computer vision News - Page 22 of 26 - CNX Software

January 9, 2020 by Stephen Vicinanza - 4 Comments

Lattice Introduces CrossLink-NX FPGA for Edge AI & Embedded Vision

Lattice CrossLink-NX FPGA Lattice Semiconductor has announced the first product associated with its Nexus Platform, the CrossLink-NX FPGA designed for embedded vision and Edge AI applications. There are two offerings at this time, the CrossLink-NX FPGA 17, and the CrossLink-NX FPGA 40. Recent Announcements The Nexus Platform was introduced at the beginning of December 2019, and now CrossLink-NX has been developed and is being manufactured. The first announcements of Lattice Nexus Platform and The CrossLink-NX Product Family came as the company’s moved to capture the embedded vision systems market. The Standout Features The low-power consumption, low soft error immunity, and 10Gbps MIPI are highlights of the CrossLink-NX FPGA. Other features include Instant On, with IO configured in 3 ms, and a total of 8 ms for the device. The Cross-Platform FPGAs The trends in technology are leading to devices that can cross function in a number of different tech environments. […]

December 29, 2019December 29, 2019 by Ayo Ayibiowu - No Comments

NanoVision & NanoBerry Miniature Computer Vision Evaluation Kits Released For Arduino & Raspberry Pi Platforms

NanoVision and NanoBerry evaluation kits

AMS (Austria Mikro Systeme) known for their array for micro sensing solutions and most importantly the NanEye, a Miniature CMOS image sensor which is designed for applications where size is a critical factor has also launched a set for evaluation kits called the NanoVision and the NanoBerry for the development of solution based on the AMS NanEyeC miniature image sensor. NanEyeC Camera Sensor The NanEyeC comes in a footprint of just 1mm x 1mm surface mount, and it can produce 100kpixel resolution up to 58 frames/s. It seems the NanEyeC is based on the NanEye series, which typical (NanEye) features a 249×250 resolution with a high sensitive 3um x 3um rolling shutter pixel and capable of a high frame rate of about 43fps to 62fps. The NanEyeC sensor is based on the high-speed LVDS data interface. The sensor is assembled with a unique lens and cover glass, which fits in […]

December 26, 2019June 9, 2020 by Siji Sunny - No Comments

MediaPipe is an Open Source Perception Pipeline Framework Developed by Google

MediaPipe is an open-source perception pipeline framework introduced by Google, which helps to build multi-modal machine learning pipelines. A developer can build a prototype, without really getting into writing machine learning algorithms and models, by using existing components. This framework can be used for various vision & media processing applications (especially in VR) such as Object Detection, Face Detection, Hand Tacking, Multi-hand Tracking and Hair Segmentation. MediaPipe supports various hardware and operating system platforms such as Android, iOS & Linux by offering API’s in C++, Java, Objective-c, etc. And this framework also capable of utilizing GPU resources. MediaPipe Components The framework is comprised of three major components A framework for inference from the pipeline data Tools for evaluation And a collection of reusable inference and processing components It follows the approach of Graph-based frameworks in OpenCV and all processing happens with the context of the Graph. The Graph contains a […]

October 11, 2019October 11, 2019 by Jean-Luc Aufranc (CNXSoft) - 3 Comments

Intrinsyc Unveils Open-Q 845 µSOM and Snapdragon 845 Mini-ITX Development Kit

Intrinsyc introduced the first Qualcomm Snapdragon 845 hardware development platform last year with its Open-Q 845 HDK designed for OEMs and device makers. But the company has now just announced a solution for embedded systems and Internet of Things (IoT) products with Open-Q 845 micro system-on-module (µSOM) powered by the Snapdragon 845 octa-core processor, as well as a complete development kit featuring the module and a Mini-ITX baseboard. Open-Q845 µSOM Specifications: SoC – Qualcomm Snapdragon SDA845 octa-core processor with 4x Kryo 385 Gold cores @ 2.649GHz + 4x Kryo 385 Silver low-power cores @ 1.766GHz cores, Hexagon 685 DSP, Adreno 630 GPU with OpenGL ES 3.2 + AEP (Android Extension Pack), DX next, Vulkan 2, OpenCL 2.0 full profile System Memory – 4GB or 6GB dual-channel high-speed LPDDR4X SDRAM at 1866MHz Storage – 32GB or 64GB UFS Flash Storage Connectivity Wi-Fi 5 802.11a/b/g/n/ac 2.4/5Ghz 2×2 MU-MIMO (WCN3990) with 5 GHz […]

