Collaborating Authors


These Are the First 100% AI-Generated Stock Photos of People


Smarterpix, Germany's leading stock photo agency, has announced an industry-first: a set of stock portraits that are 100% generated by artificial intelligence (AI) and available for legal licensing. Smarterpix is operated by PantherMedia, the first microstock agency in Germany, which partnered with VAIsual, a technology company that pioneers algorithms and solutions to generate synthetic licensed stock media. The two have come together to offer the first set of 100% AI-generated, licensable stock photos of "people." None of the photos are of people who actually exist. PantherMedia says that phase one of its partnership with VAIsual will see a library of human portraits appear on Smarterpix, all of which can be generated with a green-screen background which allows them to be easily merged or inserted with other synthetic elements or real-life photography backgrounds to create entirely new content.

Artificial Intellgence -- Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021 Artificial Intelligence

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

NICER: Aesthetic Image Enhancement with Humans in the Loop Artificial Intelligence

Fully- or semi-automatic image enhancement software helps users to increase the visual appeal of photos and does not require in-depth knowledge of manual image editing. However, fully-automatic approaches usually enhance the image in a black-box manner that does not give the user any control over the optimization process, possibly leading to edited images that do not subjectively appeal to the user. Semi-automatic methods mostly allow for controlling which pre-defined editing step is taken, which restricts the users in their creativity and ability to make detailed adjustments, such as brightness or contrast. We argue that incorporating user preferences by guiding an automated enhancement method simplifies image editing and increases the enhancement's focus on the user. This work thus proposes the Neural Image Correction & Enhancement Routine (NICER), a neural network based approach to no-reference image enhancement in a fully-, semi-automatic or fully manual process that is interactive and user-centered. NICER iteratively adjusts image editing parameters in order to maximize an aesthetic score based on image style and content. Users can modify these parameters at any time and guide the optimization process towards a desired direction. This interactive workflow is a novelty in the field of human-computer interaction for image enhancement tasks. In a user study, we show that NICER can improve image aesthetics without user interaction and that allowing user interaction leads to diverse enhancement outcomes that are strongly preferred over the unedited image. We make our code publicly available to facilitate further research in this direction.

Multi-Kernel Prediction Networks for Denoising of Burst Images Machine Learning

In low light or short-exposure photography the image is often corrupted by noise. While longer exposure helps reduce the noise, it can produce blurry results due to the object and camera motion. The reconstruction of a noise-less image is an ill posed problem. Recent approaches for image denoising aim to predict kernels which are convolved with a set of successively taken images (burst) to obtain a clear image. We propose a deep neural network based approach called Multi-Kernel Prediction Networks (MKPN) for burst image denoising. MKPN predicts kernels of not just one size but of varying sizes and performs fusion of these different kernels resulting in one kernel per pixel. The advantages of our method are two fold: (a) the different sized kernels help in extracting different information from the image which results in better reconstruction and (b) kernel fusion assures retaining of the extracted information while maintaining computational efficiency. Experimental results reveal that MKPN outperforms state-of-the-art on our synthetic datasets with different noise levels.

CeBIT 2016: The Aerotain Skye Could Be Your Friendly Floating Camera Drone

IEEE Spectrum Robotics

Editors Note: This week IEEE Spectrum is covering CeBIT, the enormous information and communications technology show that takes place annually in Hanover, Germany. For up-to-the-second updates, you can follow our CeBIT Ninja, Stephen Cass, on Twitter (@stephencass), or catch daily highlights throughout the week here. Once upon a time there was a very odd British television show called The Prisoner, which featured a secret agent repeatedly attempting to escape from a mysterious village. One of the biggest threats the agent faced was a giant balloon called Rover, which would pursue and subdue rule-breaking villagers. Now Rover has been brought to reality, albeit in a much more adorable version, thanks to the engineers at Aerotain and their Skye inflatable drone.