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How a path-tracing method could help train next-generation event cameras

A new path-tracing method simulates event camera data from virtual 3D scenes efficiently, reducing computational costs for applications like autonomous driving and robotics. The method uses physically-based path tracing and adaptive temporal search to precisely determine event timings, enabling more efficient training-data generation.

SourceChiba University·JournalIEEE Transactions on Visualization and Computer Graphics·TypeComputational simulation/modeling·DateOct 1, 2026

Disco lasers improve the safety of snow groomers

Researchers developed a disco laser system to enhance data visualization for snow groomers, improving operator comfort and reducing nausea caused by VR headsets. The system also enables better tracking and orientation aids, leading to more efficient and safe operation in challenging conditions.

SourceGraz University of Technology·JournalComputers & Graphics·TypeComputational simulation/modeling·DateMay 28, 2026

The ingenuity of white oval squid camouflage brought to light

The white oval squid employs a range of survival strategies, including color matching, disruptive patterns, and synchronized schooling. By analyzing the mathematical patterns behind their behavior, researchers have confirmed the effectiveness of these strategies in evading predators and camouflaging in diverse environments.

SourceOkinawa Institute of Science and Technology (OIST) Graduate University·JournalScientific Reports·TypeObservational study·DateNov 24, 2025

KAIST researchers unveil an AI that generates "unexpectedly original" designs​

Researchers at KAIST have developed a technology to enhance creative generation of AI generative models like Stable Diffusion, generating novel and useful images. The algorithm amplifies internal feature maps to boost creativity without new training, outperforming existing methods in novelty and utility.

SourceThe Korea Advanced Institute of Science and Technology (KAIST)·TypeComputational simulation/modeling·DateJun 20, 2025

Physisorption-assistant optoelectronic synaptic transistors based on Ta2NiSe5/SnS2 heterojunction from ultraviolet to near-infrared

Researchers developed physisorption-assistant optoelectronic synaptic transistors based on Ta2NiSe5/SnS2 heterojunction, demonstrating tunable synaptic functionality in broadband (375-1310 nm). The strategy utilizes gas molecule adsorption to extend carrier lifetime and improve NIR light performance.

HKU researchers unveil neuromorphic exposure control system to improve machine vision in extreme lighting environments

Researchers at HKU have developed a neuromorphic exposure control system that mimics human peripheral vision to achieve unprecedented speed and robustness in dynamic perception environments. The system operates at 130 million events/sec, enabling edge deployment and addressing limitations of traditional exposure control.

SourceThe University of Hong Kong·JournalNature Communications·TypeComputational simulation/modeling·DateMar 3, 2025

Machine vision under low-light conditions improved

Researchers developed a system to detect and decode fiducial markers in challenging lighting conditions using neural networks. The system, DeepArUco++, overcomes the limitations of classic machine vision techniques and can be applied today thanks to open availability of its code.

SourceUniversity of Córdoba·JournalImage and Vision Computing·TypeExperimental study·DateJan 24, 2025

Gwangju Institute of Science and Technology researchers develop cat's eye-inspired vision system for autonomous robotics

Researchers at GIST developed a cat's eye-inspired vision system that filters out unnecessary light and improves visibility in low-light conditions. The system promises to elevate the precision of drones, security robots, and self-driving vehicles, enabling them to navigate intricate environments with unparalleled accuracy.

SourceGIST (Gwangju Institute of Science and Technology)·JournalScience Advances·TypeExperimental study·DateOct 15, 2024

You're just a stick figure to this camera

A new camera system called PrivacyLens can replace people in images with generic stick figures, protecting their identities and reducing unnecessary surveillance. This technology could prevent embarrassing photos from being shared online and make patients more comfortable using cameras for chronic health monitoring.

Innovations in depth from focus/defocus pave the way to more capable computer vision systems

A new depth from focus/defocus approach, DDFS, combines model-based and learning-based strategies to achieve notable improvements in performance and applicability. The proposed method outperformed state-of-the-art methods in various metrics for several image datasets.

