Arnaud Darmont coordinates standardization activities of the association.

The European Machine Vision Association (EMVA) has appointed Arnaud Darmont as new EMVA Standards Manager. Arnaud will be responsible to promote the European machine vision standardization activities worldwide and to coordinate the development process of machine vision standards. In addition, in a rapidly changing industrial environment another major task will be to identify new standardization needs.

“We are thrilled to have Arnaud joining the EMVA team to fill such an important role as Standards Manager. He brings profound and long machine vision experience in engineering, management and marketing; and has been working on CMOS image sensors, industrial cameras, image quality, and embedded processing. Furthermore, Arnaud is one of the developers of the EMVA1288 standard. We are looking forward to working with him to take the EMVA standardization activities to the next level”, says EMVA President Jochem Herrmann.

Since almost 15 years, the European Machine Vision Association (EMVA) is hosting the development of standards for the machine vision industry with the now well-known and widely used standards Gen<i>cam and EMVA1288. Gen<i>cam standardizes the high level interfacing of a vision device and a computer. EMVA1288 is a characterization and specification procedure for image sensors and cameras used in machine vision.

Since 2009, the EMVA is collaborating with other international machine vision associations worldwide to work towards a standardization of the technologies and processes in our industry. These joint global standardization activities have become a pillar of the success of machine vision technology in numerous industrial and non-industrial applications.

Neil Martin from MVPro Media caught up with Boaz Arad, winner of the EMVA Young Professional Award 2017, and asked him about his position as CTO of the start-up company HC-Vision.

Evaluation of machine vision markets in France.

The European Machine Vision Association (EMVA) has published its 2017 Market Report “Machine Vision in France”.
EMVA non-members can obtain the 56 paged pdf-report at a price of € 345,- plus VAT through info@emva.org.

The 56 page report maps the machine vision activities in France in all its facets. It covers the machine vision industry, their customers as well as technical and commercial trends. In addition, the “eco system” for machine vision is described, including clusters, research centers and associations, trade shows and special magazines. This is being supplemented by market and growth drivers and an estimate of the market volume.

The report identifies more than 440 players in the French machine vision market, including manufacturers of components and systems, integrators, direct sellers and distributors as well as a long list of universities and research institutes with strong vision competence. All of them are listed by name and website in the report.

“Integrators play an important role in the industrial implementation of machine vision in the French machine vision market. Also, with no dominant component manufacturing scene the role of national distributors and direct sales of components from international players is rather distinguished in France. In addition, due to the overall structure of the French economy national customers of vision technology tend to be either big corporate entities such as from the aerospace, military or automotive sector or rather small, leaving a gap in the SME sector that is quite strong in other European countries”, says Andreas Breyer, EMVA’s Director of Market Research and adds: “What is remarkable is the distinctive strength France shows in the research sector. More than 40 percent of the players we identified are active in machine vision or computer vision research or education.”

With the current 2017 market report the EMVA continues the strategy to investigate the European markets through the eyes of the machine vision industry. EMVA non-members can obtain the pdf-report at a price of € 345,- plus VAT through info@emva.org.

Chronocam’s technology strategy is built on a straight-forward premise: in order to enable a safer, more efficient world though the capabilities of machine vision, we need to re-think traditional vision and processing. Our technology introduces a new computer vision paradigm based on how the human eye and brain work. Our approach significantly improves the performance and power efficiency of how computer vision can be implemented in a wide range of products and applications that improve the convenience and safety of our daily lives. Chronocam addresses head on the obstacles faced by previous generations of camera technology to meet the needs of modern applications in automotive, consumer, IoT, and industrial products.

‘Deep learning for machine vision’

Olivier Despont, ViDi Product Marketing Specialist at Cognex, presents at the 2017 Embedded VISION Europe conference.

Passionate by innovative and cutting-edge technologies, Olivier Despont spent the last 3 years working for ViDI systems SA, a swiss leading AI software company developing the business and setting up the sales channel to promote its Deep Learning Software in Asia and North America.  Following the recent acquisition Olivier is now the Product Marketing Specialist for Deep Learning at Cognex.

