Data annotation is the process of labeling the data available in various formats like text, video, or images, etc. For supervised machines, learning labeled data sets are required, so that machine can easily and clearly understand the input patterns. We provide two types of annotation services.
Image Annotation is the process of labeling an image, which strategically involves human-powered work and sometimes, computer-assisted help. It is an important step in creating Computer Vision models for tasks like image segmentation, image classification, and object detection. Just like human beings need information about the environment that surrounds them, computers need meta information to learn to recognize and label all of the various objects that surround them as well. So in order to help computers recognize, annotation is used. Image annotation outsourcing companies perform all of the tedious and time-consuming processes with the understanding that their work is absolutely critical to the overall success of the project.
Video annotation is a process of labeling or tagging video clips that are used for training Computer Vision models to detect or identify objects. Video annotation helps to extract intelligence from videos by annotating objects frame-by-frame and making them recognizable to Machine Learning models. Our annotation experts work with top annotation tools to give the computer the best vision, our expert annotators perform tasks with accuracy and quickly with their expertise and we try to give our customers as best as we could.
Image Annotations
This project is to collect photo image data from the Internet with one or multiple persons (partially occluded or un-occluded), and then label the 6 types of objects as defined in Annex A (person and associated body parts such as head, hand, arm, leg) appearing on the images with a bounding box.
Tool Used:
labeling
Technical Specifications:
Content of data should be photo images downloaded from the Internet or captured by cameras. The images should be photos captured from real scenes, not drawings or paintings, with no copyright infringements.
The objects must be real-world objects (not toys, drawings, or paintings)
The images shall not contain offensive content.
No Grayscale images.
The background of the objects should not be plain or without background.
Image must be clear without blurring or noticeable compression artifacts
A total of 12,500 Man-made / Natural disasters or disaster-like scenarios (e.g. earthquake scenes, building collapse scenes, fire scenes, accident scenes).
6250 images contain at least one person. (at least 3750 images CAN BE partially occluded person
6250 images contain 2 or more people.
Project Description:
1) Label multiple segmentation regions for 3D images provided by Arise.
2) Number of segmentation regions varies depending on image types. All image types and corresponding segmentation regions are defined in Annex A.
3) Some segmentation regions have underlying shapes while some other regions have arbitrary shapes. Special attention must be paid on border areas where two segmentation regions meet.
4) Total of 100 different 3D images are provided by Arise for multi-class labeling.
5) There are 3 different image types as defined in Annex A. Each image type has multiple segmentation classes respectively as also defined in Annex A.
6) Labelling of 3D images takes place on multiple 2D planner projections of 3D images. There are three 2D planner projections as in Annex B. All 2D views must be individually labelled to get 3D segmentation regions
This project is to collect photo image data of person from Internet with one or multiple persons (partially occluded or un-occluded), and then label the 6 types of objects as defined in Annex A (person and associated body parts such as head, hand, arm, leg) appearing on the images with a bounding box.
Tool Used:
LabelImg
Technical Specifications:
Content of data should be photo images downloaded from the Internet or captured by cameras. The images should be photos captured from real scenes, not drawings or paintings, with no copyright infringements.
The objects must be real-world objects (not toys, drawings or paintings)
The images shall not contain offensive content.
No Grayscale images.
The background of the objects should not be plain or without background.
Image must be clear without blurring or noticeable compression artifacts
A total of 12,500 Man-made / Natural Pictures
