Machine Vision Lab
Our team of PhDs and engineers help medical device and automation teams turn vision and imaging ideas into production-trustworthy algorithms. We don’t build cells or sell cameras — we plug in as the algorithm team alongside your existing engineering and integration partners.
Recent work includes image registration from in-procedure video in surgery, and creating high-fidelity virtual worlds for training physical AI systems (synthetic environments and simulation pipelines that let teams validate perception + inspection logic before full-scale deployment).
- Vision & Imaging Algorithm Development
- Design and implement robust vision/imaging algorithms (classical + ML) for inspection, defect detection, measurement, tracking, and image registration — built to survive real-world variability.
- Feasibility & Risk Evaluation
- Quickly assess whether a vision/imaging use case is viable with your real samples and constraints. Clear answers on expected performance, key risks, and what it would take to get to production reliability.
- Reliability & Performance Improvement
- When a system “works in the demo” but not in production: diagnose failure modes, reduce false rejects/misses, and tighten evaluation so improvements are measurable and repeatable.
- Edge / On-Device Optimization
- Optimize algorithms for real-time, embedded, or edge constraints (latency, throughput, memory). Make models and pipelines run where they need to run — reliably (Jetson-class or x86).
- Synthetic Environments & Simulation
- Build high-fidelity virtual worlds and simulation pipelines to validate perception + inspection logic before full-scale deployment. Useful for autonomy, robotics, and complex environments where data collection is slow or expensive.
- Industrial Inspection Systems (On-Prem)
- Defect detection and measurement workflows for manufacturing — designed to hold up under production variance. We can work within strict data/security constraints and integrate outputs into your PLC/robot/HMI stack.
Let's work together
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