Computation Attention Machine Vision Projects
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Feng-GUI additional projects in the field of Computation Attention and Machine Vision
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Computation Attention Machine Vision Projects
- 2. Embedded • Complexity = (width*height)*12 • 1280x720 frame takes =~ 100ms • 320x240 frame takes =~ 10ms • Memory Peak = (width*height)*1rgb(byte) • Program Footprint = 10kb 1.5 GHz quad-core ARM Cortex-A9
- 3. FG SAAS vs. FG Embedded
- 4. Scene Analysis • Test the embedded version inside a Canon camera. • Analyze detected ROIs to adjust factors as focus and exposure accordingly. • Similar to auto-area AF & Subject Tracking Canon CHDK
- 5. Retinex Retina/Cortex Example: MultiScale Retinex with Contrast Restoration
- 6. Photoshop filter Fovea retinex filter
- 7. Fovea filter - original
- 8. Fovea filter – Photoshop AC
- 9. Fovea filter - Contrast
- 10. Viewing Retargeting mobile content adaptation. crop and resize the photo leaving most important part of the photo. benefits: automatic content adaptation services. Automatic Thumbnails
- 14. Automatic Thumbnails visual search improves by 25%
- 15. Interaction Enhanced navigation inside images on handheld devices
- 16. Dynamic Ads & creative optimization (DCO)
- 17. miscellaneous Manufacture: Automatic defect localization in machine vision DSP: Automatic event detection in audio signals. Medical: Automatic pathology detection and localization in medical imaging. Surveillance: Efficiently tracking patterns changes in natural scenes.
- 18. miscellaneous Compression: Region-based coding approach: lower JPEG compression rates were used for important areas Medical: Attention Deficit/Hyperactivity Disorder (AD/HD)
Hinweis der Redaktion
- Automatic Thumbnails for photos galleries
- * Automatic Thumbnail View: Instead of simply shrinking the entire original image, AutoThumbnails crops out less informative regions and keeps the most informative part of the image before shrinking. It displays the remaining rectangle, which contains all of the attention objects from the original image, as the thumbnail. * Automatic Zooming-in: This feature allows the viewer to go directly to the most informative part of an image while maintaining an appropriate display ratio. It selects the optimal region by finding the region that contains the most attention objects as possible under the display constraint. * Automatic Navigation: AutoThumbnails offers an optimal browsing path based on the image attention model. It automatically steers the viewing window through this path to show the important parts of the image.
- Region-based coding approach: lower JPEG compression rates were used for important areas