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
Andes Technology’s AutoOpTune Applies Genetic Algorithms to Accelerate RISC-V Software Optimization
Andes AutoOpTune™ v1.0 accelerates software development by giving software developers the ability to automatically explore and choose the compiler optimizations to achieve their performance and code-size targets. Hsinchu, Taiwan – July 10, 2025 – Andes Technology, a leading provider of high-efficiency, low-power 32/64-bit RISC-V processor cores and a Founding Premier member of RISC-V International, today
Stereo ace for Precise 3D Images Even with Challenging Surfaces
The new high-resolution Basler Stereo ace complements Basler’s 3D product range with an easy-to-integrate series of active stereo cameras that are particularly suitable for logistics and factory automation. Ahrensburg, July 10, 2025 – Basler AG introduces the new active 3D stereo camera series Basler Stereo ace consisting of 6 camera models and thus strengthens its position as
Optimizing Your AI Model for the Edge
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. Key takeaways: We talk about five techniques—compiling to machine code, quantization, weight pruning, domain-specific fine-tuning, and training small models with larger models—that can be used to improve on-device AI model performance. Whether you think edge AI is
Cadence Demonstration of a SWIN Shifted Window Vision Transform on a Tensilica Vision DSP-based Platform
Amol Borkar, Director of Product Marketing for Cadence Tensilica DSPs, presents the company’s latest edge AI and vision technologies at the 2025 Embedded Vision Summit. Specifically, Borkar demonstrates the use of the Tensilica Vision 230 (Q7) DSP for advanced AI and transformer applications. The Vision 230 DSP is a highly efficient, configurable, and extensible processor
DeGirum Demonstration of Its PySDK Running on BrainChip Hardware for Real-time Edge AI
Stephan Sokolov, Software Engineer at DeGirum, demonstrates the company’s latest edge AI and vision technologies and products in BrainChip’s booth at the 2025 Embedded Vision Summit. Specifically, Sokolov demonstrates the power of real-time AI inference at the edge, running DeGirum’s PySDK application directly on BrainChip hardware. This demo showcases low-latency, high-efficiency performance as a script
Best-in-class Multimodal RAG: How the Llama 3.2 NeMo Retriever Embedding Model Boosts Pipeline Accuracy
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Data goes far beyond text—it is inherently multimodal, encompassing images, video, audio, and more, often in complex and unstructured formats. While the common method is to convert PDFs, scanned images, slides, and other documents into text, it
BrainChip Demonstration of LLM Inference On an FPGA at the Edge using the TENNs Framework
Kurt Manninen, Senior Solutions Architect at BrainChip, demonstrates the company’s latest edge AI and vision technologies and products at the 2025 Embedded Vision Summit. Specifically, Van Manninen demonstrates his company’s large language models (LLMs) running on an FPGA edge device, powered by BrainChip’s proprietary TENNs (Temporal Event-Based Neural Networks) framework. BrainChip enables real-time generative AI
BrainChip Demonstration of Its Latest Audio AI Models in Action At the Edge
Richard Resseguie, Senior Product Manager at BrainChip, demonstrates the company’s latest edge AI and vision technologies and products at the 2025 Embedded Vision Summit. Specifically, Van Resseguie demonstrates the company’s latest advancements in edge audio AI. The demo features a suite of models purpose-built for real-world applications including automatic speech recognition, denoising, keyword spotting, and
Network Optix Demonstration of How the Company is Powering Scalable Data-driven Video Infrastructure
Tagir Gadelshin, Director of Product at Network Optix, demonstrates the company’s latest edge AI and vision technologies and products at the 2025 Embedded Vision Summit. Specifically, Gadelshin demonstrates how the company’s latest release, Gen 6 Enterprise, is enabling cloud-powered, event-driven video infrastructure for enterprise organizations at scale. Built on Nx EVOS, Gen 6 Enterprise supports
Network Optix Demonstration of Seamless AI Model Integration with Nx AI Manager
Robert van Emdem, Senior Director of Data Science at Network Optix, demonstrates the company’s latest edge AI and vision technologies and products at the 2025 Embedded Vision Summit. Specifically, Van Emdem demonstrates how Nx AI Manager enables seamless deployment of AI models across a wide variety of hardware, including GPU, VPU, and CPU environments. van
Small Purpose Built AI Models
This blog post was originally published at Synetic AI’s website. It is reprinted here with the permission of Synetic AI. In an era dominated by ever-larger foundation models, a quieter revolution is underway—one defined not by scale, but by precision. This paper argues that the most impactful AI systems of the future will not be
Network Optix Demonstration of Extracting AI Model Data with AI Manager
Marcel Wouters, Senior Backend Engineer at Network Optix, the company’s latest edge AI and vision technologies and products at the 2025 Embedded Vision Summit. Specifically, Wouters demonstrates how Nx AI Manager simplifies the extraction and use of data from AI models. Wouters showcases a live model detecting helmets and vests on a construction site and
Network Optix Overview of the Company’s Technologies, Products and Capabilities
Bradley Milligan, North America Sales Coordinator at Network Optix, demonstrates the company’s latest edge AI and vision technologies and products at the 2025 Embedded Vision Summit. Specifically, Milligan shares how Network Optix is enabling scalable, intelligent video solutions for organizations and industries around the world, including by using Nx EVOS and Nx AI Manager. Learn
Qualcomm Trends and Technologies to Watch In IoT and Edge AI
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. “It’s amazing how Qualcomm was able to turn the ship on a dime since the last [Embedded World] show. The launch of Qualcomm Dragonwing and the Partner Day event were on point and helpful, showing Qualcomm’s commitment
Nota AI Collaborates with Renesas on High-efficiency Driver Monitoring AI for RA8P1 Microcontroller
AI model optimization powers high-efficiency DMS on ultra-compact MCUs 50FPS real-time performance with ultra-low power and minimal system footprint SEOUL, South Korea, July 2, 2025 /PRNewswire/ — Nota AI, a global leader in AI optimization, today announced a collaboration with Renesas Electronics Corporation, a premier supplier of advanced semiconductor solutions, to deliver an optimized Driver Monitoring
“Voice Interfaces on a Budget: Building Real-time Speech Recognition on Low-cost Hardware,” a Presentation from Useful Sensors
Pete Warden, CEO of Useful Sensors, presents the “Voice Interfaces on a Budget: Building Real-time Speech Recognition on Low-cost Hardware” tutorial at the May 2025 Embedded Vision Summit. In this talk, Warden presents Moonshine, a speech-to-text model that outperforms OpenAI’s Whisper by a factor of five in terms of speed.… “Voice Interfaces on a Budget:
