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Make Object Detection Easier with Image Annotation for Machine Learning

Have you used Google Lens in a while? If yes, then you must be knowing that it lets you find information about random products and objects. One simply needs to point their device camera onto a keyboard, mouse, or speakers, and Google Lens will be able to tell the model, make, and manufacturer of the device.

Users can also point it to a location or a building and get all the general details about it in real time. Students can find solutions for their mathematical problems by simply scanning them. You can also track packages, convert handwritten notes into text, and do much more with the device camera without any interface whatsoever.

Take another example: When you upload an image on your profile on Meta (formerly known as Facebook), it automatically detects, identifies, and tags the faces of your friends and family. All such wonders are possible via Computer Vision technology that is simplifying complex tasks, elevates people’s lifestyles, and makes their lives easier.

 

Getting the Basics Right

Simply put, Computer Vision is like giving vision to computers through which they can detect and identify objects in their environment. It is about making sense of real-world elements that it sees through its camera.

And, getting to the point we are at today wasn’t linear. What makes the Google Lens instantly detect an image and gauge everything that is there on the World Wide Web is the years of evolution and training. The success of Computer Vision technology comes down to what is known as image annotation for Machine Learning—the fundamental process behind this revolutionizing technology, which enables the devices to take intelligent actions and ideal decisions.

In practice, image annotation is the process of adding tags and labels to the input datasets that are to be fed into the Machine Learning algorithms. These labeled images carrying additional information like metadata and attributes help the AI/ML models to detect and identify objects easily. After years of rigorous and continuous training, the smart models can perform the desired tasks.

 

Image Annotation Techniques

To get started with the image labeling process, businesses need tools and software that offer specific features and functionalities in sync with the project’s requirements. Apart from these, there are certain image annotation techniques that are universal. Some of these are elucidated here:

 

  • Bounding Boxes

 The bounding box is one of the most basic image annotation techniques where a box is drawn around an object to attribute different object-specific details. This technique is ideal for annotating symmetrical objects. And, just as the bounding box suffices 2D needs, cuboids are its 3D variant.

Simply put, 2D bounding boxes provide the details of an object’s length and breadth. But the cuboid gives details about the depth of the object as well. And if an object is only partially visible, annotating images using 3D cuboids becomes more taxing. As a way out, data annotators approximate an object’s edges and corners using existing visuals and information.

 

  • Polygons

 Objects in images need not be symmetrical or regular always. There are dozens of instances where one finds them to be irregular or just random. So, what is to be done here? The annotators deploy the polygon technique to precisely annotate irregular shapes and objects in such cases. In this, dots are placed across an object’s dimensions and lines are drawn manually along the circumference or perimeter of the object.

 

  • Landmarking

 Landmarking image annotation technique is used to define the intricacies in the movements of objects in an image or video footage. It is specifically used for facial recognition to annotate facial gestures, features, postures, expressions, and so on. Also used to annotate small objects, it involves individually identifying different facial features and their attributes for more accurate and reliable results.

Take the real-world example where landmarking helps—think of the Snapchat or Instagram filters that accurately place goggles, hats, and other such funny elements based on your facial expressions and features. So, the next time you pose for a dog filter, let’s say, know that the application has landmarked your facial features to give precise results.

 

  • Lines

 Apart from polygons and other basic shapes, simple lines are also used for annotating different objects in images. Machines can seamlessly identify the boundaries of the objects annotated with this technique. For example, lines are drawn across driving lanes for autonomous vehicles to better understand the boundaries within which they must maneuver. This technique is also used to train such systems for diverse circumstances and scenarios as well as help them make better driving decisions.

Though there are several commercially available image annotation software, applications, and tools that let you modify them for the specific use case, they have certain limitations. Besides, they cannot be relied upon for bulk operations. Given the resource-intensive and time-consuming nature of the task, it is better left to professionals or you can engage in image annotation services.

 

Leveraging the Right Approach

 You must be thinking of getting an in-house setup to label images, but image annotation outsourcing is more profitable in the longer run. It is a cost-effective option, wherein businesses get accurately annotated datasets within the stipulated time to fuel their Computer Vision models. Hence, to accelerate your AI/ML project implementation, you must begin by finding a suitable partner.

 

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