Computer vision engineers automate various functions that the human visual system can do. It consists of methods for acquiring, analyzing, processing and understanding digital images. The programming languages widely used by computer vision engineers for image processing include Matlab, Python, Java and C++. There are wide-ranging subfields for this skill set. Applications can be from machine vision systems to artificial intelligence (AI) and robots with modeling, segmentation, tracking and detection. Computer vision engineers also design, construct and examine software systems for VR (Virtual Reality) and AR (Augmented Reality) experiences. They can observe image data in various forms, such as multi-dimensional and video sequences for medical technologies. These specialized software engineers have an end goal, which is to interpret the contents of an image from various platforms.
This interdisciplinary field usually requires a bachelor's or a master's degree in computer science, computer engineering, machine learning or other related studies. Computer vision engineers will have knowledge of OpenCV, as well as linear algebra and calculus. They use Convolutional Neural Networks (CNNs) and Deep Learning for classifying and interpreting an image. Computer vision engineers are aware of vector quantization and clustering to build search engines and systems, as well as Principal Component Analysis (PCA) and other machine learning models for facial recognition. They're familiar with a range of visual system hardware, such as a structured-light 3D scanner, a hyperspectral imager and LIDAR (Light Imaging, Detection and Ranging). Computer vision engineers also keep track of the latest advancements in their field.
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