Specified Object Contour Segmentation Dataset

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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.