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What Are The Applications of Image Annotation in Machine Learning and AI?


At the time of developing the AI models through machine learning (ML) first and most important thing you need, relevant training data sets, which can only help the algorithms understand the scenario through new data or seeing the objects and predict when used in real-life making various tasks autonomous. In the visual perception based AI model, you need images, containing the objects that we see in our real life. And to make the object of interest recognizable to such models the images need to be annotated with the right techniques. And image annotation is the process, used to create such annotated images. The applications of image annotation in machine learning and AI is substantial in terms of model success.

Machine Learning Training Data Annotation Types for AI in News & Media


AI in media making this industry operate with more automated tasks for better efficiency in the market. Using the computer vision or NLP/NLU, AI in news media makes the objects and languages recognition system possible for machines. Cogito provides the training data sets for AI in media and news to develop the visual perception based AI model or language based machine learning models. Media industry can well-utilize the power of face recognition system to detect the various types of faces captured into the images or videos while reporting or covering the important topics around the world. The landmark annotation technique is used to detect or recognize such faces through AI.

Image Annotation Types For Computer Vision And Its Use Cases


There are many types of image annotations for computer vision out there, and each one of these annotation techniques has different applications. Are you curious about what you can accomplish with these various annotation techniques? Let's take a look at the different annotation methods used for computer vision applications, along with some unique use cases for these different computer vision annotation types. Before we dive into use cases for computer vision image annotation, we need to be acquainted with the different image annotation methods themselves. Let's analyze the most common image annotation techniques.

LIDAR Sensor in Autonomous Vehicles: Why it is Important for Self-Driving Cars?


Sensor-based technologies are playing a key role in making artificial intelligence (AI) possible in various fields. LiDAR is one of the most promising sensor-based technology, used in autonomous vehicles or self-driving cars and became essential for such autonomous machines to get aware of its surroundings and drive properly without any collision risks. Autonomous vehicles already use various sensors and LiDAR is one of them that helps to detect the objects in-depth. So, right here we will discuss LiDAR technology, how it works, and why it is important for autonomous vehicles or self-driving cars. LIDAR stands for Light Detection and Ranging is a kind of remote sensing technology using the light in the form of a pulsed laser to measure ranges (variable distances) to the Earth.

Image Semantic Segmentation Annotation Service - Data Enrichment Services Outsourcing Training Data for AI


Semantic segmentation in image annotation makes the objects recognizable to machines in a single class. Cogito is providing image semantic segmentation service to annotate the multiple types of objects in a single image to make is identifiable to computer vision based perception model like self-driving car or autonomous vehicle. Semantic segmentation is available for AI and machine learning models needs to better understand the scenario and recognize the object visible into their surroundings. It helps visual perception models to visualize the objects visible on road and segment them from each other to differentiate from each other using different colors. Cogito is expert in image annotation services providing the full-range semantic segmentation service using the best tool and system for various types of autonomous machines like self-driving cars and drones or autonomous flying objects to get the areal imagery view for crop and fields monitoring.