Introduction:
Image annotation is the process of labeling images to provide context and meaning for computer vision models. It’s crucial for training AI systems to recognize and interpret visual information. Without data annotation, machines wouldn’t be able to understand the content of images.
Types of Eye Image Annotation:
Several annotation types are employed for eye images, each serving a specific purpose:
• Bounding Boxes: Simple rectangular boxes drawn around the entire eye region. Useful for tasks like detecting the presence of eyes in an image.
• Polygons: More precise outlines that closely follow the shape of the eye. Provide more detailed information about the eye’s contour.
• Keypoints: Marking specific points on the eye, such as pupil center, corners of the eye, and eyelid boundaries. Essential for applications like eye tracking and gaze estimation.
• Semantic Segmentation: Pixel-level annotation that labels each pixel within the eye region with a specific class (e.g., iris, pupil, sclera). Enables fine-grained analysis of eye structures.
Applications of Eye Image Annotation:
Annotated eye images are used in a wide range of applications:
• Facial Recognition: Eye features play a crucial role in identifying individuals.
• Eye Tracking: Understanding eye movements is essential for applications like human-computer interaction and assistive technologies.
• Medical Diagnosis: Eye image annotation helps in diagnosing eye diseases like glaucoma, cataracts, and diabetic retinopathy.
• Driver Monitoring Systems: Detecting driver drowsiness or distraction based on eye behavior.