Vision Algorithms for Embedded Vision
Most computer vision algorithms were developed on general-purpose computer systems with software written in a high-level language
Most computer vision algorithms were developed on general-purpose computer systems with software written in a high-level language. Some of the pixel-processing operations (ex: spatial filtering) have changed very little in the decades since they were first implemented on mainframes. With today’s broader embedded vision implementations, existing high-level algorithms may not fit within the system constraints, requiring new innovation to achieve the desired results.
Some of this innovation may involve replacing a general-purpose algorithm with a hardware-optimized equivalent. With such a broad range of processors for embedded vision, algorithm analysis will likely focus on ways to maximize pixel-level processing within system constraints.
This section refers to both general-purpose operations (ex: edge detection) and hardware-optimized versions (ex: parallel adaptive filtering in an FPGA). Many sources exist for general-purpose algorithms. The Embedded Vision Alliance is one of the best industry resources for learning about algorithms that map to specific hardware, since Alliance Members will share this information directly with the vision community.
General-purpose computer vision algorithms

One of the most-popular sources of computer vision algorithms is the OpenCV Library. OpenCV is open-source and currently written in C, with a C++ version under development. For more information, see the Alliance’s interview with OpenCV Foundation President and CEO Gary Bradski, along with other OpenCV-related materials on the Alliance website.
Hardware-optimized computer vision algorithms
Several programmable device vendors have created optimized versions of off-the-shelf computer vision libraries. NVIDIA works closely with the OpenCV community, for example, and has created algorithms that are accelerated by GPGPUs. MathWorks provides MATLAB functions/objects and Simulink blocks for many computer vision algorithms within its Vision System Toolbox, while also allowing vendors to create their own libraries of functions that are optimized for a specific programmable architecture. National Instruments offers its LabView Vision module library. And Xilinx is another example of a vendor with an optimized computer vision library that it provides to customers as Plug and Play IP cores for creating hardware-accelerated vision algorithms in an FPGA.
Other vision libraries
- Halcon
- Matrox Imaging Library (MIL)
- Cognex VisionPro
- VXL
- CImg
- Filters

“AI-powered Scouting: Democratizing Talent Discovery in Sports,” a Presentation from ai.io
Jonathan Lee, Chief Product Officer at ai.io, presents the “AI-powered Scouting: Democratizing Talent Discovery in Sports,” tutorial at the May 2025 Embedded Vision Summit. In this presentation, Lee shares his experience using AI and computer vision to revolutionize talent identification in sports. By developing aiScout, a platform that enables athletes… “AI-powered Scouting: Democratizing Talent Discovery

Capgemini Leverages Qualcomm Dragonwing Portfolio to Enhance Railway Monitoring with Edge AI
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. AI device powered by Qualcomm Dragonwing boosts productivity and reduces cloud dependence in Capgemini’s monitoring application for grade crossings Capgemini moved from their previous hardware solution to an edge AI device powered by the Qualcomm® Dragonwing™ QCS6490

“Vision-based Aircraft Functions for Autonomous Flight Systems,” a Presentation from Acubed (an Airbus Innovation Center)
Arne Stoschek, Vice President of AI and Autonomy at Acubed (an Airbus innovation center), presents the “Vision-based Aircraft Functions for Autonomous Flight Systems” tutorial at the May 2025 Embedded Vision Summit. At Acubed, an Airbus innovation center, the mission is to accelerate AI and autonomy in aerospace. Stoschek gives an… “Vision-based Aircraft Functions for Autonomous

“Edge AI and Vision at Scale: What’s Real, What’s Next, What’s Missing?,” An Embedded Vision Summit Expert Panel Discussion
Sally Ward-Foxton, Senior Reporter at EE Times, moderates the “Edge AI and Vision at Scale: What’s Real, What’s Next, What’s Missing?” Expert Panel at the May 2025 Embedded Vision Summit. Other panelists include Chen Wu, Director and Head of Perception at Waymo, Vikas Bhardwaj, Director of AI in the Reality… “Edge AI and Vision at

Hot Topics at Hot Chips: Inference, Networking, AI Innovation at Every Scale — All Built on NVIDIA
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. At the conference in Palo Alto, California, NVIDIA experts detail how NVIDIA NVLink and Spectrum-X Ethernet technologies, Blackwell and CUDA accelerate inference for millions of AI workflows across the globe. AI reasoning, inference and networking will be

