Computer Vision Central publishes 1,000th news item
One thousand days ago Computer Vision Central started publishing a daily news item. In that short span of time -- less than three years -- the field has exploded. A look back through the news items we've published on this site proves that participation in computer vision encompasses all regions of the world; includes many types of actors -- individuals, academia, large companies, startups, governments, and non-profits; and benefits too many different industries to list. Computer vision has truly gone mainstream: it is embedded in our devices including smartphones, cars, and video game consoles; it is a topic of conversations in social networks; and it is regularly covered in mainstream news channels.
What's next? With so much happening in the computer vision world, and so much of it easy to find via social networks, a centralized limited-authorship computer vision news blog is less necessary today than it was 1,000 days ago. We will refocus the site on its most popular crowdsourced features: computer vision jobs, forums, and conferences. The computer vision jobs board will be the new front page. We will no longer post daily news, although all existing articles will continue to be available. If you see something in the computer vision world that excites you, we encourage you to share it with your fellow computer vision enthusiasts by posting about it in Computer Vision Central's forum!
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Robotic automation coming to plant nurseries
Eric Smalley writes in a Wired report that several companies are testing autonomous robots for plant nurseries that grow ornamental shrubs and trees. The robots use computer vision and other sensors to navigate and to pick up and deliver potted shrubs and trees. One startup, Harvest Automation, has raised $5 million in venture capital. A video of the robots in action is available here.
Related articles on Computer Vision Central:
- Computer vision for agriculture increases profits, reduces waste
- Google awards research grant in mobile crop surveillance
- Computer Vision used to target individual weeds
- Vision-based pruning system reduces labor costs in vineyards
- Senate-approved Department of Agriculture appropriations bill includes funding for computer vision staff
- Vision analysis can estimate crop yields earlier
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U.S. funds research towards street intersection analyzer for the blind
The U.S. National Institutes of Health (NIH) is providing $400,000 in funding to Dr. James Coughlan, of the Smith-Kettlewell Eye Research Institute, to develop tools to help the visually impaired. Building on the team's existing computer vision software for detecting crosswalks and 'walk' lights, the new system will help the blind and visually impaired navigate across intersections via continous feedback. The goal is to deploy the system in the form of free smartphone apps for common platforms such as Android and iOS so that users will not have to carry a separate device.
Abstract
Urban intersections are among the most dangerous parts of a blind person's travel. They are becoming increasingly complex, making safe crossing using conventional blind orientation and mobility techniques ever more difficult. To alleviate this problem, we propose to develop and evaluate a cell phone-based system to analyze images of street intersections, taken by a blind or visually impaired person using a standard cell phone, to provide real-time feedback. Building on our past work on a prototype "Crosswatch" system that uses computer vision algorithms to find crosswalks and Walk lights, we will greatly enhance the functionality of the system with information about the intersection layout and the identity of its connecting streets, the presence of stop signs, one-way signs and other controls indicating right-of-way, and timing information integrated from Walk/Don't Walk lights, countdown timers and other traffic lights. The system will convey intersection information, and will actively guide the user to align himself/herself with crosswalks, using a combination of synthesized speech and audio tones. We will conduct human factors studies to optimize the system functionality and the configuration of the user interface, as well as develop interactive training applications to equip users with the skills to better use the system. These training applications, implemented as additional cell phone software to complement the intersection system, will train users to hold the camera horizontal and forward and to minimize veer when traversing a crosswalk. The intersection analysis and training software will be made freely available for download onto popular cell phones (such as iPhone, Android or Symbian models). The cell phone will not need any hardware modifications or add-ons to run this software. Ultimately a user will be able to download an entire suite of such algorithms for free onto the cell phone he or she is already likely to be carrying, without having to carry a separate piece of equipment for each function.
Public Health Relevance
The ability to walk safely and confidently along sidewalks and traverse crosswalks is taken for granted every day by the sighted, but approximately 10 million Americans with significant vision impairments and a million who are legally blind face severe difficulties in this task. The proposed research would result in a highly accessible system (with zero or minimal cost to users) to augment existing wayfinding techniques, which could dramatically improve independent travel for blind and visually impaired persons.
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NVIDIA introduces Tegra 3 for tablets and phones
NVIDIA has introduced the quad-core Tegra 3 for mobile devices including tablets and phones. This embedded system was formely code-named "Project Kal-El". In addition to the four high-performance main cores, the Tegra 3 comes with a fifth low-power core which is used when high-performance applications are not running, to extend battery life. There is also a new 12-core GeFORCE GPU. The Tegra 3 will provide a powerful new platform for mobile computer vision applications.
