Specified Object Contour Segmentation Dataset

Project Overview:

Objective

Our ambitious project aims to create a groundbreaking dataset for accurately outlining object contours. This is crucial for advancing computer vision technology, especially in areas like AR/VR, image editing, and robotics. Moreover, this initiative underscores our commitment to offering varied datasets for training machine learning models, covering images, videos, texts, and speech.

Scope

Our team embarked on the task of gathering a diverse array of images showcasing various objects. Then, we meticulously outlined the contours to mark object boundaries with precision. This process significantly enhanced the dataset’s practical utility. Moreover, this approach ensures the dataset’s versatility across a wide range of real-world applications. As a result, our efforts have made the dataset more valuable and accessible for various scenarios.

Specified Object Contour Segmentation Dataset
Specified Object Contour Segmentation Dataset
Specified Object Contour Segmentation Dataset
Specified Object Contour Segmentation Dataset

Sources

  • Objects in household settings (e.g., furniture, appliances).
  • Outdoor items (e.g., vehicles, trees, street furniture).
  • Objects under varying light conditions.
  • Objects with different textures and complexities.
Specified Object Contour Segmentation Dataset
Specified Object Contour Segmentation Dataset

Data Collection Metrics

  • Household Objects: 30,000 images
  • Outdoor Items: 25,000 images
  • Varying Light Conditions: 20,000 images
  • Different Textures: 25,000 images
  • Additional Random Volume: 10,000 images

Annotation Process

Stages

  1. Total Contour Pixels Annotated: 220 million (approximately)
  2. Complex Contour Pixels (with intricate shapes or patterns): 77 million
  3. Simpler Contour Pixels: 143 million

Annotation Metrics

  • Total Contour Pixels Annotated: 200 million (approximation)
  • Complex Contour Pixels (with intricate shapes or patterns): 70 million
  • Simpler Contour Pixels: 130 million
  • Images with Pest and Disease Annotations: 15,000
  • Metadata Logging: 15,000
Specified Object Contour Segmentation Dataset
Specified Object Contour Segmentation Dataset
Specified Object Contour Segmentation Dataset
Specified Object Contour Segmentation Dataset

Quality Assurance

Stages

Enhancing Contour Detection: By harnessing advanced algorithms, we fortify our process with automated cross-checks on manual annotations.
Rigorous Expert Evaluation: Our dedicated team of contour detection specialists meticulously examines intricate objects.
Ensuring Consistency: Multiple experts collaborate on identical image sets to guarantee uniformity and precision. 

QA Metrics

  • Annotations Reviewed by Experts: 4 million pixels (2% of total contour annotations)
  • Inconsistencies Identified and Rectified: 800,000 pixels (0.4% of total contour annotations)

Conclusion

The Specified Object Contour Segmentation Dataset sets a new standard in detailed object boundary detection. Thanks to careful data collection, annotation, and validation processes, this dataset is a crucial resource for advancing computer vision. Additionally, it serves as a cornerstone for leading-edge research in this field.

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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