Search-and-replace editing for personal photo collections

We propose a new system for editing personal photo collections, inspired by search-and-replace editing for text. In our system, local edits specified by the user in a single photo (e.g., using the “clone brush” tool) can be propagated automatically to other photos in the same collection, by matching the edited region across photos. To achieve this, we build on tools from computer vision for image matching. Our experimental results on real photo collections demonstrate the feasibility and potential benefits of our approach.Natural Sciences and Engineering Research Council of Canada Postdoctoral FellowshipMassachusetts Institute of Technology. Undergraduate Research Opportunities ProgramNational Science Foundation (U.S.) (CAREER award 0447561)T-Party ProjectUnited States. National Geospatial-Intelligence Agency (NGA NEGI-1582- 04-0004)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-06-1-0734)Microsoft ResearchAlfred P. Sloan Foundatio

History Assisted View Authoring for 3D Models

3D modelers often wish to showcase their models for sharing or review purposes. This may consist of generating static viewpoints of the model or authoring animated flythroughs. Manually creating such views is often tedious and few automatic methods are designed to interactively assist the modelers with the view authoring process. We present a view authoring assistance system that supports the creation of informative view points, view paths, and view surfaces, allowing modelers to author the interactive navigation experience of a model. The key concept of our implementation is to analyze the model’s workflow history, to infer important regions of the model and representative viewpoints of those areas. An evaluation indicated that the viewpoints generated by our algorithm are comparable to those manually selected by the modeler. In addition, participants of a user study found our system easy to use and effective for authoring viewpoint summaries. Author Keywords 3D model; editing history; viewpoint authorin

Crowdsourcing step-by-step information extraction to enhance existing how-to videos

Millions of learners today use how-to videos to master new skills in a variety of domains. But browsing such videos is often tedious and inefficient because video player interfaces are not optimized for the unique step-by-step structure of such videos. This research aims to improve the learning experience of existing how-to videos with step-by-step annotations. We first performed a formative study to verify that annotations are actually useful to learners. We created ToolScape, an interactive video player that displays step descriptions and intermediate result thumbnails in the video timeline. Learners in our study performed better and gained more self-efficacy using ToolScape versus a traditional video player. To add the needed step annotations to existing how-to videos at scale, we introduce a novel crowdsourcing workflow. It extracts step-by-step structure from an existing video, including step times, descriptions, and before and after images. We introduce the Find-Verify-Expand design pattern for temporal and visual annotation, which applies clustering, text processing, and visual analysis algorithms to merge crowd output. The workflow does not rely on domain-specific customization, works on top of existing videos, and recruits untrained crowd workers. We evaluated the workflow with Mechanical Turk, using 75 cooking, makeup, and Photoshop videos on YouTube. Results show that our workflow can extract steps with a quality comparable to that of trained annotators across all three domains with 77% precision and 81% recall