What is Computer Vision?
Computer Vision enables AI models to automatically identify significant data points from visual content found in picture and video data as well as physical environments. This technology enables operations through the following functions:
Facial Recognition
Autonomous Vehicles:
Retail AI
Medical Imaging AI
The achievement of these results requires data annotation with high-quality standards. The expert team at Logictive Solutions optimizes the training process of AI models to help businesses develop effective and precise CV applications.
Our Computer Vision Annotation Services
Keypoint Annotation
Keypoint annotation involves marking specific points on an object, like joints on a human body or corners on a face. It's widely used in pose estimation and facial recognition tasks.
These points help train models to understand shapes, motions, and spatial relationships between features, making it essential for applications like activity recognition and gesture tracking.
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Industries Benefiting from NLP Solutions
Insurance Tech
Finance Tech
Autonomous Vehicles
Health Tech
Agri Tech
Retail/E-commerce
Geospatial
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Frequently Asked Questions
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Annotation plays a key role in training computer vision models by providing labeled examples the AI can learn from. It helps the model understand what to look for in images or videos by linking visual data with meaningful tags.
Annotation provides the ground truth needed to teach AI models how to identify and interpret visual patterns. Without accurately labeled data, the models cannot learn effectively or perform tasks like object detection or image classification.
- Bounding Boxes: Rectangular boxes to identify object locations.
- Polygon Annotation: Precise outlines for irregular shapes.
- Keypoint Annotation: Marks specific points (e.g., facial landmarks).
- Image Segmentation: Pixel-level labeling for detailed analysis.
- Classification Labels: Categorizes entire images or regions.
Annotation is typically done by trained data labelers or annotation specialists using specialized tools. In some cases, it may be partially automated and then manually verified for accuracy.
- Healthcare: Medical imaging analysis (e.g., tumor detection).
- Automotive: Self-driving cars (object recognition).
- Agriculture: Crop monitoring and disease detection.
- Security: Facial recognition and surveillance.
- Retail: Inventory management and customer behavior analysis.