I.e., the pixels close to the centre become more important to The height of the plot corresponds to the weight given to the underlying pixel Imagine that plot laid over the kernel for the Gaussian blur filter. That a larger kernel will blur the image more than a smaller kernel.Ĭonsider this plot of the two-dimensional Gaussian function: Larger kernels have more values factored into the average, and this implies The averaging is done on a channel-by-channel basis,Īnd the average channel values become the new value for the pixel in Increasing values of σ² in either dimension increases the amount of blurring in that dimension. The mean μ is always 0, and represents the middle of the 2D kernel. In fact, when using Gaussian functions in Gaussian blurring, we use a 2D Gaussian function to account for X and Y dimensions, but the same rules apply. The mean determines the central point of the bell curve on the x axis, and the variance describes the spread of the curve. The shape of the function is described by a mean value μ, and a variance value σ². The rate at which this weight diminishes is determined by a Gaussian function, hence the nameĪ Gaussian function maps random variables into a normal distribution or “Bell Curve”. Given more weight than those far away from the centre. In a Gaussian blur, the pixels nearest the centre of the kernel are To apply the kernel to the current pixel,Īn average of the the colour values of the pixels surrounding it is calculated, In the example shown above, the kernel is square, with a dimension of seven pixels. So that the pixel being worked on is always in its centre. The width and height of the kernel must be an odd number, That moves along with the pixel being worked on by the filter. Of the same dimensions as the rectangular group of pixels in the image, The kernel is another group of pixels (a separate matrix / small image), When we apply a filter, we consider rectangular groups of pixels surrounding In this example, the pixel we are currently working on is highlighted in red, When we apply a filter, we consider each pixel in the image, one at a time. Now, zoom in on the area of the cat’s eye, as shown in the left-hand image below. In particular the area of the image outlined by the white square. On convolution with an image, a big, blobby kernel will retainīig, blobby, low spatial frequency features. The kernel can be thought of as a little image in itself,Īnd will favour features of a similar size and shape in the main image. Is a small matrix which is combined with the image usingĭifferent sizes, shapes and contents of kernel produce different effects. KernelsĪ kernel can be used to implement a filter on an image. This could be interpreted quite broadly in the context of image analysis -Īnything that reduces or distorts the detail of an image might apply.Īpplying a low pass filter, which removes detail occurring at high spatial frequencies,Ī Gaussian blur is a filter that makes use of a Gaussian kernel. Blurringīlurring is to make something less clear or distinct. Maybe a couple of big features per image. Refer to high and low spatial frequencies in the image.ĭetails associated with high spatial frequencies are small,Ī lot of these features would fit across an image.įeatures associated with low spatial frequencies are large. This is a good analogy for image filters.Ī high-pass filter will retain the smaller details in an image,Ī low-pass filter retains the larger features,Īnalogous to what’s left behind by a physical filter mesh. We have physical filters which separate out objects by size.Ī filter with small holes allows only small objects through, So we will focus on just one here, the Gaussian blur. There are several different blurring functions in the skimage.filters module, The effect is to average out rapid changes in pixel intensity.Ī blur is a very common operation we need to perform before other tasks such as We make the colour transition from one side of an edge in the image to another Where the background of the image ends and the object begins. One example of an edge is the pixels that represent Similar pixels in the image to another different group. Is that of edges: the lines that represent a transition from one group of by counting them, measuring their sizes, etc.Īn important concept associated with the identification of objects in an image Represented within it so that we can perform some further analysis of these When processing an image, we are often interested in identifying objects In this episode, we will learn how to use skimage functions to blur images. Explain why applying a low-pass blurring filter to an image is beneficial.Īpply a Gaussian blur filter to an image using skimage.
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