Specified Object Contour Segmentation Dataset
Project Overview:
Objective
Our objective was to gather an extensive and detailed medical dataset, prioritizing accuracy, breadth, and ethical compliance. Moreover, this dataset, along with the Specified Object Contour Segmentation Dataset, is tailored to support research and development in machine learning and healthcare technologies. Additionally, to build a dataset of OCR images and their corresponding transcriptions in Arabic for training and evaluating OCR and text recognition systems capable of accurately converting scanned or handwritten Arabic text into digital text.
Scope
Our team embarked on the task of gathering a diverse array of images showcasing various objects. Subsequently, we meticulously outlined the contours to mark object boundaries with precision. This process, in turn, significantly enhanced the dataset’s practical utility. Moreover, this approach ensures the dataset’s versatility across a wide range of real-world applications. Consequently, our efforts have made the dataset more valuable and accessible for various scenarios.
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.
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
- Total Contour Pixels Annotated: 220 million (approximately)
- Complex Contour Pixels (with intricate shapes or patterns): 77 million
- 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
Quality Assurance
Stages
Enhancing Contour Detection: By harnessing advanced algorithms, we further fortify our process with automated cross-checks on manual annotations.
Rigorous Expert Evaluation: Moreover, through rigorous expert evaluation, our dedicated team of contour detection specialists meticulously examines intricate objects.
Ensuring Consistency: Furthermore, to ensure 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. Consequently, thanks to careful data collection, annotation, and validation processes, this dataset is a crucial resource for advancing computer vision. Furthermore, it serves as a cornerstone for leading-edge research in this field.
Quality Data Creation
Guaranteed TAT
ISO 9001:2015, ISO/IEC 27001:2013 Certified
HIPAA Compliance
GDPR Compliance
Compliance and Security
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