Creating High-Quality Image Matting Datasets
Deep learning is the optimal solution for image matting, with its effectiveness depending heavily on learning from provided examples. The quality and diversity of the training dataset significantly influence the accuracy of the model and reliability.
Setup Process
Use a tripod to stabilize the camera and set it to full manual mode. This includes fixing the focus, aperture, and shutter speed to ensure consistency across shots.
Process Steps
First Shot
Place the object, which serves as the foreground subject, in the scene and take a photo.
Second Shot
Position a solid chroma backdrop behind the object. The chosen chroma color should be distinctly different from any colors present in the foreground subject to facilitate easier separation.
Background Removal
The post-processing stage involves using image editing software to eliminate the chroma color and extract the alpha channel.
Additional Examples
Benefits: Enhanced Accuracy and Realism
- Complex Settings: Our method enables the generation of training examples that accurately represent complex settings.
- High Accuracy: By preserving minute details, the alpha matte produced is of superior quality.
- Naturalness Over Compositing: Unlike image compositing techniques, our method maintains the natural characteristics of the scene, including shadows and distortions, thus providing a more realistic outcome.
Contact for Purchase
We are selling the dataset we created with this methodology. If you are interested in purchasing the dataset, please fill out the form below.