blurs in each octave.

In the image, I’ve shown just one octave. This is done for all octaves. Also, this image just shows the first part of keypoint detection. The Taylor series part has been skipped.


Here, we detected the maxima and minima in the DoG images generated in the previous step. This is done by comparing neighbouring pixels in the current scale, the scale “above” and the scale “below”.

Next, we’ll reject some keypoints detected here. This is because they either don’t have enough contrast or they lie on an edge

Got questions or suggestions? Leave a comment below!

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In the previous step , we created the scale space of the image. The idea was to blur an image progressively, shrink it, blur the small image progressively and so on. Now we use those blurred images to generate another set of images, the Difference of Gaussians (DoG). These DoG images are a great for finding out interesting key points in the image.

Laplacian of Gaussian

The Laplacian of Gaussian (LoG) operation goes like this. You take an image, and blur it a little. And then, you calculate second order derivatives on it (or, the “laplacian”). This locates edges and corners on the image. These edges and corners are good for finding keypoints.

But the second order derivative is extremely sensitive to noise. The blur smoothes it out the noise and stabilizes the second order derivative.

The problem is, calculating all those second order derivatives is computationally intensive. So we cheat a bit.

The Con

To generate Laplacian of Guassian images quickly, we use the scale space. We calculate the difference between two consecutive scales. Or, the Difference of Gaussians. Here’s how:

These Difference of Gaussian images are approximately equivalent to the Laplacian of Gaussian. And we’ve replaced a computationally intensive process with a simple subtraction (fast and efficient). Awesome!

These DoG images comes with another little goodie. These approximations are also “scale invariant”. What does that mean?

The Benefits

Just the Laplacian of Gaussian images aren’t great. They are not scale invariant. That is, they depend on the amount of blur you do. This is because of the Gaussian expression. (Don’t panic ;) )

See the σ2 in the demonimator? That’s the scale. If we somehow get rid of it, we’ll have true scale independence. So, if the laplacian of a gaussian is represented like this:

Then the scale invariant laplacian of gaussian would look like this:

But all these complexities are taken care of by the Difference of Gaussian operation. The resultant images after the DoG operation are already multiplied by the σ2. Great eh!

Oh! And it has also been proved that this scale invariant thingy produces much better trackable points! Even better!

Side effects

You can’t have benefits without side effects >.<

You know the DoG result is multiplied with σ2. But it’s also multiplied by another number. That number is (k-1). This is the k we discussed in the previous step.

But we’ll just be looking for the location of the maximums and minimums in the images. We’ll never check the actual values at those locations. So, this additional factor won’t be a problem to us. (Even if you multiply throughout by some constant, the maxima and minima stay at the same location)


Here’s a gigantic image to demonstrate how this difference of Gaussians works.

In the image, I’ve done the subtraction for just one octave. The same thing is done for all octaves. This generates DoG images of multiple sizes.


Two consecutive images in an octave are picked and one is subtracted from the other. Then the next consecutive pair is taken, and the process repeats. This is done for all octaves. The resulting images are an approximation of scale invariant laplacian of gaussian (which is good for detecting keypoints). There are a few “drawbacks” due to the approximation, but they won’t affect the algorithm.

Next, we’ll actually find some interesting keypoints. Maxima and Minima. Or, Maximums and Minimums of the image.

Got any questions or suggestions? Leave a comment below!


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Real world objects are meaningful only at a certain scale. You might see a sugar cube perfectly on a table. But if looking at the entire milky way, then it simply does not exist. This multi-scale nature of objects is quite common in nature. And a scale space attempts to replicate this concept on digital images.

Scale spaces

Do you want to look at a leaf or the entire tree? If it’s a tree, get rid of some detail from the image (like the leaves, twigs, etc) intentionally.

While getting rid of these details, you must ensure that you do not introduce new false details. The only way to do that is with the Gaussian Blur (it was proved mathematically, under several reasonable assumptions).

So to create a scale space, you take the original image and generate progressively blurred out images. Here’s an example:

Look at how the cat’s helmet loses detail. So do it’s whiskers.

Scale spaces in SIFT

SIFT takes scale spaces to the next level. You take the original image, and generate progressively blurred out images. Then, you resize the original image to half size. And you generate blurred out images again. And you keep repeating.

