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In Machine Learning, image annotation is a model of integrating a metadata to image classification using text or annotation tools. Computer Vision models used at Avistos allow our image annotation tools to identify and classify images. They are primarily used for face recognition, anti-spoofing, counting people, activity tracking and automatic license plate recognition purposes. Image mapping to associated text is thereafter loaded into datasets. Image annotation tools consolidate and transform old image using OCR capabilities of our models. The Machine Learning models are trained using Open - image datasets, thereafter refining accurancy during testing. Industry specific models are developed and already in use by various companies globally. Various image annotation models are pre-defined and based on your requirement are suggested.
Bounding Box 2D/3D that we provide as a service identifies a given image based on your dataset and creates a box around it.
Image annotation that allows various objects to be identified based on 2D and 3D images. It primarily focus on tagging using cuboids.
An extremely accurate form of image annotation. This technique is ideally used in autonomous driving and flying sectors. It segregates objects that are otherwise obstructed.
Linking pixels of an image at a micro level is termed as semantic segmentation. The objects are variable and this form is utilized by all industries. There is a bifurcation on each class/specific identity. The two types include: Instance Segmentation and Standard Segmentation.
Polyline annotation services are primarily used in autonomous driving. With Self-driving cars gaining popularity across the glove, artificial intelligence models are successfully being used to identify street lane lines across.
Keypoint and Skeletal Annotation tools are used to recognize the framework of automative parts as well as human features.