September 6, 2019 by Jean-Luc Aufranc (CNXSoft) - 10 Comments

NXP i.MX RT106F & RT106A/L Cortex-M7 Processors Target Offline Face Recognition & Smart Audio Applications

NXP i.MX RT crossover processors combine real-time capabilities of microcontrollers with the performance of application processors thanks to an Arm Cortex-M7 core clocked at 528 MHz and more. The performance is indeed impressive as shown by Teensy 4.0 benchmarks, but so far NXP i.MX RT processor targeted general purpose applications. The company has now introduced three new crossover processors designed for AI applications. NXP i.MX RT106F is designed for offline face recognition and expression Identification, while RT106L and RT106A are made for local and cloud-based embedded voice applications. NXP i.MX RT106F Processor Highlights of the processor: CPU – Arm Cortex-M7 @ 600 MHz (3020 CoreMark/1284 DMIPS) Memory – 1 MB On-Chip SRAM plus up to 512 KB configurable as Tightly Coupled Memory (TCM) External memory interface options – NAND, eMMC, QuadSPI NOR Flash, and Parallel NOR Flash Real-time, low-latency response as low as 20 ns Industry’s lowest dynamic power with […]

September 3, 2019September 3, 2019 by Jean-Luc Aufranc (CNXSoft) - No Comments

CDVA (Compact Descriptors for Video Analysis) Enable “Video Understanding”

One of the most popular applications of artificial intelligence is object detection where you have models capable of detecting objects or subjects being cats, dogs, cars, laptops, or other. As I discovered in a press release by Gyrfalcon, there’s something similar for videos called CDVA (Compact Descriptors for Video Analysis) that’s capable of analyzing the scene taking place, and describe it in a precise manner. The CDVA standard, aka MPEG ISO/IEC 15938-15, describes how video features can be extracted and stored as compact metadata for efficient matching and scalable search. Gyrfalcon published a press release, their Lightspeeur line of AI chips will adapt CDVA. You can get the technical details in that paper entitled “Compact Descriptors for Video Analysis: the Emerging MPEG Standard”. CDVA still relies on (CNN Convoluted Neural Network) but do so but extracting frames first, append a timestamp and the encoded CDVA descriptor to the video, which […]

August 21, 2019 by Jean-Luc Aufranc (CNXSoft) - 8 Comments

Getting Started with Sipeed M1 based Maixduino Board & Grove AI HAT for Raspberry Pi

Last year we discovered Kendryte K210 processor with a RISC-V core and featuring AI accelerators for machine vision and machine hearing. Soon after, Sipeed M1 module was launched with the processor for aroud $10. Then this year we started to get more convenient development board featuring Sipeed M1 module such as Maixduino or Grove AI Hat. Seeed Studio sent me the last two boards for review. So I’ll start by showing the items I received, before showing how to get started with MicroPython and Arduino code. Note that I’ll be using Ubuntu 18.04, but development in Windows is also possible. Unboxing I received two packages with a Maixduino kit, and the other “Grove AI HAT for Edge Computing”. Grove AI HAT for Edge Computing Let’s start with the second. The board is a Raspberry Pi HAT with Sipeed M1 module, a 40-pin Raspberry Pi header, 6 grove connectors, as well […]

August 1, 2019 by Jean-Luc Aufranc (CNXSoft) - 8 Comments

HuskyLens AI Camera & Display Board is Powered by Kendryte RISC-V Processor (Crowdfunding)

A couple of years ago, I reviewed JeVois-A33 computer vision camera powered by Allwinner A33 quad-core Cortex-A7 processor running Linux. The tiny camera would implement easy-to-use software for machine vision with features such as object detection, eye tracking, QR code and ArUco marker detection, and so on. The camera could handle the tasks at hand, but since it relied on purely software computer vision, there were lag for some of the demo applications including 500ms for single object detection, and up to 3 seconds for YOLO test with multiple object types using deep learning algorithms. That’s a bit slow for robotics project, and software solutions usually consume more than hardware accelerated ones. Since then, we’ve started to see low-cost SoC and hardware with dedicated hardware AI accelerators, and one of those is Kendryte K210 dual-core RISC-V processor with a built-in KPU Convolutional Neural Network (CNN) hardware accelerator and APU audio […]