Image Annotation: Labeling Services

We label digital images for training your computer vision algorithms. There are three critical elements to our image annotation process; firstly, labeling objects in images, secondly, identifying features in images, and thirdly, outlining the boundaries of objects.

Image annotation can be used for a range of tasks which include detecting objects, segmenting pictures, and categorizing them.

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Steps in Image Annotation

Step 1

Collecting Images

Collecting the images that will be annotated and organizing them into folders.

Step 2

Labeling Images

Each image is labeled according to the activities and objects it contains.

Step 3

Verifying Accuracy

The labels are then verified for accuracy.

Step 4

Uploading Data

The data is uploaded to a database for use in machine learning models.

Types of Image Annotation for Computer Vision

2D Bounding Boxes

2D Bounding Boxes

It defines the boundaries of objects in a two-dimensional space using graphic representations. The boxes are usually used in computer vision and ML applications for separating areas of interest for objects.

Object Detection

Object Detection

This deals with detection of instances of semantic objects pertaining to a certain class (humans, buildings, or cars) with respect to digital images and videos.

Key Point Annotation

Key Point Annotation

The Key Point image data annotation recognizes facial gestures, human poses, expressions, emotions, body language, and sentiments through the connection of multiple dots.

Polygon Annotation

Polygon Annotation

This involves marking and drawing shapes on a digital image. It allows marking objects within an image based on their position and orientation. It involves labeling images of irregular dimensions.

3D Cuboid Annotation

3D Cuboid Annotation

This is used for detecting and recognizing 3D objects in images. It helps machines in determining the depth of objects like vehicles, people, buildings, and other objects.

Semantic Segmentation

Semantic Segmentation

A semantic segmentation technique is used in computer vision to segment images. An image dataset is semantically segmented to locate all categories and classes.

Image Classification

Image Classification

Images or objects are classified within images as per custom multi-level taxonomies like land use, crops, etc. It converts image data into image insights for AI and ML models.

Skeletal Annotation

Skeletal Annotation

This is used to highlight body movement and alignment. Annotators connect lines on the human body through this technique. They connect them with dots at points of articulation.

Image Annotation Use Cases

Our expertise in data annotation for over a decade has helped us in gaining a strong foothold. We believe that industries can benefit from our high-quality computer vision training data to automate their manufacturing, production, and other operations.

Autonomous Vehicles

Autonomous Vehicles

Providing the automobile industry with well-annotated training data to quickly deploy AI in cars for autonomous driving.

Medical

Medical

Leveraging image annotation techniques to improve clinical and surgical procedures with AI and ML integration.

Retail

Retail

Helping the retail and e-commerce industry with training data to straighten out their in-store operations through AI.

Security & Surveillance

Security & Surveillance

Enabling AI in cameras & sensors to detect risks at workplaces, airports, and industrial sites by embedding computer vision into security and surveillance systems.

Insurance

Insurance

Getting training data ready to incorporate AI in insurance processes for risk assessment, fraud detection, and reducing human error.

Agritech

Agritech

Assisting agriculture with computer vision training data for identifying product defects, sorting produce, managing livestock, capturing soil quality, applying fertilizer, and adjusting genetic conditions.

Outsource To Us

With us as your preferred partner for image annotation outsourcing, you will receive high-quality image annotations tailored to your industry use cases.

Quality on a Promise

Quality on a Promise

Our team is committed to delivering high-quality image annotations. Our training data is therefore tailored for the applications of our clients.

Uncompromised Data Security

Uncompromised Data Security

Data security and confidentiality are of utmost importance to us. At all points in the annotation process, our team ensures that no data breaches occur.

Scalable with Quick Turnaround Time

Scalable with Quick Turnaround Time

We at Cogito claim to have the necessary resources and infrastructure to provide image annotation services on any scale while promising quality and timeliness.

Flexible Pricing

Flexible Pricing

Besides offering flexible pricing, we can tailor our services to suit your budget and training data requirements with our pay-as-you-go pricing model.

Get Us On Board

Let us contribute the best of our data annotation expertise to your computer vision-based models and AI algorithms.

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