SourceNara Institute of Science and Technology·JournalInternational Journal of Computer Vision·TypeComputational simulation/modeling·DateFeb 9, 2024

Autonomous excavator constructs a 6-meter-high dry-stone wall

Researchers at ETH Zurich developed an autonomous excavator called HEAP to construct a 6-meter-high and 65-meter-long dry-stone wall. The excavator uses sensors, machine vision, and algorithms to place stones in the desired location, achieving a high level of precision and speed.

SourceETH Zurich·JournalScience Robotics·TypeExperimental study·DateNov 22, 2023

How human faces can teach androids to smile

A recent study by Osaka University's researchers aims to bring science fiction stories closer to reality by studying the mechanical properties of human facial expressions. The team mapped out the intricacies of human facial movements using tracking markers, revealing that even simple motions can be surprisingly complex and nuanced.

SourceOsaka University·JournalMechanical Engineering Journal·TypeData/statistical analysis·DateNov 9, 2023

Integration propels machine vision

A joint research team published a review on in-sensor visual computing, a three-in-one hardware solution that overcomes high latency, power consumption, and privacy risks. The SCAMP chip is a key device, enabling general-purpose, programmable, and massively parallel systems for robotics and computer vision.

SourceIntelligent Computing·JournalIntelligent Computing·DateSep 27, 2023

School of Science researchers use AI to innovate insect discovery

A team of IUPUI researchers has developed an AI-powered approach to classify insect species, tackling the challenge of discovering new species. The method uses deep hierarchical Bayesian learning to distinguish between known and unknown species, providing insight into their taxonomy and ecosystem impacts.

SourceIndiana University-Purdue University Indianapolis School of Science·JournalMethods in Ecology and Evolution·TypeComputational simulation/modeling·DateApr 27, 2023

Portable and affordable all-optical system for testing lab-on-a-chip human hearts

Researchers have developed a novel portable and low-cost macroscopic mapping system for all-optical cardiac electrophysiology using optogenetics and machine vision cameras. The system can stimulate and image engineered networks of human heart cells, providing insights into cardiac wave function and stability.

SourceSPIE--International Society for Optics and Photonics·JournalJournal of Biomedical Optics·DateJan 11, 2023

LOEN: Lensless opto-electronic neural network empowered machine vision

A new lensless opto-electronic neural network (LOEN) architecture is developed for computer vision tasks, utilizing a passive mask to perform convolution operations in the optical field. The system achieves high recognition accuracy and energy efficiency compared to traditional machine vision links.

Instagram teaches AI to recognize rooms

Researchers at the University of Groningen have developed an AI system that can recognize indoor spaces with high accuracy by combining image and audio data. The system achieved a 70% accuracy rate in recognizing nine different types of indoor spaces, surpassing previous results.

SourceUniversity of Groningen·JournalNeural Computing and Applications·TypeExperimental study·DateJan 26, 2022

Spiders’ web secrets unraveled

Johns Hopkins researchers used AI and infrared cameras to track every movement of a spider's eight legs as it built its web. They found that web-making behaviors are similar across spiders, with the same rules governing their construction. This discovery sheds light on how small brains support complex architectural creations.

SourceJohns Hopkins University·JournalCurrent Biology·DateNov 1, 2021

Paint the town

A team of scientists from Osaka University developed a machine learning method for classifying the type of building and its primary façade color using deep learning models applied to street-level images. This work may assist in fostering neighborhood cohesion and support urban renewal by providing tailored street-view datasets.

SourceOsaka University·JournalISPRS International Journal of Geo-Information·DateAug 31, 2021

Scientists discover hidden structure of enigmatic 'backwards' neural connections

Researchers at Champalimaud Centre for the Unknown uncover exquisitely organized map of visual space in feedback connections, providing insights into visual perception. The study reveals that these connections encode information from further locations in visual space, giving lower structures contextual 'whole picture' information.

SourceChampalimaud Centre for the Unknown·JournalNature Neuroscience·DateApr 16, 2018

When is a Pollock not a Pollock?

A machine vision approach has demonstrated 93% accuracy in spotting true Pollocks, verifying the authenticity of Jackson Pollock's drip paintings. The software, developed by Lior Shamir, analyzes numerical image descriptors and quantifies details at the pixel level to reveal specific features and textures unique to Pollock's style.

SourceInderscience Publishers·JournalInternational Journal of Arts and Technology·DateFeb 10, 2015