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- Vision AI weekly: Issue 07
Vision AI weekly: Issue 07
Another exciting week in the Vision AI ecosystem!
🌟 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.
🗓️ Tool Spotlight
Oxipital AI unveiled V-CORTX, its next-generation AI vision platform, at PACK EXPO International 2025. Designed for manufacturers and machine builders, the platform eliminates traditional barriers in computer vision with a no-code, no-annotation, no-images workflow.
It integrates a vision-model manager, drag-and-drop recipe builder, and real-time analytics dashboard, enabling rapid deployment of vision inspection and robotics solutions.
V-CORTX empowers users without deep AI expertise to build, test, and scale intelligent automation systems efficiently, marking a major step toward democratizing industrial vision AI. [link]
🚀 Project Spotlight
Sergems18’s Medium article explains how to build an object detection model using Azure AI Custom Vision. It walks through creating Custom Vision resources in Azure, starting a new project, uploading and tagging images, training the model, and testing its predictions.
The workflow involves labeling objects, training a deep-learning model, and exporting or deploying it through Azure’s API.
This no-code tool enables quick setup and deployment of models for tasks like product recognition, quality control, or surveillance, making it ideal for developers seeking a low-code approach to custom computer vision systems.[link]
🦄 Startup Spotlight
vfrog.ai : Accelerating Computer Vision Development
vfrog.ai is a startup offering an end-to-end computer-vision platform designed to dramatically reduce manual annotation effort.
Users upload images, specify what objects to detect (e.g., shelf items, PPE compliance, defects), and the system auto-annotates around 80% of labels, allowing quick fine-tuning. It then supports one-click model training and API deployment.
Pricing starts at $49/month, targeting teams moving models from idea to production in days. (vfrog.ai).
🔥 Paper to Factory
The authors evaluate segmentation bias across demographic subgroups (Black male, Black female, White male, White female) when segmenting the nucleus accumbens in brain MRI scans.
They compare three deep-learning models (UNesT, nnU-Net, CoTr) and a classical atlas-based method (ANTs). Results show that some models (ANTs, UNesT) perform significantly better when training and testing share race‐matched data, whereas nnU-Net appears robust to demographic mismatch.
They further analyse how sex and race impact derived volumes and segmentation accuracy using linear mixed models, finding persistent sex effects but mostly absent race effects for most models. (link)
🏆 Community Spotlight:
In their recent blog, Chooch AI discusses the various strategies to prevent stockouts in hospitals
A recent Visionfacts article highlights the importance of Computer Vision in Zoo and animal welfare.
A recent Linkedin post highlights how computer vision is transforming manufacturing quality control
Reddit / X corner:
Till next time,