“A View From the 2025 Embedded Vision Summit (Part 2),” a Presentation from the Edge AI and Vision Alliance
Jeff Bier, Founder of the Edge AI and Vision Alliance, welcomes attendees to the May 2025 Embedded Vision Summit on May 22, 2025. Bier provides an overview of the edge AI and vision market opportunities, challenges, solutions and trends. He also introduces the Edge AI and Vision Alliance and the… “A View From the 2025

“A View From the 2025 Embedded Vision Summit (Part 1),” a Presentation from the Edge AI and Vision Alliance
Jeff Bier, Founder of the Edge AI and Vision Alliance, welcomes attendees to the May 2025 Embedded Vision Summit on May 21, 2025. Bier provides an overview of the edge AI and vision market opportunities, challenges, solutions and trends. He also introduces the Edge AI and Vision Alliance and the… “A View From the 2025

“The Future of Visual AI: Efficient Multimodal Intelligence,” a Keynote Presentation from Trevor Darrell
Trevor Darrell, Professor at the University of California, Berkeley, presents the “Future of Visual AI: Efficient Multimodal Intelligence” tutorial at the May 2025 Embedded Vision Summit. AI is on the cusp of a revolution, driven by the convergence of several breakthroughs. One of the most significant of these advances is… “The Future of Visual AI:

Akida Exploits Sparsity For Low Power in Neural Networks
This blog post was originally published at BrainChip’s website. It is reprinted here with the permission of BrainChip. In the rapidly evolving field of artificial intelligence, edge computing has become increasingly vital for deploying intelligent systems in real-world environments where power, latency, and bandwidth are limited: we need neural network models to run efficiently. For

5 Key Questions about Synthetic Data Every Data Scientist Should Know
This blog post was originally published at Geisel Software’s Symage website. It is reprinted here with the permission of Geisel Software. In this article, we tackle the 5 key questions about synthetic data that every data scientist must understand to stay ahead in the rapidly evolving world of AI. From its creation process to its

Snapdragon Ride: A Foundational Platform for Automakers to Scale with the ADAS Market
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. The automotive industry is well into the transformation of vehicle architectures and consumer-driven experiences. As the demand for advanced driver assistance systems (ADAS) technologies continues to soar, Qualcomm Technologies’ cutting-edge Snapdragon Ride Platforms are setting a new standard for automotive

“The New OpenCV 5.0: Added Features, Performance Improvements and Future Directions,” a Presentation from OpenCV.org
Satya Mallick, CEO of OpenCV.org, presents the “New OpenCV 5.0: Added Features, Performance Improvements and Future Directions” tutorial at the May 2025 Embedded Vision Summit. In this presentation, Mallick delves into the latest version of OpenCV, the world’s most popular open-source computer vision library. He highlights the major innovations and… “The New OpenCV 5.0: Added

Maximize Robotics Performance by Post-training NVIDIA Cosmos Reason
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. First unveiled at NVIDIA GTC 2025, NVIDIA Cosmos Reason is an open and fully customizable reasoning vision language model (VLM) for physical AI and robotics. The VLM enables robots and vision AI agents to reason using prior

“Introduction to Shrinking Models with Quantization-aware Training and Post-training Quantization,” a Presentation from NXP Semiconductors
Robert Cimpeanu, Machine Learning Software Engineer at NXP Semiconductors, presents the “Introduction to Shrinking Models with Quantization-aware Training and Post-training Quantization” tutorial at the May 2025 Embedded Vision Summit. In this presentation, Cimpeanu explains two neural network quantization techniques, quantization-aware training (QAT) and post-training quantization (PTQ), and explain when to… “Introduction to Shrinking Models with

Implementing Multimodal GenAI Models on Modalix
This blog post was originally published at SiMa.ai’s website. It is reprinted here with the permission of SiMa.ai. It has been our goal since starting SiMa.ai to create one software and hardware platform for the embedded edge that empowers companies to make their AI/ML innovations come to life. With the rise of Generative AI already

“Customizing Vision-language Models for Real-world Applications,” a Presentation from NVIDIA
Monika Jhuria, Technical Marketing Engineer at NVIDIA, presents the “Customizing Vision-language Models for Real-world Applications” tutorial at the May 2025 Embedded Vision Summit. Vision-language models (VLMs) have the potential to revolutionize various applications, and their performance can be improved through fine-tuning and customization. In this presentation, Jhuria explores the concept… “Customizing Vision-language Models for Real-world