Techniques We Use for Data Annotation
By applying these data annotation techniques, we ensure accuracy and maintain quality control throughout the AI training data development process.
Bounding Boxes
3D Cuboid Annotation
Landmark Annotation
This technique allows us to identify specific points on natural objects, such as faces, to assess emotional characteristics. A key application of this is in highly targeted marketing campaigns that deliver personalized customer experiences.
Skeletal Annotation
Using skeletal annotation, we create extensive datasets of annotated skeletal images to train machine learning algorithms, resulting in more efficient and accurate medical diagnoses.
Polygon Annotation
With polygon annotation, we draw precise outlines around objects in videos and images, making it easier to identify and locate specific products.
Semantic Segmentation
Through semantic segmentation, we address the visual limitations of legacy computers that struggle with unprocessed 2D image data, allowing users to gain a more detailed understanding of the content within the image.
3D Point Cloud Annotation
Training models using 3D point cloud annotation enhances the accuracy and detail in object detection and classification, providing customers with more precise and reliable measurements of dimensional attributes, leading to improved performance and better decision-making capabilities.
Polyline Annotation
By employing polyline annotation, we enable autonomous cars and other mobile technologies to effectively detect and follow lane markings on roadways, improving their safety and reliability.
Ready for the next step?
Contact our Customer Success team to get started.