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
On the Brink of the Technological Singularity: Is AI Set to Surpass Human Intelligence?
This blog post was originally published at Geisel Software’s website. It is reprinted here with the permission of Geisel Software. Each advancement in artificial intelligence (AI), machine learning (ML), and contemporary large language models (LLMs), rekindles debates over the technological singularity, a hypothetical future point in time at which technological growth becomes uncontrollable and irreversible,
How NVIDIA Jetson AGX Orin Helps Unlock the Power of Surround-view Camera Solutions
This blog post was originally published at e-con Systems’ website. It is reprinted here with the permission of e-con Systems. Autonomous vehicles, such as warehouse robots, rely on precise maneuvering. NVIDIA Jetson AGX Orin™-powered surround-view cameras provide a perfectly synchronized solution, allowing these robots to move freely within designated areas without requiring intensive manual intervention.
Streamlining AI Inference Performance and Deployment with NVIDIA TensorRT-LLM Chunked Prefill
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. In this blog post, we take a closer look at chunked prefill, a feature of NVIDIA TensorRT-LLM that increases GPU utilization and simplifies the deployment experience for developers. This builds on our previous post discussing how advanced
Developing and Deploying Vision-based Multi-camera Solutions
This blog post was originally published at eInfochips’ website. It is reprinted here with the permission of eInfochips. Over the past several years, with strong advances in technology, Artificial Intelligence (AI) and Machine Learning (ML) capabilities have become available in highly compact chipsets. These chipsets have been adopted across vision solutions including low power wearable
Synthetic Data is Revolutionizing Sensor Tech: Real Results from Virtual Worlds
This blog post was originally published at Geisel Software’s website. It is reprinted here with the permission of Geisel Software. Imagine you’re a developer on your first day at a new job. You’re handed a state-of-the-art sensor designed to capture data for an autonomous vehicle. The excitement quickly turns to anxiety as you realize the
Snapdragon Summit’s AI Highlights: A Look at the Future of On-device AI
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. Qualcomm Technologies sets new standards in AI performance for its latest mobile, automotive and Qualcomm AI Hub advancements Our annual Snapdragon Summit wrapped up with exciting new announcements centered on the future of on-device artificial intelligence (AI).
FotoNation Completes a Hat-trick Management Buyout
Vision Technology Leader Reacquired by Company’s Original Founders GALWAY, Ireland, Nov. 12, 2024– FotoNation® (www.fotonation.com), a leading provider of ultra-low power, high-efficiency computer vision AI solutions, announced that its founders have completed a management buyout of the company’s chip-level hardware, firmware, and imaging business, acquiring the company for the third time. Petronel Bigioi, its current
How to Accelerate Larger LLMs Locally on RTX With LM Studio
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. GPU offloading makes massive models accessible on local RTX AI PCs and workstations. Editor’s note: This post is part of the AI Decoded series, which demystifies AI by making the technology more accessible, and showcases new hardware,
Top 4 Computer Vision Problems & Solutions in Agriculture — Part 1
This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. In Part 1 of this series, we highlight the 4 main issues you are likely to encounter in object detection datasets in agriculture. We begin by summarizing the challenges of applying AI to crop monitoring and yield
Introducing the First AMD 1B Language Models: AMD OLMo
This blog post was originally published at AMD’s website. It is reprinted here with the permission of AMD. In recent years, the rapid development of artificial intelligence technology, especially the progress in large language models (LLMs), has garnered significant attention and discussion. From the emergence of ChatGPT to subsequent models like GPT-4 and Llama, these
Computer Vision Integration in Robotic Applications: Real-world Insights
This blog post was originally published at Geisel Software’s website. It is reprinted here with the permission of Geisel Software. Imagine a world where computer vision integration allows robots not only ‘do’ but ‘see’ and ‘understand’. Welcome to the Fourth Industrial Revolution (Industry 4.0)! Here, the fusion of artificial intelligence and industrial robotics is sparking
Introducing Qualcomm IoT Solutions Framework: Making It Easier to Develop and Deploy Solutions
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. The Qualcomm IoT Solutions Framework represents a comprehensive suite of developer tools, reference blueprints and a robust ecosystem of partners Qualcomm Technologies, Inc. is known for its vast collection of wireless-related intellectual property and its processor and
Give AI a Look: Any Industry Can Now Search and Summarize Vast Volumes of Visual Data
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Accenture, Dell Technologies and Lenovo are among the companies tapping a new NVIDIA AI Blueprint to develop visual AI agents that can boost productivity, optimize processes and create safer spaces. Enterprises and public sector organizations around the
The Global Robotaxi Market Value in 2024 Will be $174B
For more information, visit https://www.idtechex.com/en/research-report/autonomous-vehicles-market-2025-2045-robotaxis-autonomous-cars-sensors/1045. The global robotaxi vehicle market value in 2045 will be US$174 billion, growing with a 20-year CAGR of 37% between 2025 and 2045 and with a market share dominated by leaders from the US and China, such as Google’s Waymo, GM’s Cruise, WeRide, Baidu, and AutoX. IDTechEx’s Autonomous Vehicles Market
“Improved Data Sampling Techniques for Training Neural Networks,” a Presentation from Karthik Rao Aroor
Independent AI Engineer Karthik Rao Aroor presents the “Improved Data Sampling Techniques for Training Neural Networks” tutorial at the May 2024 Embedded Vision Summit. For classification problems in which there are equal numbers of samples in each class, Aroor proposes and presents a novel mini-batch sampling approach to train neural… “Improved Data Sampling Techniques for
“Embedded Vision Opportunities and Challenges in Retail Checkout,” an Interview with Zebra Technologies
Anatoly Kotlarsky, Distinguished Member of the Technical Staff in R&D at Zebra Technologies, talks with Phil Lapsley, Co-Founder and Vice President of BDTI and Vice President of Business Development at the Edge AI and Vision Alliance, for the “Embedded Vision Opportunities and Challenges in Retail Checkout” interview at the May… “Embedded Vision Opportunities and Challenges
