Multiple Objects Matting Dataset

Project Overview:

Objective

Develop a rich dataset tailored for the task of matting multiple objects within diverse scenarios. This dataset would be instrumental in enhancing technologies such as video editing software, augmented reality (AR) applications, and real-time visual effects production.

Scope

Curate images showcasing multiple objects, both animate and inanimate, across various backgrounds and settings. Every image will possess high-quality alpha mattes for each object, enabling precise foreground extraction.

Multiple Objects Matting Dataset
Multiple Objects Matting Dataset
Multiple Objects Matting Dataset
Standard OCR dataset

Sources

  • Engaged in collaborative partnerships with photographers and film production houses, leading to a meticulously collected and successfully curated array of visual content.
  • Meticulously collected crowdsourced images from global contributors have ensured a wide variety of objects and backgrounds in the dataset.
  • Captured in controlled environments to include specific and challenging matting scenarios, such as fine hair against contrasting backgrounds, resulting in a successfully collected and thoughtfully curated visual dataset.
  • Public datasets have been carefully refined and expanded upon, contributing to a successfully collected and professionally curated set of resources.
Multiple Objects Matting Dataset
Multiple Objects Matting Dataset

Data Collection Metrics

  • Total Images: 35,000
  • Urban & Natural Landscapes: 15,000
  • Indoor Settings: 10,000
  • Controlled Environments: 7,000
  • Miscellaneous: 3,000

Annotation Process

Stages

  1. Image Pre-processing: Standardization for resolution, clarity, and brightness.
  2. Alpha Matte Generation: Expert annotators will produce high-resolution alpha mattes for every distinct object in the images, leveraging cutting-edge matting tools.
  3. Validation: Dual-layered validation involving automated algorithms and human reviewers ensures the integrity of the mattes.

Annotation Metrics

  • Total Alpha Mattes: 100,000+ (Given multiple objects per image)
  • Average Annotation Time per Image: 40 minutes (Due to the intricacy of creating detailed mattes for multiple objects)
Multiple Objects Matting Dataset
Multiple Objects Matting Dataset
Water Bottle Image Classification Dataset
Multiple Objects Matting Dataset

Quality Assurance

Stages

Automated Checks: Proprietary matting algorithms evaluate the mattes for potential errors or artifacts.
Peer Review: A subset of images and their associated mattes undergo thorough peer scrutiny.
Inter-annotator Agreement: Complex images are assigned to multiple annotators, ensuring a unanimous understanding of matting nuances.

QA Metrics

  • Mattes Verified using Algorithms: 17,500 (50% of total images)
  • Peer-reviewed Mattes: 10,500 (30% of total images)
  • Inconsistencies Identified and Amended: 350 (1% of total images)

Conclusion

The Multiple Objects Matting Dataset represents a pioneering endeavor in the realm of digital media editing and AR. By providing high-fidelity mattes for numerous objects within varied scenarios, it equips AI models to swiftly and accurately extract foreground elements, paving the way for innovative visual projects and tools.

quality dataset

Quality Data Creation

Guaranteed TAT‚Äč

Guaranteed TAT

ISO 9001:2015, ISO/IEC 27001:2013 Certified‚Äč

ISO 9001:2015, ISO/IEC 27001:2013 Certified

HIPAA Compliance‚Äč

HIPAA Compliance

GDPR Compliance‚Äč

GDPR Compliance

Compliance and Security‚Äč

Compliance and Security

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