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Tracking all the players on the field
A team of researchers from Switzerland's Ecole Polytechnique Fédérale de Lausanne (EPFL) have developed a system that can continously track every player on the field in a sporting event, even through occlusion events. The system is entirely based on eight cameras positioned around the field, and does not require the players to wear any devices. The research was presented at ICCV 2011 this week. More information is available in a press release.
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Students win competition with Kinect visual gait analysis
In the recent Siemens Competition in Math, Science and Technology held at Georgia Tech, high school seniors Ziyuan Liu and Cassee Cain took first place in the regional championship. Their project used the Xbox Kinect and computer vision to analyze human gait. The Oak Ridge High School seniors will advance to the national competition on Dec. 2-5 in Washington D.C., where they will compete for a $100,000 scholarship. More information is available in a Knoxnews article.
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New MIT algorithm speeds up MRI scans
Researchers from MIT (Elfar Adalsteinsson and Vivek Goyal) have developed a new imaging algorithm that could reduce magnetic resonance imaging (MRI) scans from 45 to 15 minutes. Using information from the MRI's first contrast scan, the algorithm predicts the likely object boundaries between different types of tissues in subsequent scans. The researchers are now working to improve the algorithm and accelerate the processing time on a GPU. More information is available in a MIT news article.
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Improving smartphone cameras
The U.S. National Science Foundation (NSF) has awarded $450,000 to Colorado-based company FiveFocal to improve the manufacturing of camera modules for mobile phones and other platforms. The grant is primarily targeted to increasing yields, which would reduce costs; however, it is likely that the improved manufacturing process monitoring will lead to higher quality as well. Increasing numbers of apps that require computer vision software are being written for smartphone platforms; higher and more consistent imaging quality will enhance the performance of many of those computer vision apps. A detailed description of the project is included below.
Research Abstract
This Small Business Innovation Research (SBIR) Phase II project will develop and test real-time process monitoring systems to support manufacturing of miniature digital cameras. Rapid growth in unit volume of digital cameras for cellphones and consumer goods has outpaced the industry?s manufacturing process monitoring technology. Except for simple pass/fail outgoing quality tests, high volume camera manufacturers lack any system for in-line, real-time monitoring of production errors that cause low yields, high production costs, and delay new product introduction. The Real-time Camera Analysis and Process Tracking algorithm, ReCAPT, integrates with existing production equipment to identify manufacturing errors and trends before product quality is compromised. ReCAPT leverages outgoing QC data, along with novel design-aware algorithms to identify assembly and fabrication errors and improve the manufacturing process. The Phase II objectives include optimizing the data collection hardware and pre-processing software, automating and generalizing the algorithm initialization, and integrating ReCAPT into the production environment through improvements to the algorithm?s robustness. With a key commercialization partner, ReCAPT will be tested multiple times in actual production environments with potential customers reviewing the results. The results will determine the achievable improvement in production efficiency, and quantify ReCAPT?s economic value to manufacturers of digital cameras.
The broader impact/commercial potential of this project involves improving yields in the production of miniature camera lenses. Over one billion miniature digital cameras produced annually supply the explosive growth in cell phones and other mobile consumer electronics. The pursuit of cost reduction has led to development of wafer-level manufacturing where thousands of camera lenses are simultaneously fabricated, affixed to a wafer of image sensors and then diced ? potentially eliminating the need for individual component assembly. By improving yields and lowering costs, ReCAPT will enable the rapid adoption of wafer-level and other automated, capital intensive camera manufacturing technologies. The broader impact is the development of manufacturing technologies that rely on automation and precision engineering instead of manual labor, enabling US companies to gain traction in the growing $15 Billion annual digital camera market. The statistical manufacturing process data supplied by ReCAPT enables real-time control of manufacturing, reduces new product risk, and allows more aggressive development of innovative camera technology. Sold as an enhancement to existing automated manufacturing equipment, the ReCAPT software product will increase profit for component manufacturers, improve product performance and performance consistency for consumer goods manufacturers.
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Stanford Decaptcha identifies CAPTCHA weaknesses
Researchers at Stanford University have developed a tool to automatically decipher and defeat CAPTCHAs (Completely Automated Public Turing tests to tell Computers and Humans Apart). The research team (Elie Bursztein, Matthieu Martin, and John C. Mitchel) developed "Decaptcha" using various methods of cleaning up intentional background noise and breaking text strings into individual characters for easier recognition. They ran against CAPTCHAs used by 15 high-profile Web sites, and the only tested site that could not be broken was Google. Several recommendations to improve CAPTCHA security were provided, which include randomizing the length of the text string, randomizing the character size, applying a wave-like effect to the output, and using a collapsing background with lines. More information is available in a Network World article.
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Apple buys C3 Technologies
According to GPS Business News, Apple is buying C3 Technologies for $267 million. C3 Technologies is a Swedish company that provides 3D images of cities. It has vision technology to create 3D models from oblique camera systems without LIDAR.
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