Our AI solutions process images and video, recognize objects and scenes, and generate descriptions and labels for visual content. In other words, thanks to computer vision, you never lose sight of your business value.
We develop custom, high-performance machine learning models for object detection, even in challenging real-world circumstances. We tailor our approach - data pre-processing, modeling, tuning and setup - to fit each unique use case for optimal results.
Unlock the full potential of your video data with our expert object tracking techniques. We help you detect and segment objects, identify activities, and overcome the unique challenges of video analysis, like resource management and model architecture.
Edge Processing of video can reduce network traffic and increase data security. With our Edge Video Analysis (EVA) project, subsidized by Vlaio, we aim to develop a high-performing solution for anonymizing and re-identifying video on the edge.
Monitor, steer and improve your quality control with advanced machine vision technology. Our algorithms detect various defects, optimizing production processes.
December 30, 2021
September 8, 2021
Building a computer vision solution can be complex process. Let's take a look at some common challenges.
Computer vision algorithms need labeled data to learn, but getting this data can be difficult and expensive. That’s why it’s important to use the data effectively, which means getting good quality data and making sure it covers a wide range of situations. This leads to more accurate and robust computer vision models.
Finding and separating objects in an image is important for many computer vision tasks. It can be challenging to track objects over time, especially if they're hidden or have complicated shapes, but there are ways to overcome these challenges and improve the accuracy of computer vision models.
Computer vision aims to accurately and efficiently identify objects in images, even if some objects are hard to tell apart. With proper training on large datasets, computer vision models can achieve high accuracy and improve our ability to analyze visual information.
Choosing the right model is crucial for successful computer vision solutions. It requires a deep understanding of algorithms and fine-tuning for specific use cases. While transfer learning can be useful, adapting pre-trained models to new domains is important for optimal results.
Computer vision applications often need to process data quickly, but the algorithms can be demanding. This means that it's important to find ways to make the algorithms faster without losing accuracy. Finding the right balance between speed and accuracy is key.
Explaining how computer vision models work can be tough because they often use complex neural networks. But there are ways to make these networks more interpretable, so stakeholders can understand and use the models more effectively.