Olivier holds a Master degree in Management from Fribourg University (Switzerland) and Napier University in Edinburgh (Scotland).

Abstract of  Olivier’s presentation:

Today, enterprises are leveraging Machine Vision solutions for extending the capabilities of manufacturing machines through image processing and analytics. To interact with the world in a meaningful way, machines must first understand images. However, traditional computer vision solutions are limited in performance and can hardly manage changing or unpredictable environments. In real-time operation, conventional solutions require supervised learning, extensive training and faster computing, thereby, limiting the success of machine learning-based products to content filtering and speech recognition. Hence, the gap between what can be done with Artificial Intelligence (AI) in the lab and what is done in real-world applications is huge. Cognex Corp, with its new Deep learning Tool Suite bridges the gap by allowing Machine Vision companies across multiple industries to create ground-breaking inspection systems to tackle otherwise impossible to program both functional and aesthetics anomalies inspection & classification.

 

The debut of EMVA’s brandnew conference Embedded VISION Europe, supplemented by an already well booked table top exhibition, will take place 12-13 October 2017 in Stuttgart.

Find all conference details at www.embedded-vision-emva.org

‘Enabling Computer Vision and Deep Learning for Real Life’

Alexey Myakov, Chief Computer Vision Advocate at Intel Corporation, presents keynote at the 2017 Embedded VISION Europe conference.

Alex serves as Chief Computer Vision (CV) Advocate at Intel. In this role he contributes to collaboration across Intel with regards to all aspects of CV and DL, works with IoTG’s BUs on various markets’ and customers’ CV requirements as well as drives data related activities across Intel.

He joined Intel in July, 2016 through acquisition of Itseez Inc (widely known as a developer and supporter of OpenCV) by Intel. Formerly the CEO of Itseez, he joined Itseez in 2013 as Chief Marketing Officer (CMO) with a mandate to grow business and turn the company around by focusing it on products vs services.

Alex got a diverse technological and business background. He ran 3 start-up companies in the biomedical field (early cancer diagnostics) from 2003 till 2006 and worked as a VP, Business Development/Sales at the software company MERA from 2006 to 2013.

He holds BSc and Msc in Physics, and did post graduate studies at the Institute of Applied Physics of Russian Academy of Sciences and University of Texas at Austin in Physics and electrical/biomedical engineering respectively.

‘Innovations in camera processing and computer vision for IoT applications’

The EMVA proudly presents Raj Talluri, Senior Vice President of Product Management at Qualcomm Technologies, giving his keynote  at Embedded VISION Europe conference.

Raj Talluri serves as senior vice president of product management for Qualcomm Technologies, Inc. (QTI), where he is currently responsible for managing QTI’s Internet of Things (IoT) business.

Prior to this role, he was responsible for product management of mobile computing, Sense ID 3D finger print technology and Qualcomm Snapdragon Application Processor technologies. Talluri has more than 20 years of experience spanning across business management, strategic marketing, and engineering management.

He began his career at Texas Instruments (TI), working on media processing in their corporate research labs. During that time, Talluri started multiple new businesses in digital consumer electronics and wireless technologies. He also served as general manager of the Imaging and Audio business for five years, where he led the development of successful digital signal processing technologies for various consumer electronics devices. Later, Talluri was named general manager of the Cellular Media Solution business in TI’s Wireless Terminals Business Unit. In this role, he led the successful launch of TI’s OMAP3 and OMAP4 application processor platform for smartphones.

Talluri holds a Ph.D in electrical engineering from the University of Texas at Austin. He also holds a Master of Engineering degree from Anna University in Chennai, India and a Bachelor of Engineering from Andhra University in Waltair, India.

He has published more than 35 journal articles, papers, and book chapters in many leading electrical engineering publications. He has been granted 13 U.S. patents for image processing, video compression, and media processor architectures.

Talluri was chosen as No. 5 on Fast Company’s list of 100 Most Creative People in Business in 2014.

