Vision Workbench
The NASA Vision Workbench (VW) is a modular, extensible, cross-platform computer vision software framework written in C++. It was designed to support a variety of space exploration tasks, including automated science
and engineering analysis, robot perception, and 2D/3D environment reconstruction, though it can also serve as a general-purpose image processing and machine vision framework in other contexts as well.
The VW was developed within the Autonomous Systems and Robotics area of the Inteligent Systems Division at NASA's Ames Research Center. It leverages the Intelligent Robotics Group's (IRG) extensive experience developing surface reconstruction and tools for planetary exploration---e.g. the Mars Pathfinder and Mars Exploration Rover missions---and rover autonomy. It has also been developed in collaboration with the Adaptive Control and Evolvable Systems (ACES) group, and draws on their experience developing computer vision techniques for autonomous vehicle control systems.
The VW is composed of several modules each of which provides a separate C++ library. The core library provides the basic image and pixel data types as well as a range of fundamental image processing operations. The other modules provided in this release are:
- Math: geometric, numeric, and other mathematical types and functions
- GPU: accelerated image processing using commodity graphics hardware
- HDR: creating, processing, and compressing high dynamic range images
- InterestPoint: Detecting, tracking, and matching interest points
- Mosaic: compositing, blending, and manipulating 2D image mosaics
- Camera: camera models and related types and functions
- Cartography: tools for manipulating geospatially-referenced images
Software
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Vision Workbench
Vision Workbench (VW) is a modular, extensible computer vision framework developed by the NASA Ames Autonomous Systems and Robotics Area (Intelligent Systems Division). VW is implemented in C++ and makes extensive use of templates and generative programming techniques. To date, VW has been used to develop a wide range of NASA computer vision applications including: 2-D panorama creation from gigapixel data sets, 3-D terrain modeling using orbital images, high-dynamic-range images, and texture-based image content matching and retrieval.
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