How do you make the move to Computer Vision? It’s one of the most exciting areas of Deep Tech today, and there’s plenty of opportunity lying in wait for those wishing to take the leap.
At DeepRec.ai, we’ve connected many a Computer Vision Scientist with growth-enabled opportunities over the years, and we know what to watch out for when it comes to narrowing down a role.
If you’re eyeing a move into the wonderful world of Computer Vision, it's worth running a self-assessment – Here’s what a typical Computer Vision Scientist takes care of:
Research and Development
A Computer Vision Scientist is generally responsible for large portion of R&D in any given role. Computer Vision is a constantly evolving space, and so too are the tools, technologies, and techniques used to pilot progress. A heavy R&D focus is a given. A Computer Vision Scientist will investigate data to develop new algorithms and solve complex issues.
Image Processing
One of the key ingredients of computer vision, image processing is a fundamental part of the Computer Vision Scientists day-to-day. These techniques include image resizing, filtering, noise removal, edge detection, image normalisation, and more. These steps help to improve the quality and reliability of the data used for subsequent analysis, fuelling R&D, and therefore, the development of the finest Computer Vision technology of tomorrow's world.
Feature Extraction
Feature extraction plays a crucial role in Computer Vision tasks. Computer vision scientists utilize various methods to extract discriminative features from images or videos, such as local binary patterns (LBP), scale-invariant feature transform (SIFT), histogram of oriented gradients (HOG), convolutional neural networks (CNN), and many others. These features capture important patterns and characteristics in the data, enabling subsequent analysis and decision-making.
Neural Network Architectures
Neural Network Architectures: Deep learning architectures have revolutionised computer vision. Computer vision scientists design and implement neural network architectures tailored for specific tasks, such as convolutional neural networks (CNNs) for image classification, region-based CNNs (R-CNNs) for object detection, U-Net for image segmentation, and generative adversarial networks (GANs) for image synthesis. They fine-tune these architectures by adjusting hyperparameters, selecting appropriate activation functions, optimising loss functions, and employing regularisation techniques.
Data Augmentation
Data Augmentation: Computer vision scientists use data augmentation techniques to increase the diversity and size of training datasets. Techniques like image rotation, flipping, scaling, cropping, and adding noise or occlusions help to expose models to a wider range of variations and improve their generalisation capabilities.
If you’re looking for opportunities, don’t hesitate to reach out to DeepRec.ai today, we’re here to find you a role you can thrive in.
