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Recent project
Project: BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video (WACV'23)
Customer: Deva Ramanan
Professor, Robotics Institute, Carnegie Mellon University
BURST is a dataset/benchmark for object segmentation in video. It contains a total of 2,914 videos with pixel precise segmentation masks for 16,809 unique object tracks (600,000 per-frame masks) spanning 482 object classes. It is based on the exisiting TAO dataset which contains box-level annotations which we extended to pixel-precise masks.
Image annotation techniques
Image annotation involves the process of labeling and annotating objects, regions, or features within an image to provide contextual information. It typically includes techniques such as bounding box annotation, semantic segmentation, polygon annotation, and landmark annotation. Annotators carefully outline and mark the desired elements in the image, enabling machine learning algorithms to recognize and understand them.
Image segmentation
Image segmentation is a fundamental task in computer vision that involves dividing an image into meaningful and distinct regions or segments. Unlike bounding boxes, which provide a rectangular boundary around objects, image segmentation aims to assign a specific label or class to every pixel within an image.
Bounding Box
Bounding boxes provide crucial information for algorithms to identify, analyze, and manipulate objects within a given scene, enabling machines to understand and interact with visual data more effectively. By encapsulating objects within a bounding box, computer vision systems can extract valuable features and make accurate predictions and decisions based on the detected objects' spatial information. By segmenting an image, it becomes possible to extract valuable information, such as object boundaries, shapes, textures, and spatial contexts, which can be utilized for further analysis, decision-making, or visual understanding tasks.
Testimonials
Tarasha Khurana
Ph.D. student at The Robotics Institute, Carnegie Mellon University
AnnotateX is a treat to work with. We were so impressed by the quality and precision of the BURST dataset annotations that we started another collaboration with AnnotateX on a one-of-a-kind dataset, the annotations of which do not have a precedent set. Even then Berin made sure that we got the most competitive pricing and ensured strict quality control. He and his team continue to help us iterate over the annotations, whenever needed. If I had another dataset to curate, I would go to AnnotateX again in a heartbeat!
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- Email: berin@annotatex.com