Abstract of  Raj’s presentation:

In the last couple of decades we have seen tremendous advances in visual computing technologies. The smartphone revolution has led to new breakthroughs in camera processing, machine learning and computer vision, which are now finding their way into many Internet of Things applications – including self-driving cars, virtual reality headsets, connected cameras, autonomous robots and more. This talk will highlight some of the key innovations in the area of visual processors and drill deeper into what future innovations to expect and the impact of these processing innovations on future vision applications.

The Embedded VISION Europe presents 15 expert talks and presentations covering embedded vision hardware platforms, software tools and deep learning, image acquisition and a range of application success stories. Throughout the conference, 25 companies showcase their products & services at the accompanying table top exhibition.

Don’t miss this outstanding technology event. Get registered today at www.embedded-vision-emva.org

Munich based Kortiq is focusing on easy-to-use, small. efficient, scalable and flexible FPGA based machine learning hardware accelerators to enable fast and efficient implemention of algorithms and methodologies in artifical intelligence applications. Kortiq’s first design, the AIScale convolutional neural network accelerator, provides low cost edge machine learning inference for the embedded vision industry. AIScale makes it easy for system designers to implement machine learning functionality, such as image recognition, and helps enabling this new technology in their industrial embedded vision- or robotics systems. The novel way of mapping calculations to hardware resources in combination with highly advanced compression methods, which offer a significant reduction in required external memory transfer size and power, enable a fast turnaround from idea to product, with having an efficient and economic solution in mind. For more information visit: www.kortiq.com

‘Democratizing deep learning – a low cost food product identification approach designed for embedded devices’

The EMVA is happy to welcome Dr. Vassilis Tsagaris, CEO at IRIDA Labs, presenting a use case from the food industry at Embedded VISION Europe conference.

Vassilis Tsagaris founded IRIDA Labs in 2009 with the aim to bridge the gap between a camera and a human eye by bringing visual perception to any device, making embedded computer vision accessible to everyone. Today, as CEO of IRIDA Labs he is responsible for corporate strategy, business partnerships and business development worldwide. IRIDA Labs is partnered with large international companies like Qualcomm, Cadence and CEVA (all NASDAQ listed) and has a client basis in Europe, China and USA.
Prior founding IRIDA Labs, he has worked as a researcher, post-doc researcher or project manager for about ten European and National R&D projects for the academic and company sector. He has BSc. In Physics and a PhD in Computer Vision and Data Fusion from the University of Patras, Greece, and has published more than 30 journal and conference papers

Abstract of  Vassilis’ presentation:

Deep learning has recently emerged as the dominant approach for performing various classification tasks ranging from computer vision to speech processing. For computer vision, Deep Convolutional Neural Networks (CNNs), are incorporating end-to-end learnable modules able to achieve robust feature representations. However, CNN based approaches developed by technology giants like Google, Baidu or others often require large amounts of data for training and are computationally intensive during evaluation, which makes them impractical or even prohibitive for embedded or time-critical applications.
In this presentation we are going to present how we democratize deep learning by studying the application scenario and hardware platform in order to be able to transfer the knowledge and accuracy of large scale CNN networks in an embedded device, thus making deep learning a powerful tool for everybody.
A case study is presented for the food recognition scenario where we have conducted analysis utilizing the FOOD 101 database which is comprised by images of food taken into different conditions and it is organized to 101 categories. We will first present the results of our CNN-based approach outperforming conventional approaches and then we will discuss how we implement the inference or evaluation part of the CNN structure in a common ARM based CPU embedded system achieving the low power and high speed performance needed for this case study.
Finally, we will discuss how this approach is applied in a food preparation environment in order to categorize between predefined products (like bakery products) in an unconstrained environment where embedded deep learning at the edge provides breakthrough solutions to challenging computer vision problems.

The debut of EMVA’s brandnew conference Embedded VISION Europe, supplemented by an already well booked table top exhibition, will take place 12-13 October 2017 in Stuttgart.

Find all conference details at www.embedded-vision-emva.org