Connelly Barnes will present his preFPO on Wednesday October 13 at 3PM in Room 302 (note room!). The members of his committee are: Adam Finkelstein, advisor; Szymon Rusinkiewicz and Dan Goldman (Adobe), readers; Tom Funkhouser and Bob Sedgewick, nonreaders. Everyone is invited to attend his talk. His abstract follows below. ---------------------------- PatchMatch: A Fast Randomized Matching Algorithm with Applications to Images, Videos, and 3D Volumes Abstract: This thesis presents a novel fast randomized matching algorithm for finding correspondences between small local regions of signals. We also explore a wide variety of applications of this new fast randomized matching technique. The core matching algorithm, which we call PatchMatch, can find similar regions or "patches" of an image one to two orders of magnitude faster than previous techniques. The algorithm requires only very loose assumptions that neighboring correspondences tend to be similar or "coherent" in order to quickly converge to an approximate solution. Our subsequent research has shown that the matching algorithm is quite generic, and can be extended to work on video summarization, computer vision problems, collections of images (ongoing work), and 3D volumes (ongoing work). Our fully generalized algorithm works on a variety of 2D and 3D signals in quite disparate application domains, gaining speed-ups over alternative techniques in a number of areas. We have explored many applications of this matching algorithm. In computer graphics, we have explored removing unwanted objects from images, seamlessly moving objects in images, changing image aspect ratios, and video summarization. In computer vision we have explored denoising images, object detection, detecting image forgeries, and detecting symmetries. In our ongoing work we plan to also apply our algorithm to collections of images, and producing hybrids from a set of input exemplar meshes. We finally will discuss how the statistics of the inputs relate to convergence of our algorithm, the limitations of our algorithm, and areas for future research.