![]() ![]() 3)įig.3: Sobel threshold applied to the original image It’s working with an energy distribution matrix to differentiate the sensitive image information from the less sensitive ones. Typically in image processing the Sobel operator is used to detect image edges. We will use the Sobel filter operator in our case. ![]() Seam carving can support several types of energy functions such as gradient magnitude, entropy, sobel filter, each of them having something in common: it values a pixel by measuring its contrast with its neighboring pixels. ![]() Find the lowest cost seam from the energy matrix starting from the last row and remove it.For each pixel in a row we compute the energy of the current pixel plus the energy of one of the three possible pixels above it. Traverse the image from top to bottom (or from left to right in case of vertical resizing) and compute the minimum energy level.The minimum energy level is calculated by summing up the current pixel value with the lowest value of the neighboring pixels from the previous row.Traverse the image from the second row to the last row and compute the cumulative minimum energy for all possible connected seams for each entry.Using a dynamic programming approach the algorithm will generate individual seams crossing the image from top to down, or from left to right (depending on the horizontal or vertical resizing) and will allocate for each seam a custom value, the least important pixels having the lowest energy cost and the most important ones having the highest cost.The algorithm tries to find the least important parts of the image taking into account the lowest energy values.An energy map (edge detection) is generated from the provided image.Illustrates the process.įig.2: The seam carving method illustratedįirst let’s skim through the details and summarize the important steps. By successively removing or inserting seams we can reduce or enlarge the size of the image in both directions. It works by establishing a number of seams (a connected path of low energy pixels) crossing the image from top to down or from left to right defining the importance of pixels. ![]() Seam carving was developed typically for this kind of use cases. Scaling also is not sufficient since it is not aware of the image content and typically can be applied only uniformly. Also advanced cropping features like smart cropping cannot resolve our issue, since it will remove the person from the left margin or will crop a small part from the castle. Cropping is limited since it can only remove pixels from the image periphery. We have two options: either to crop it, or to scale it. Now suppose that we want to make it smaller. It’s a nice and clean picture with a wide open background. I'is pretty much based on the article Seam Carving for Content-Aware Image Resizing by Shai Avidan and Ariel Shamir. This is what Caire, my content aware image resizing library developed in Go is trying to remedy. Not even the smart cropping technique will help too much in this case. The normal image resize, but also the content cropping technique is not really suitable for this kind of task, since the first one will simply resize the image by preserving the aspect ratio and the last one will crop the image on the defined coordinate section, which might results in content loss, especially on photos with the relevant information scattered trough the image. Let's assume you want to resize an image without content distortion but also you wish to preserve the relevant image parts. ![]()
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