Computer Vision

Empower your devices to perceive and understand the world around us with software that’s masterful, scalable, and tested.

NVIDIA® software enables the end-to-end computer vision (CV) workflow—from model development to deployment—for individual developers, higher education and research, and enterprises.

Computer vision is a field of technology that enables devices like smart cameras to acquire, process, analyze, and interpret images and videos. It can be understood across domains based on type and technique.

Traditional computer vision, also referred to as non-deep learning-based computer vision or image processing, performs a specific task based on hard-coded instructions. For instance, image processing might be used to mirror an image or reduce noise in a video. AI-based computer vision, or vision AI, relies on algorithms that have been trained on visual data to accomplish a specific task. For example, the driver assistance system on an autonomous vehicle designed with CV algorithms uses cameras and other sensors to not only display, but to perceive what’s in front of and behind it to identify and classify into regions or points of interest within an image frame. In this case, computer vision has a safety application—helping the vehicle operator to navigate around road debris, other vehicles, animals, and people. Similarly, farmers might rely on CV-enabled devices to automatically identify weeds and where crops are growing well over a large field to increase yield. CV tasks like these are based on artificial intelligence and, more specifically, deep learning, a type of machine learning patterned after the brain.

Regardless of type, computer vision models let devices perform tasks in real-time that mimic human-like vision capabilities.

Computer vision techniques

Most computer vision techniques begin with a model, or a mathematical algorithm, that has been trained with volumes of data to accomplish a specific task. Some of the common techniques include:

The artificial intelligence-based computer vision workflow

The computer vision workflow is highly dependent on the task, model, and data. A typical, simplified Artificial Intelligence (AI)-based end-to-end CV workflow involves three (3) key stages—Model and Data Selection, Training and Testing/Evaluation, and Deployment and Execution.

Let’s look at these stages using the CV detection technique to identify a dog (classification and segmentation-based techniques would follow an identical workflow).

Finding Fido: Developing an AI-based object-detection cv workflow

Challenge: You want to build software for a monitoring system that automatically detects when your dog arrives at or leaves through the backdoor.​

Three-stage solution

Get started with pretrained models for computer vision

Developing models for these techniques on your own would require a lot of training data, time, and expertise. Here’s the good news–you don’t have to be an expert to get started. NVIDIA hosts a number of pretrained models, already built and ready-to-use, to start developing your own CV solutions. Start with NGC,--our Graphics Processing Unit (GPU)-accelerated software hub–to learn about computer vision models and resources, as well as other deep learning-based speech and natural language processing use cases and application frameworks.

Explore computer vision software

Learn how to develop applications using NVIDIA's industry-specific software products and platforms.

Explore GPU-accelerated libraries and optimization platform

Learn how NVIDIA’s libraries and optimization platform accelerate computer vision on GPUs.

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