(EMAILWIRE.COM, October 23, 2024 ) The global Computer Vision in Healthcare market growth forecasted to transform from USD 3.9 billion in 2024 to USD 11.5 billion by 2029, driven by a CAGR of 24.0%. Market growth is propelled by the regulatory initiatives and incentives promote the adoption of computer...
Computer vision syndrome, also referred to as digital eyestrain, encompasses a group of vision-related problems that result from prolonged computer, tablet, e-reader and smartphone use, according to the American Optometric Association.
Soft X-ray spectroscopy is an important technique for measuring the fundamental properties of materials. However, for measurements of samples in the sub-millimetre range, many experimental setups show limitations. Position drifts on the order of hundreds of micrometres during thermal stabilization of the system can last for hours of expensive beam time. To compensate for drifts, sample tracking and feedback systems must be used. However, in complex sample environments where sample access is very limited, many existing solutions cannot be applied. In this work, we apply a robust computer vision algorithm to automatically track and readjust the sample position in the dozens of micrometres range. Our approach is applied in a complex sample environment, where the sample is in an ultra-high vacuum chamber, surrounded by cooled thermal shields to reach sample temperatures down to 2.5 K and in the center of a superconducting split coil. Our implementation allows sample-position tracking and adjustment in the vertical direction since this is the dimension where drifts occur during sample temperature change in our setup. The approach can be easily extended to 2D. The algorithm enables a factor of ten improvement in the overlap of a series of X-ray absorption spectra in a sample with a vertical size down to 70 µm. This solution can be used in a variety of experimental stations, where optical access is available and sample access by other means is reduced.
By Carlos Osuna, Head of R&D, Panasonic Connect Europe.
The retail industry is at an inflection point. Booming e-commerce volumes and changing consumer behaviour on the back of economic pressures are making customers more price-sensitive and less brand loyal.
Lars Pruijn, Innovation Director, and Lorenzo D'Arsie, Computer Vision Product Manager at Prime Vision, examine computer vision technology and the new opportunities it provides in the postal and parcel sectors.
Why Bengaluru startup Game Theory believes technology offers our best shot at sporting glory
This post will cover how testing is done for the coronavirus, why it's important in battling the pandemic, and how deep learning tools for medical imaging can help us improve the quality of COVID-19 testing.
Network security has become an area of significant importance more than ever as highlighted by the eye-opening numbers of data breaches, attacks on critical infrastructure, and malware/ransomware/cryptojacker attacks that are reported almost every day. Increasingly, we are relying on networked infrastructure and with the advent of IoT, billions of devices will be connected to the internet, providing attackers with more opportunities to exploit. Traditional machine learning methods have been frequently used in the context of network security. However, such methods are more based on statistical features extracted from sources such as binaries, emails, and packet flows.
On the other hand, recent years witnessed a phenomenal growth in computer vision mainly driven by the advances in the area of convolutional neural networks. At a glance, it is not trivial to see how computer vision methods are related to network security. Nonetheless, there is a significant amount of work that highlighted how methods from computer vision can be applied in network security for detecting attacks or building security solutions. In this paper, we provide a comprehensive survey of such work under three topics; i) phishing attempt detection, ii) malware detection, and iii) traffic anomaly detection. Next, we review a set of such commercial products for which public information is available and explore how computer vision methods are effectively used in those products. Finally, we discuss existing research gaps and future research directions, especially focusing on how network security research community and the industry can leverage the exponential growth of computer vision methods to build much secure networked systems.
When a person uses a computer, the graphic display of the computer screen causes a user's eye blink rate to be reduced by an average of sixty-six percent. This reduction in blinking can cause a variety of eye ailments, such as eye strain that results from lack of adequate blinking. This system is a light display system for compelling a computer user to blink their eyes. In one embodiment, the system includes a computer screen, a light source associated with a controller configured to execute a program to activate and deactivate the light source, wherein the light source turns the light on and off when the light source is activated, and deactivated. The systems and methods provide advantages in that a computer user will involuntarily blink as the light source is activated, which will increase the overall blink rate of the computer user's eyes, and reduce eyestrain therefrom.
oscon: Beginner's Guide to Computer Vision - 2D/3D image fundamentals, OpenCV, OpenNI Library + more http://t.co/ph2dKrC9W4 #oscon #tutorial
We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate rapid prototyping and promote reproducible research. We also provide modular APIs with flexible building blocks to enable efficient customization. Leveraging the MXNet ecosystem, the deep learning models in GluonCV and GluonNLP can be deployed onto a variety of platforms with different programming languages. The Apache 2.0 license has been adopted by GluonCV and GluonNLP to allow for software distribution, modification, and usage.
Clinical pain assessment methods in youth are vulnerable to underestimation bias and underrecognition. Facial expressions are sensitive, specific biomarkers of the presence and severity of pain. Computer vision–based pattern recognition enables measurement of pain-related facial expressions from video.
This study demonstrates initial validity for developing computer vision algorithms for automated pain assessment in children. The system developed and tested in this study could provide standardized, continuous, and valid patient monitoring that is potentially scalable. (Read the full article)
A new contest aims to help first responders leverage computer vision algorithms and drone imagery during a search
New machine learning methods bring insights into how lithium ion batteries degrade, and show it's more complicated than many thought.
New machine learning methods bring insights into how lithium ion batteries degrade, and show it's more complicated than many thought.
Company: MNR Solutions Private Limited
Experience: 0 to 50
location: India
Ref: 24341108
Summary: Job Description:
Job Description:
Client has a large variety of data for research and analysis. All products have pictures and descriptions. Researchers in computer vision will work on object recognition, image/video processing,....