If one ignores both issues however, a greedy approximation for parallel seam carving is possible. The map holds a "nth seam" number for each pixel on the image, and can be used later for size adjustment. Avidan 2007 computes all seams by removing each seam iteratively and storing an "index map" to record all the seams generated. However, the problem of making multiple seams at the same time is harder for two reasons: the energy needs to be regenerated for each removal for correctness and simply tracing back multiple seams can form overlaps. The calculation of the DP array can also be parallelized with some interprocess communication. The energy calculation is trivially parallelized for simple functions. The value in black represents the cumulative sum of energies leading up to and including that pixel.įor the lowest energies we have at the end, work back up the minimals to recover the seam with minimal energy. Each square represents a pixel, with the top-left value in red representing the energy value of that pixel. The images below depict a DP process to compute one optimal seam. If attempting to compute a vertical seam (path) of lowest energy, 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. Dynamic programming can be used to compute seams. Dynamic programming ĭynamic programming is a programming method that stores the results of sub-calculations in order to simplify calculating a more complex result. This can be done via Dijkstra's algorithm, dynamic programming, greedy algorithm or graph cuts among others. Computing seams Ĭomputing a seam consists of finding a path of minimum energy cost from one end of the image to another. It is also possible to invert step 4 so the algorithm enlarges in one dimension by copying a low energy seam and averaging its pixels with its neighbors. The seams to remove depends only on the dimension (height or width) one wants to shrink. Seams can be calculated via the dynamic programming approach below. Seams are ranked by energy, with low energy seams being of least importance to the content of the image. Here we use gradient magnitude.ģ) From the energy, make a list of seams. This can be done by various algorithms: gradient magnitude, entropy, visual saliency, eye-gaze movement. The below example describes the process of seam carving:Ģ) Calculate the weight/density/energy of each pixel. The importance/energy function values a pixel by measuring its contrast with its neighbor pixels. A horizontal seam is similar with the exception of the connection being from left to right. A vertical seam is a path of pixels connected from top to bottom in an image with one pixel in each row. Seams can be either vertical or horizontal. Image Retargeting was invented by Vidya Setlur, Saeko Takage, Ramesh Raskar, Michael Gleicher and Bruce Gooch in 2005. The purpose of the algorithm is image retargeting, which is the problem of displaying images without distortion on media of various sizes (cell phones, projection screens) using document standards, like HTML, that already support dynamic changes in page layout and text but not images. Seam carving also allows manually defining areas in which pixels may not be modified, and features the ability to remove whole objects from photographs. It functions by establishing a number of seams (paths of least importance) in an image and automatically removes seams to reduce image size or inserts seams to extend it. Seam carving (or liquid rescaling) is an algorithm for content-aware image resizing, developed by Shai Avidan, of Mitsubishi Electric Research Laboratories (MERL), and Ariel Shamir, of the Interdisciplinary Center and MERL. Cropping is undesirable because part of the castle is removed. Original image to be made narrower Scaling is undesirable because the castle is distorted.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |