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Content-Aware Image Resizing
An anonymous reader writes "At the SIGGRAPH 2007 conference in San Diego, two Israeli professors, Shai Avidan and Ariel Shamir, have demonstrated a new method to shrink images. The method is called 'Seam Carving for Content-Aware Image Resizing' (PDF paper here) and it figures out which parts of an image are less significant. This makes it possible to change the aspect ratio of an image without making the content look skewed or stretched out. There is a video demonstration up on YouTube."
I think you're missing the point of their method, which is to provide realistic images during rescaling that aren't corrupted by blind interpolation (equal averaging). In downscaling the images, it preserves parts of the images that would lose their information through downscaling (e.g. complex textures, people), while at the same time removing textures that would not lose information through downscaling (sky, water, sand). The sky, water and sand will still look like sky water and sand whether it's at 1/4 or 10x resolution, people however look much different if you try and downscale them or upscale them(they would appear blurry and hard to distinguish). The same works in reverse. The sky is still going to look like the sky whether you scale it to 10x or 5x-- it would still look natural. Tree's on the other hand, would not. Once you start to scale up the trees you would expect to be seeing different characteristics-- leaves, branches, etc. Any type of scaling up of a tree would make it seem very blurry and unnatural (lacking leaves, branches, etc.)-- you cannot create an additional information that isn't present in the original image. Therefore, the most natural looking image would be to increase the sky.
It's not perfect of course. I'm guessing that if you had a picture of two people next to each other, one with a solid colored shirt, and the other with a striped colored shirt, that the solid colored shirt guy would get skinner than the striped when shrinking, and the reverse when enlarging. However, it's a neat idea, and I look forward to reading the paper.
I think you've got it except for a small detail in the "Remove objects", which the narrator alludes to around timestamp 4:01 of the video. You might want to add:
Step 6: Extend image to match original size using the previous extend image algorithm
(Of course, I leave the obligatory Profit step as an exercise for the reader).
It has nothing to do with edge detection. The algorithm simply detects paths of minimal gradient which lead from one side of the image to the opposite side. This can be used to produce a "pretty picture" which shows the edges -- but this is merely fallout.
They showed what I thought were several realistic photos with complex backgrounds, and the algorithm did well overall, except on structures where people are closely attuned to exact detail -- such as human faces. If we weren't innately wired to process faces in incredible detail, we wouldn't even notice the distortion.
So it's not perfect. Can you show me something in this world that is? And I don't think there has been any mention of "prime time" application, whatever that means.
Your comment seems to be similar to the headline on tabloids.. Just because a technology could be used for negative purposes does not mean that it should not be developed.. If your reasoning was used, we should have all been living in caves by now..
By your reasoning
Cars can be used by criminals to travel faster.
A knife can be used to kill
Electricity can be used to kill
Computers can be used by the govt to collect more information abt us effectively
Is that really what we want?
see the flaw in the logic?