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Lines & Splines Annotation
//Video Annotation
Lines & Splines Annotation
Lines and splines annotation involves marking and defining lines or curves within images or datasets for various purposes, including computer vision, image processing, and geometric modeling.
APPLICATIONS OF LINES AND SPLINES ANNOTATION
Object Detection and Localization
In computer vision tasks, lines annotation involves marking and labeling lines representing objects or features of interest within images. For example, annotating lane markings on roads, edges of objects, or boundaries of regions in satellite imagery. Similarly, splines annotation can be used to annotate curved objects or features within images. For instance, annotating the outlines of roads, rivers, or boundaries using spline curves for more accurate representation.
Geometric Modeling and CAD
In computer-aided design (CAD) and geometric modeling, lines annotation is used to define geometric shapes and structures. Engineers and designers annotate lines to represent edges, profiles, or dimensions of components in 2D sketches or technical drawings. Splines annotation is crucial in CAD for defining smooth curves and surfaces. Designers annotate spline curves to create aesthetically pleasing and functionally accurate shapes for products, architectural designs, or industrial components
Medical Imaging
In medical imaging, lines annotation can be used for tasks such as annotating blood vessels, nerve pathways, or anatomical landmarks within images. Lines annotation helps medical professionals visualize and analyze structures for diagnosis and treatment planning. Splines annotation is employed for delineating complex anatomical structures or organ boundaries in medical images. Annotating splines enables precise segmentation and quantification of volumes, shapes, and contours for medical research or patient-specific modeling.
//Industries
USE CASES FOR 2D BOUNDING BOXES
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