JeVois Smart Machine Vision Camera Review – Part 1: Developer / Robotics Kit Unboxing

JeVois-A33 computer vision camera was unveiled at the end of last year through a Kickstarter campaign. Powered by an Allwinner A33 quad core Cortex A7 processor, and a 1.3MP camera sensor, the system could detect motion, track faces and eyes, detect & decode ArUco makers & QR codes, follow lines for autonomous cars, etc.. thanks to JeVois framework. Most rewards from KickStarter shipped in April of this year, so it’s quite possible some of the regular readers of this blog are already familiar the camera. But the developer (Laurent Itti) re-contacted me recently, explaining they add improves the software with Python support, and new features such as the capability of running deep neural networks directly on the processor inside the smart camera. He also wanted to send a review sample, which I received today, but I got a bit more than I expected, so I’ll start the review with an unboxing of they call the “Developer / Robotics Kit”. I …

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Arm Research Summit 2017 Streamed Live on September 11-13

The Arm Research Summit is “an academic summit to discuss future trends and disruptive technologies across all sectors of computing”, with the second edition of the even taking place now in Cambridge, UK until September 13, 2017. The Agenda includes various subjects such as architecture and memory, IoT, HPC, computer vision, machine learning, security, servers, biotechnology and others. You can find the full detailed schedule for each day on Arm website, and the good news is that the talks are streamed live in YouTube, so you can follow the talks that interest you from the comfort of your home/office. Note that you can switch between rooms in the stream above by clicking on <-> icon. Audio volume is a little low… Thanks to Nobe for the tip. Jean-Luc Aufranc (CNXSoft)Jean-Luc started CNX Software in 2010 as a part-time endeavor, before quitting his job as a software engineering manager, and starting to write daily news, and reviews full time later in …

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Getting Started with OpenCV for Tegra on NVIDIA Tegra K1, CPU vs GPU Computer Vision Comparison

This is a guest post by Leonardo Graboski Veiga, Field Application Engineer, Toradex Brasil Introduction Computer vision (CV) is everywhere – from cars to surveillance and production lines, the need for efficient, low power consumption yet powerful embedded systems is nowadays one of the bleeding edge scenarios of technology development. Since this is a very computationally intensive task, running computer vision algorithms in an embedded system CPU might not be enough for some applications. Developers and scientists have noticed that the use of dedicated hardware, such as co-processors and GPUs – the latter traditionally employed for graphics rendering – can greatly improve CV algorithms performance. In the embedded scenario, things usually are not as simple as they look. Embedded GPUs tend to be different from desktop GPUs, thus requiring many workarounds to get extra performance from them. A good example of a drawback from embedded GPUs is that they are hardly supported by OpenCV – the de facto standard libraries …

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Open Source ARM Compute Library Released with NEON and OpenCL Accelerated Functions for Computer Vision, Machine Learning

GPU compute promises to deliver much better performance compared to CPU compute for application such a computer vision and machine learning, but the problem is that many developers may not have the right skills or time to leverage APIs such as OpenCL. So ARM decided to write their own ARM Compute library and has now released it under an MIT license. The functions found in the library include: Basic arithmetic, mathematical, and binary operator functions Color manipulation (conversion, channel extraction, and more) Convolution filters (Sobel, Gaussian, and more) Canny Edge, Harris corners, optical flow, and more Pyramids (such as Laplacians) HOG (Histogram of Oriented Gradients) SVM (Support Vector Machines) H/SGEMM (Half and Single precision General Matrix Multiply) Convolutional Neural Networks building blocks (Activation, Convolution, Fully connected, Locally connected, Normalization, Pooling, Soft-max) The library works on Linux, Android or bare metal on armv7a (32bit) or arm64-v8a (64bit) architecture, and makes use of  NEON, OpenCL, or  NEON + OpenCL. You’ll need an …

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Rockchip RV1108 Visual Processor is Designed for 1080p & 2K Camera Applications

Rockchip has joined other companies in developing camera SoCs with their RV1108 Visual Platform based on a single Cortex A7 core, a CEVA XM4 visual processing DSP, and capable of H.264 video encoding up to 1440p30 / 1080p60. Rockchip RV1108 main features and specifications: CPU – ARM Cortex A7 @ up to 1.0 GHz DSP – embedded CEVA XM4 vision processor up to 600MHz Video Encoder – 2K/H.264, high definition & low bit rate Camera – MIPI CSI and DVP interfaces Image processing – Low-light-level night vision imaging: 8MP professional image processing unit Audio Processing – Audio Codec supporting up to 8-way MIC array, 3A? phonetic algorithms, such as echo cancellation, noise suppression; Video Out/Input – HDMI OUT, CVBS OUT, MIPI DSI, CVBS IN Networking – 10/100 Ethernet PHY The processor is expected to be used in drones, IP cameras, car dashcams, sports/action cameras, as well as other applications such as panoramic cameras, computer vision applications, or real-time WiFi video …

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$55 OpenMV Cam M7 Open Source Computer Vision Board is Powered by an STM32F7 Cortex-M7 MCU

I wrote about Jevois-A33 computer vision camera based on Allwinner A33 quad core Cortex A7 processor last week, and today, I’ve come across OpenMV Cam M7 open source computer vision board based on a much less powerful STMicro STM32F7 ARM Cortex M7 micro-controller, but with the advantage of consuming less power, and exposing some extra I/Os. OpenMV Cam M7 board specifications & features: MCU – STMicro STM32F765VI ARM Cortex M7 @ up to 216 MHz with 512KB RAM, 2 MB flash. External Storage – micro SD slot Camera Omnivision OV7725 image sensor supporting 640×480 8-bit grayscale images or 320×240 16-bit RGB565 images at 30 FPS 2.8mm lens on a standard M12 lens mount USB – 1x micro USB port (Virtual COM Port and a USB Flash Drive) Expansion – 2x 8-pin headers with SPI, I2C CAN bus, asynchronous serial bus (Tx/Rx), 12-bit ADC, 12-bit DAC, 3x I/Os for servo control; interrupts and PWM on all I/O pins; 3.3V (5V tolerant) Misc – RGB LED …

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