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Polygon Annotation Services
//Image annotation
POLYGON ANNOTATION
Polygon annotation is a technique used in computer vision, image processing, and machine learning to label and annotate regions of interest within images using polygonal shapes. Instead of bounding boxes or keypoints, polygon annotation delineates objects or areas of interest with more precise boundaries represented by interconnected vertices. This technique is particularly useful for annotating irregularly shaped objects, regions, or boundaries where bounding boxes may not accurately represent the object’s contours.
// POLYGON ANNOTATION
KEY ASPECTS OF POLYGON ANNOTATION
Annotation Process
In polygon annotation, annotators manually or semi-automatically draw polygonal shapes around objects or regions of interest within images. These polygons are defined by a series of vertices connected by straight lines, encapsulating the object's boundaries or delineating specific areas within the image.
Semantic Segmentation
Polygon annotation is commonly used in semantic segmentation tasks, where the goal is to label each pixel in an image with the corresponding object category or class. Each polygon corresponds to a specific object or region class, and pixels within the polygon are assigned the corresponding class label. This enables fine-grained object segmentation and pixel-level understanding of image content.
Instance Segmentation
In instance segmentation, where the goal is to segment individual instances of objects within an image, polygon annotation is used to delineate the boundaries of each object instance. Each object instance is represented by a separate polygon, allowing for precise instance-level segmentation and recognition.
Object Tracking and Motion Analysis
Polygon annotation can be used in object tracking and motion analysis applications to track the movement and trajectory of objects over time. By annotating objects with polygons in consecutive frames of a video sequence, object tracking algorithms can accurately follow object motion and analyze dynamic interactions between objects.
//Industries
USE CASES FOR POLYGON ANNOTATION
RETAIL
Assisting the retail and e-commerce sectors by providing training data to optimize their in-store operations through the implementation of artificial intelligence (AI).ROBOTICS
3D object detection finds extensive application in robotics, particularly to prevent collisions with dynamic entities such as humans, animals, and other objects.AGRICULTURE
Supporting agriculture through computer vision training data involves facilitating the identification of product defects, sorting produce, managing livestock, assessing soil quality, implementing fertilizer applications, and fine-tuning genetic conditions.INSURANCE
Preparing training data to integrate AI into insurance procedures for tasks such as risk assessment, fraud detection, underwriting and minimizing human error.HEALTHCARE
Incorporating annotations and accurate labeling within AI systems is crucial for uncovering connections within genetic codesand enhancing efficiency in healthcare processes.SECURITY & SURVEILLANCE
Facilitating the integration of AI into cameras and sensors enables the detection of potential risks at workplaces, airports, and industrial sites. This involves incorporating computer vision technology into security and surveillance systems.SELF-DRIVING
Bounding boxes serve to annotate the surroundings of a vehicle, aiding in the detection of various objects including pedestrians, vehicles, traffic signs, and barriers.LOGISTICS
Logistics represents one of the growing areas of artificial intelligence application. We specialize in annotating images of goods to generate high-quality training data utilized in logistics.AUTONOMOUS FLYING
Simplifying and broadening access to AI implementations for automated or assisted flight can be achieved by leveraging image annotation conducted at the backend using training data specifically tailored for autonomous flying.// Drop us a line! We are here to answer your queries