Here’s what it would look like in SIFT:

Images of the same size (vertical) form an octave. Above are four octaves. Each octave has 5 images. The individual images are formed because of the increasing “scale” (the amount of blur).

The technical details

Now that you know things the intuitive way, I’ll get into a few technical details.

Octaves and Scales

The number of octaves and scale depends on the size of the original image. While programming SIFT, you’ll have to decide for yourself how many octaves and scales you want. However, the creator of SIFT suggests that 4 octaves and 5 blur levels are ideal for the algorithm.

The first octave

If the original image is doubled in size and antialiased a bit (by blurring it) then the algorithm produces more four times more keypoints. The more the keypoints, the better!


Mathematically, “blurring” is referred to as the convolution of the gaussian operator and the image. Gaussian blur has a particular expression or “operator” that is applied to each pixel. What results is the blurred image.

The symbols:

  • L is a blurred image
  • G is the Gaussian Blur operator
  • I is an image
  • x,y are the location coordinates
  • σ is the “scale” parameter. Think of it as the amount of blur. Greater the value, greater the blur.
  • The * is the convolution operation in x and y. It “applies” gaussian blur G onto the image I.

This is the actual Gaussian Blur operator.

Amount of blurring

The amount of blurring in each image is important. It goes like this. Assume the amount of blur in a particular image is σ. Then, the amount of blur in the next image will be k*σ. Here k is whatever constant you choose.

This is a table of σ’s for my current example. See how each σ differs by a factor sqrt(2) from the previous one.


In the first step of SIFT, you generate several octaves of the original image. Each octave’s image size is half the previous one. Within an octave, images are progressively blurred using the Gaussian Blur operator.

In the next step, we’ll use all these octaves to generate Difference of Gaussian images.


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Matching features across different images in a common problem in computer vision. When all images are similar in nature (same scale, orientation, etc) simple corner detectors can work. But when you have images of different scales and rotations, you need to use the Scale Invariant Feature Transform.

Why care about SIFT

SIFT isn’t just scale invariant. You can change the following, and still get good results:

  • Scale (duh)
  • Rotation
  • Illumination
  • Viewpoint

Here’s an example. We’re looking for these:

And we want to find these objects in this scene:

Here’s the result:

Now that’s some real robust image matching going on. The big rectangles mark matched images. The smaller squares are for individual features in those regions. Note how the big rectangles are skewed. They follow the orientation and perspective of the object in the scene.

The algorithm

SIFT is quite an involved algorithm. It has a lot going on and can become confusing, So I’ve split up the entire algorithm into multiple parts. Here’s an outline of what happens in SIFT.

  1. Constructing a scale space
    This is the initial preparation. You create internal representations of the original image to ensure scale invariance. This is done by generating a “scale space”.
  2. LoG Approximation
    The Laplacian of Gaussian is great for finding interesting points (or key points) in an image. But it’s computationally expensive. So we cheat and approximate it using the representation created earlier.
  3. Finding keypoints
    With the super fast approximation, we now try to find key points. These are maxima and minima in the Difference of Gaussian image we calculate in step 2
  4. Get rid of bad key points
    Edges and low contrast regions are bad keypoints. Eliminating these makes the algorithm efficient and robust. A technique similar to the Harris Corner Detector is used here.
  5. Assigning an orientation to the keypoints
    An orientation is calculated for each key point. Any further calculations are done relative to this orientation. This effectively cancels out the effect of orientation, making it rotation invariant.
  6. Generate SIFT features
    Finally, with scale and rotation invariance in place, one more representation is generated. This helps uniquely identify features. Lets say you have 50,000 features. With this representation, you can easily identify the feature you’re looking for (say, a particular eye, or a sign board).

That was an overview of the entire algorithm. Over the next few days, I’ll go through each step in detail. Finally, I’ll show you how to implement SIFT in OpenCV!

What do I do with SIFT features?

After you run through the algorithm, you’ll have SIFT features for your image. Once you have these, you can do whatever you want.

Track images, detect and identify objects (which can be partly hidden as well), or whatever you can think of. We’ll get into this later as well.

But the catch is, this algorithm is patented.


So, it’s good enough for academic purposes. But if you’re looking to make something commercial, look for something else! [Thanks to aLu for pointing out SURF is patented too]

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