What is Computer Vision?

Computer vision is a scientific field that focuses on extracting useful information from visual data such as images and videos. In recent years, machine learning has revolutionized computer vision, enabling significant advancements across various industries and research domains. Here, we outline the key tasks tackled within computer vision.

Image segmentation is a fundamental problem in computer vision, where the goal is to differentiate objects in the foreground from the background. This involves assigning a mask to each pixel in the image, indicating whether it belongs to an object of interest or not. Semantic segmentation takes this further by identifying regions of the same category, while instance-level segmentation aims to identify individual objects within the image.

Another approach to object detection is through bounding boxes. Here, the model predicts the minimum box that encloses an object in the visual data.

In addition to localizing objects, computer vision tasks often involve classifying the identified regions. This requires the model to determine the category to which a highlighted region belongs.

Object tracking extends object detection to video data, where objects are tracked frame by frame. Although object tracking is less precise than bounding box predictions, it provides valuable information about an object's trajectory.

ezML's platform empowers users to easily perform these computer vision tasks with their own custom projects. The platform offers a Quickstart guide for getting started on a project and showcases various Use Cases to demonstrate practical examples.