Salient Object: Matting Trimap (512px)
A three-level matting trimap (black = background, grey = unknown edge band, white = foreground) for the salient robot: the standard auxiliary input for alpha-matting methods.
Specifications
- Width
- 512
- Height
- 512
- Mode
- L
- Levels
- 0 bg / 128 unknown / 255 fg
- Role
- trimap
- Alt Text
- A three-level matting trimap (black = background, grey = unknown edge band, white = foreground) for the salient robot: the standard auxiliary input for alpha-matting methods
- Alt Text Source
- description
Testing contract
Expected to pass- Scenario
- Exercise Salient Object: Matting Trimap (512px) in its ai segmentation workflow. A three-level matting trimap (black = background, grey = unknown edge band, white = foreground) for the salient robot: the standard auxiliary input for alpha-matting methods.
- Expected result
- Decoded geometry is 512×512 pixels in L mode; frame count is 1. Declared feature checks: mode=L; levels=0 bg / 128 unknown / 255 fg; role=trimap.
What is a .png file?
PNG (Portable Network Graphics) is a raster image format using lossless DEFLATE compression. It supports full 8- or 16-bit-per-channel truecolor, palette, and greyscale modes with an optional alpha channel, but no animation. It is the standard choice for screenshots, logos, and graphics with sharp edges or transparency.
How to use this file
Use an example PNG to test image decoders, alpha-compositing, thumbnail generators, and format converters, or to verify that a pipeline preserves transparency and color depth on round-trip.
How to use this file for testing
“Salient Object: Matting Trimap (512px)” is a deterministic Testaroo fixture for Image matting, Image segmentation. Subject images paired with soft-alpha mattes and trimaps (fur, mesh, glass edges), for testing image-matting models and alpha extraction against a known ground truth.
Documented properties for this file: 512×512 · trimap. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
For image AI or enhancement tools, run the model or filter on this file and diff against the clean or ground-truth companion in the same group when available. Keep seeds and documented damage parameters in your evaluation notes so regressions are attributable.
Use the RGB input with its mask or soft-alpha companion. Class IDs and edge kinds are documented in specs, score IoU or boundary error against that ground truth, not a hand label.
Code examples
<img src="salient-trimap.png" alt="Example image" width="640" loading="lazy">Generated by generation/images_ai.py. Free for any use, no attribution required, license.
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