How NVIDIA Uses OpenCV

The article discusses enhancing robot perception efficiency on the NVIDIA Jetson Thor platform by utilizing specialized hardware accelerators alongside powerful GPUs.

NVIDIA is simplifying the deployment of its CUDA software stack by collaborating with various third-party platforms, enabling developers to access CUDA directly through their preferred package mana...

The article discusses the development of multimodal visual AI agents using NVIDIA NIM microservices, highlighting the importance of vision-language models (VLMs) in processing and analyzing diverse...

The article discusses the importance of multimodal sensor calibration for robotics and autonomous vehicles, introducing the Calibration Anywhere software by Main Street Autonomy as a solution that ...

The article discusses the optimization of Microsoft Bing Visual Search using NVIDIA accelerated libraries, focusing on the TuringMM visual embedding model.

This article discusses the process of camera calibration to enhance AI-powered multi-camera tracking using NVIDIA Metropolis.

The article discusses how to build a zero-copy AI sensor processing pipeline using OpenCV within the NVIDIA Holoscan SDK.

This article discusses the development of a high-fidelity multi-robot simulation environment using NVIDIA Isaac Sim, ROS, and Nimbus.

The article discusses the growing demand for AI-based computer vision applications and the associated increase in compute costs.

The article discusses NVIDIA's next-generation computing platforms optimized for AI, video, and data analytics performance.

The article discusses the introduction of NVIDIA L4 Tensor Core GPUs, highlighting their enhanced performance for AI video and inference tasks compared to the previous T4 generation.

The article discusses the development of an end-to-end automated labeling pipeline for autonomous vehicle perception, highlighting the challenges of manual data labeling and the efficiency gains ac...

The article discusses the use of CUDA and TensorRT execution providers in ONNX Runtime for optimizing AI applications on NVIDIA-based PCs.

The article discusses how Fyma, a computer vision company, leveraged NVIDIA DeepStream to create a simple, extensible, and performant video processing pipeline.

The article discusses the improved interoperability between NVIDIA Vision Programming Interface (VPI) and PyTorch, focusing on how VPI can enhance object detection and tracking in computer vision a...

The article discusses the use of NVIDIA Isaac GEMs for ROS, which provide GPU-accelerated packages to enhance ROS2 applications, particularly in image processing and perception models.

The article discusses the NVIDIA Data Loading Library (DALI), which provides a scalable and efficient solution for data preprocessing in deep learning applications.

The article discusses the release of NVIDIA Optical Flow SDK 3. 0, highlighting its new features that enhance optical flow computation for applications, particularly in gaming and video processing.

David Niewinski from Dave’s Armoury created a robotic arm that plays cornhole using the NVIDIA Jetson AGX Xavier Developer Kit.

The article discusses the Dragon Eye project, an electronic judging system for glider races developed by Steve Chang using the NVIDIA Jetson Nano Developer Kit.

The article discusses cuCIM, a new RAPIDS library designed for accelerated n-dimensional image processing and image I/O on GPUs.

The article discusses optimizing inference performance in deep learning applications by leveraging NVIDIA Triton Inference Server and NVIDIA DALI for efficient data preprocessing.

The article discusses how model developers can significantly accelerate model training using Determined AI’s Deep Learning Training Platform and the RAPIDS accelerated data science toolkit, achievi...

The article discusses how to reduce temporal noise in images using the NVIDIA Vision Programming Interface (VPI) on NVIDIA Jetson embedded computers.

The article announces the availability of JetPack SDK 4.

This article is the third installment in a series on deploying scalable object detection inference pipelines, focusing on optimization techniques and deployment strategies.

The Jetson Project of the Month highlights the Drowsiness, Blindspot and Emotion Monitor (DBSE) developed by Luis Oliver, Victor Izquierdo, and Alejandro Gutiérrez.

NVIDIA has launched the Jetson Nano 2GB Developer Kit, an affordable and powerful platform for learning and developing AI and robotics applications.

The article discusses how to achieve peak performance in streaming interactive deep learning applications using NVIDIA Simple Streamer (NVSS).

This article provides an overview of deploying a scalable object detection inference pipeline for autonomous vehicles, focusing on the importance of deep learning and data processing.

This article highlights two innovative projects recognized as NVIDIA Jetson Projects of the Month: an AI fever screening thermometer by Tomasz Lewicki and a self-navigating robot for search and res...

The article announces the availability of ONNX Runtime for the NVIDIA Jetson platform, highlighting its benefits for high-performance inferencing in edge AI systems.

The article discusses the detection of rotated objects using the NVIDIA Object Detection Toolkit (ODTK), emphasizing the limitations of traditional axis-aligned bounding boxes and the advantages of...

The article discusses the advancements of NVIDIA A100 GPUs in enhancing computer vision workloads, highlighting its architecture, features, and two significant research projects.

This article provides a comprehensive guide on implementing object detection using NVIDIA GPUs in a short timeframe.

The article discusses the NVIDIA Jetson Nano Developer Kit, a compact and affordable platform for AI computing, designed for embedded designers, researchers, and DIY makers.

NVIDIA JetPack 3. 2 is the latest production software release for the Jetson platform, including Jetson TX2, TX2i, and TX1.

The article discusses the application of deep learning techniques to automate road detection from high-resolution satellite imagery in the SpaceNet challenge.

The article discusses the launch of the NVIDIA Jetson TX2, a powerful low-power embedded platform designed for AI compute performance at the edge.

The article introduces the NVIDIA Jetson TX2, a high-performance, low-power embedded platform designed for AI at the edge.

NVIDIA has launched the Jetson TX1, a compact Linux system-on-module designed for advanced embedded applications in visual computing.

DJI has introduced the Manifold, a high-performance embedded computer powered by the NVIDIA Tegra K1, designed for developers to enhance drone capabilities with advanced computing and image process...