Global Market For Traffic Sign Recognition Solution – The Global Leader in Automotive Software and Services
CNET Asia – A CNET China News Network that brings you the most insightful news and research on China and Asia. CNET China, the fastest growing tech blog in Asia, has been a part of Asia’s digital news culture for four years running. With a growing number of subscribers and unique features, CNET Asia has made it one of the top blogs in Asia according to Techcrunch. com and Engadget. The blog has evolved out of an Asia-focus content management team with a Chinese-born journalist by the name of Michael Chen. Michael began writing about Chinese technology news more than five years ago, during the period when China was transitioning from state controlled to private companies. Now, the blog covers the latest technology and business stories from China, from mobile phones to 3D printing, drones to energy. CNET Asia serves as the online hub for China-focused digital news, offering the best of local, regional, national and international news to readers every day, and the best of news from the CNAS conference in Washington DC next week.
Machine Vision Market To Witness The Highest Growth in APAC region
“The introduction of the Internet has led to a dramatic increase in demand for applications that can provide the basis for autonomous driving,” the global leader in automotive software and services says in its report ‘Mobile Applications: The Market for Traffic Sign Recognition Solution,’ released yesterday.
The increasing adoption of the Internet, smart phone apps, machine vision technology and other technologies that can provide the basis for autonomous driving have enabled the driver to remain in his/her cab and do not have to rely on conventional traffic signals, as long as the sensors placed at intersections are able to recognize the driver’s movement. The driver can even take over the traffic lights after the signals have been manually triggered by an experienced driver.
Although traffic signals can be manually triggered by a driver as well as by an experienced driver, the sensors are more automated, and have the potential to recognize vehicle and pedestrian movement more quickly. The global market for traffic sign recognition solutions, according to the global leader in automotive software and services, is projected to be worth $3. 6 billion by 2015, at a CAGR of 8. 5% during the forecast period, for a total market value of $13. 7 billion by the end of 2012.
The increasing demand for vehicles with autonomous features is likely to boost the global market for traffic sign recognition solution considerably. The global market for traffic sign recognition solution is projected to grow from $2. 5 billion in 2012 to $3. 6 billion in 2015, at a CAGR of 8. 5%, for a total market value of $26. 5 billion in 2015. The market is expected to grow to $40. 9 billion by 2020, at a CAGR of 14. 8%, for a total market value of $57. 3 billion by the end of 2012.
The global leader in automotive software and services, IBM, in its latest report, ‘The Global Automotive Software Market to 2016 – Market Size, Company Profiles and Forecast to 2016’ released yesterday, expects that the global traffic sign recognition application market in automobiles will be worth $7. 6 billion by 2016, at a CAGR of 7. 6% during the forecast period, for a market value of $18. 5 billion by 2018.
A survey of PC-based machine vision systems
The last time there was a survey of PC-based machine vision systems was in 1989. At that time, the field lacked a unified approach to vision systems. However, today, all major vendors of PC-based machine vision systems have a comprehensive survey in the hands of their marketing departments. For obvious reasons, the survey was released in a rush, just before the release of the Intel PIII machine vision chips.
This survey focuses on the three most significant changes in computer vision in the past ten years: image classification, object detection, and object tracking.
This survey represents the opinion of a few computer vision experts who have worked on computer vision systems for 30 years or more. In that time span, the field has developed quite a bit of expertise in these subjects.
The survey should be viewed as a starting point for the following articles. The articles will be organized alphabetically and the survey will be cross-referenced.
The survey considers eight major areas of computer vision systems—image classification, object detection, object tracking, and machine learning—covering major fields such as stereo, pattern classification, and face and face recognition.
Computer vision systems are composed of three major components: the software, hardware, and sensors. A computer vision system often consists of two or more of these components. The software component is where the image processing and computer vision algorithms are produced. The hardware includes the image sensors and the computer vision chip that incorporates these components. The sensors, generally in the form of a camera, are generally arranged in the center of the chip. The image sensors are used to capture images of objects as they move in space. Because the movement of the object is in space, the camera converts the object position into an electrical signal. In addition to the electrical signal, a computer vision algorithm also analyzes the signal to recognize an object in the object image. In most cases, the optical system of the camera is in the form of a lens, a plan, or a zoom lens and is used to transform the electrical signal into a two-dimensional image called a pixel. As an image moves through each color, the lens converts one color into another and changes the image into a new pixel. The data that is acquired by the pixels are known as the image.
Key market players in the machine vision market
Practical applications-based research, with special emphasis on applications for industry and government are currently the two most important methods of research for machine vision. While practical applications-based research, with special emphasis on applications for industry and government are currently the two most important methods of research for machine vision. While practical applications-based research, with special emphasis on applications for industry and government are currently the two most important methods of research for machine vision.
This report provides an in-depth analysis of the key market participants, segments with respect to geography, industry, type and application, key developments, and forecast. The report also offers the latest developments in the market and forecasts into the coming years.
The following report segmented the global machine vision market on the basis of type, application, and geography.
Geographically, this report is segmented into several key regions, with sales, revenue and market share for each region sub-categorized.
The North America market for machine vision is considered to represent the largest market for machine vision due to factors such as rapid technological growth, strong demand for precision and accuracy, and strong manufacturing capabilities in the region. In addition, there are several advantages associated with the market due to the fact that in the region, technological developments in machine vision have led to increased penetration of applications in various applications.
The market player Key market players are the IBM Corporation, Cisco Systems Inc. , Raytheon Corporation, Raytheon Company, 3M Company, General Electric Company, Hewlett-Packard Company, Dassault Systèmes Systèmes, Intel Corporation, Microsoft, and Zalem.
Tips of the Day in Computer Hardware
I didn’t think I was going to be able to write one of these today. With the usual AMD CrossFireX setup set up, I was able to test my CrossFireX setup with my usual testing setup. However, I have only done few tests with each card, so I wanted to share the same testing setup to test the performance difference.
I have a Pentium G60 3. 2GHz with 3GB RAM, I had some trouble installing the nVidia drivers, so I had to use a Live CD. After the installation I was able to use the nVidia drivers with the NVIDIA graphics card. I installed the nVidia drivers first and then restarted my machine. I then installed the nVidia card in the machine.
Leave a Comment