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What is Computer Vision?
Computer vision (CV) is the subfield of artificial intelligence that deals with the automatic understanding and analysis of digital images and videos.
The tasks range from simple to highly complex analyses: classification (What is in the image?), object detection (Where exactly is the object?), segmentation (Which pixels belong to it?), OCR (What text is on it?), and video analysis (What happens over time?).
Modern CV is primarily based on deep learning: Convolutional Neural Networks (CNNs) were the standard for a long time, but today they are joined by Vision Transformers and multimodal models such as GPT-4o or Gemini, which can process images and text together.
For businesses, computer vision is one of the most practical AI building blocks—with clear business cases in production, quality assurance, logistics, and document processing.
Best Practices for Computer Vision
These six principles have proven effective in productive CV projects:
- Data quality first: 90 percent of CV success depends on clean, representative training data.
- Standardize lighting: Consistent lighting conditions are the key to stable models.
- Small iterations: Start by building a prototype with just a few examples—then improve it based on the data.
- Use pre-trained models: Transfer learning saves weeks—don’t start from scratch.
- Choose between edge and cloud deliberately: Latency, data privacy, and cost are the deciding factors—there’s no one-size-fits-all answer.
- Human-in-the-loop: Delegate uncertain cases to humans—combination outperforms pure automation.
Common Mistakes in Computer Vision
We see these pitfalls particularly often in computer vision (CV) projects:
- Insufficient data: A computer vision model typically requires hundreds to thousands of examples per class.
- Unrealistic testing conditions: A model trained in the lab fails in production.
- No data drift monitoring: New lighting, new products—and accuracy quietly declines.
- Black-box model: Without explainability, the team distrusts the model—adoption remains low.
- Overengineering: Using deep learning where classic image processing would suffice—expensive and slow.
Traditional Image Processing vs. CV with Deep Learning
Two approaches with different strengths—often combined in many projects:
- Traditional (OpenCV): Rule-based filters, edge and shape detection. Fast, deterministic, good for simple tasks.
- Deep Learning: CNNs and Transformers. Ideal for complex, unstructured images and variable conditions.
- Hybrid: Traditional methods for preprocessing, deep learning for the heavy lifting of classification—the best of both worlds.
Computer Vision for Your Processes
In a free initial consultation, we’ll review your visual inspection and image analysis tasks and identify the computer vision use cases with the greatest business impact—including a concrete implementation proposal.
As an AI partner for small and medium-sized businesses, we take computer vision from the prototype stage to full-scale production—with clear metrics and consistent, high-quality results.
What We Offer
- AI Consulting — Use-case selection and computer vision strategy.
- Software & Models — Implementation of the CV pipeline and integration.
- AI Monitoring — Drift Detection and Operational Quality.
- Anomaly Detection — Computer vision is one of the most important building blocks for detecting visual anomalies.
