MathWorks provides software tools for embedded vision. MATLAB is a high-level language and interactive environment for algorithm development, data analysis, visualization, and numeric computing. Several key toolboxes extend MATLAB for embedded vision: Computer Vision System Toolbox, Image Processing Toolbox, and Statistics Toolbox. Together, these products enable you to work much faster than with traditional programming languages such as C or C++. Model-Based Design with MathWorks products enable the migration of embedded vision algorithms into real-world hardware while meeting tight constraints on performance, power, latency, and cost. Our tools support efficient design trade-off exploration and design verification. Using products such as MATLAB Coder, Simulink, Simulink Coder, Embedded Coder, and Simulink HDL Coder, engineers can migrate embedded vision algorithms from MATLAB and Simulink into embedded systems.
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Rapid Prototyping on NVIDIA Jetson Platforms with MATLAB
This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. This article discusses how an application developer can prototype and deploy deep learning algorithms on hardware like the NVIDIA Jetson Nano Developer Kit with MATLAB. In previous posts, we explored how you can… Rapid Prototyping on NVIDIA Jetson …
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“Deploying Deep Learning Models on Embedded Processors for Autonomous Systems with MATLAB,” a Presentation from MathWorks
Sandeep Hiremath, Product Manager, and Bill Chou, Senior Computer Vision Scientist, both of MathWorks, present the "Deploying Deep Learning Models on Embedded Processors for Autonomous Systems with MATLAB" tutorial at the May 2019 Embedded Vision Summit. In this presentation, Hiremath and Chou explain how to bring the power of deep neural networks to memory- and …
“Three Key Factors for Successful AI Projects,” a Presentation from MathWorks
Bruce Tannenbaum, Technical Marketing Manager for AI applications at MathWorks, presents the "Three Key Factors for Successful AI Projects" tutorial at the May 2019 Embedded Vision Summit. AI is transforming the products we build and the way we do business. AI using images and video is already at work in our smart home devices, our …
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What Is Deep Learning? Three Things You Need to Know
This article was originally published at MathWorks' website. It is reprinted here with the permission of MathWorks. Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to …
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Speeding Up Semantic Segmentation Using MATLAB Container from NVIDIA NGC
This article was originally published at NVIDIA's website. It is reprinted here with the permission of NVIDIA. Gone are the days of using a single GPU to train a deep learning model. With computationally intensive algorithms such as semantic segmentation, a single GPU can take days to optimize a model. But multi-GPU hardware is expensive, …
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Multi-sensor Fusion for Robust Device Autonomy
While visible light image sensors may be the baseline "one sensor to rule them all" included in all autonomous system designs, they're not necessarily a sole panacea. By combining them with other sensor technologies: "Situational awareness" sensors; standard and high-resolution radar, LiDAR, infrared and UV, ultrasound and sonar, etc., and "Positional awareness" sensors such as …
2018 Vision Product of the Year Award Winner Showcase: MathWorks (Software and Algorithms)
MathWorks' GPU Coder is the 2018 Vision Product of the Year Award Winner in the Software and Algorithms category. The new MathWorks® GPU Coder software enables scientists and engineers to automatically generate optimized CUDA code from high-level functional descriptions in MATLAB® for deep learning, embedded vision, and autonomous systems. The generated CUDA code, integrated in …
Using MATLAB and TensorRT on NVIDIA GPUs
This article was originally published at NVIDIA's website. It is reprinted here with the permission of NVIDIA. As we design deep learning networks, how can we quickly prototype the complete algorithm—including pre- and postprocessing logic around deep neural networks (DNNs) —to get a sense of timing and performance on standalone GPUs? This question comes up …
“Deep Learning in MATLAB: From Concept to Optimized Embedded Code,” a Presentation from MathWorks
Avinash Nehemiah, Product Marketing Manager for Computer Vision, and Girish Venkataramani, Product Development Manager, both of MathWorks, present the “Deep Learning in MATLAB: From Concept to Optimized Embedded Code” tutorial at the May 2018 Embedded Vision Summit. In this presentation, you’ll learn how to adopt MATLAB to design deep learning based vision applications and re-target …
“How to Test and Validate an Automated Driving System,” a Presentation from MathWorks
Avinash Nehemiah, Product Marketing Manager for Computer Vision at MathWorks, presents the "How to Test and Validate an Automated Driving System" tutorial at the May 2017 Embedded Vision Summit. Have you ever wondered how ADAS and autonomous driving systems are tested? Automated driving systems combine a diverse set of technologies and engineering skill sets from …
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“Deep Learning and Vision Algorithm Development in MATLAB Targeting Embedded GPUs,” a Presentation from MathWorks
Avinash Nehemiah, Product Marketing Manager for Computer Vision, and Girish Venkataramani, Product Development Manager, both of MathWorks, presents the "Deep Learning and Vision Algorithm Development in MATLAB Targeting Embedded GPUs" tutorial at the May 2017 Embedded Vision Summit. In this presentation, you'll learn how to adopt a MATLAB-centric workflow to design, verify and deploy your …
“Getting from Idea to Product with 3D Vision,” a Presentation from Intel and MathWorks
Anavai Ramesh, Senior Software Engineer at Intel, and Avinash Nehemiah, Product Marketing Manager for Computer Vision at MathWorks, present the "Getting from Idea to Product with 3D Vision" tutorial at the May 2016 Embedded Vision Summit. To safely navigate autonomously, cars, drones and robots need to understand their surroundings in three dimensions. While 3D vision …
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May 2014 Embedded Vision Summit Technical Presentation: “How to Create a Great Object Detector,” Avinash Nehemiah, MathWorks
Avinash Nehemiah, Product Marketing Manager for Computer Vision at MathWorks, presents the "How to Create a Great Object Detector" tutorial at the May 2014 Embedded Vision Summit. Detecting objects of interest in images and video is a key part of practical embedded vision systems. Impressive progress has been made over the past few years by …
Visual Intelligence Gives Robotic Systems Spatial Sense
This article is an expanded version of one originally published at EE Times' Embedded.com Design Line. It is reprinted here with the permission of EE Times. In order for robots to meaningfully interact with objects around them as well as move about their environments, they must be able to see and discern their surroundings. Cost-effective …
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