Emulating human eyes in computer framework
RADIUS is emulating human visual perception in efficient algorithms and software to extract meaningful features at multiple scales from imagery.
Contact
- David Seigel
- Technology Transfer
- (505) 665-2743
Emulating human eyes in computer framework
Applications:
- Geospatial image analysis
- Content-based image search and retrieval
- Radiograph analysis for DHS and proliferation detection
- Scalable image rendering, graphics, and compression
Benefits:
- Scalable, distributed algorithm (laptops to clusters)
- Rapid image segmentation
- Linear scaling with image size
- Vector representation of images
- Features related in hierarchical segmentation by inclusion
- Features available as polygons for shape recognition
Sensors, sensors, everywhere... we face a deluge of digital image data.
- We are looking, but are we seeing?
- Sensing power has outstripped processing power, leaving analysts with a glut of data to process.
- Can computers help analysts see what they are looking for? Can they mimic how an analyst searches?
At Los Alamos National Laboratory (LANL), project RADIUS is emulating human visual perception in efficient algorithms and software to extract meaningful features at multiple scales from imagery.
The goal: Bring analysis capability in line with sensing capability and provide broad area search support.
Under RADIUS, LANL has developed computational framework for structural representation of images that uses polygons instead of pixels.
The RADIUS framework provides:
- data reduction from millions of pixels, representing image colors, down to thousands of polygons, representing image features:
a. Pixel image
d. Color sampling
b. Edge detection
e. Perceptual graph
c. Delaunay triangulation
f. Polygonal regions
- a hierarchical scheme for extracting multiscale features by successive grouping of polygons based on structural, spectral, and statistical attributes:
- a graph-based, traversable image representation in terms of polygonal features and their adjacencies to enable contextual characterization and recognition of features and scenarios.
High Performance Implementation
Average of ~15 megapixels/sec on an 8 core 2.66Ghz Intel Xeon with 32 GB of RAM
Scalable implementation from desktops to supercomputers
- Accelerating algorithms using advanced architectures
- Many-core support for demanding applications
LANL is seeking partners to enhance/apply the capabilities of RADIUS
Required:
Potential partners must
- Provide targeted applications of interest to the image-user community at large
- Have qualified staff
- Have the ability to enter into a CRADA (or other appropriate contractual agreement) with LANL.
Preferred:
A potential partner who can
- Identify applications for RADIUS in the GIS and other workflow arenas
- Identify and collaborate on future R&D funding opportunities
- Has the capability to further develop the RADIUS technology into a commercial product or service.
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