🌟 Editor's Note
Welcome to another exciting week in the Vision AI ecosystem! We've got a packed newsletter full of insights, events, and inspiring stories from the heart of innovation.
🗓️ Top Stories
Luna Systems raises €1.5m for AI bike safety tech
Dublin-based Luna Systems secured €1.5 million in a late seed round to develop and commercialize AI-powered safety cameras for cyclists and motorcyclists. The funding, led by Fundracer Capital and EIT Urban Mobility with support from Enterprise Ireland, accelerates its Advanced Rider Assistance Systems to help reduce two-wheeler accidents. [link]Plainsight unveils the Plainsight Platform for enterprise vision AI
Plainsight launched its new Plainsight Platform, the first end-to-end VizOps solution to unify the full computer vision lifecycle. It simplifies development, deployment, monitoring, and scaling of vision AI with modular “Filters,” improving reliability and production readiness for enterprise applications. [link]Docker Vision deploys computer vision with Intel for shipping logistics
Docker Vision has deployed its AI-powered dOCR computer vision solution with Intel tech to automate port and shipping logistics. Using standard IP cameras and edge processing, it identifies containers, vehicles, and wagons with high accuracy, enhancing efficiency, reducing emissions, and digitizing terminal operations.[link]
🦄 Startup Spotlight
Spoor is a Norwegian biodiversity technology company that uses advanced AI and computer vision to monitor bird activity around onshore and offshore wind farms.
Its platform continuously detects, tracks, and classifies bird movements, delivering high-resolution data that supports environmental impact assessments, regulatory compliance, and operational decision-making throughout a project’s lifecycle.
By automating wildlife monitoring at scale and integrating with existing cameras, Spoor helps developers and operators reduce risk, streamline permitting, and protect biodiversity, enabling renewable energy growth that coexists responsibly with nature. (spoor.ai)
🔥 Paper to Factory
MaskInversion: Localized Embeddings via Optimization of Explainability Maps introduces a method to generate context-aware embeddings for specific image regions using pre-trained vision-language models like CLIP without retraining them.
Starting from an initial token, it iteratively refines a region embedding so its explainability map matches a given mask.
A gradient decomposition strategy improves efficiency. The resulting localized features enhance tasks like open-vocabulary retrieval, referring comprehension, localized captioning, and image generation. (link)
🏆 Community Spotlight:
The recent Roboflow interview with PlayVision’s Marc Zhogby focuses on the role of computer vision for basketball analytics
Voxel51’s latest video from Computer Vision meetup focuses on data foundations for VLA models
Till next time,
