Computer vision
- Semantic and instance segmentation
- Object detection and recognition
- Multiple object tracking
- Stereo vision
- 3D reconstruction and SLAM
- Image retrieval
- Pattern recognition
Machine learning
- Dimensionality reduction
- Fitting techniques
- Clustering methods
- Supervised ML
- Neural networks
- Feature crafting for CV problems
Deep learning
- Face detection and recognition
- Semantic segmentation
- Monocular depth estimation
- Human pose estimation
- Multiple object tracking
- Image recognition
- Object detection and recognition
Image processing
- Image enhancement
- Image restoration
- Image inpainting
- Image segmentation and classification
- Image-based steganography
- Image compression
- Image decomposition
- Image filtering
- Image editing
Signal processing
- Biomedical signal analysis
- Time-series analysis and forecasting
- Sensor fusion (IMU, BLE, GPS, images, etc.)
- Signal processing for AR/MR
- Signal understanding, interpretation and filtering
- Optimization of signal processing algorithms
Augmented & mixed reality
- AR mobile SDKs
- Optimized deep learning models for mobile platforms
- Mobile image retrieval and visual search
- Integration of custom C++ code on Android/iOS
- TF Lite, Core ML, ML Kit for AI on mobile
- Web AR applications
01
Integration of high-quality solutions into various platforms and hardware
02
Extraction of information from visual data of any nature, sensors and conditions
03
Fusion of traditional approaches with different types of machine learning algorithms and deep learning architectures
04
Tailoring to the needs of businesses from different industries
Data
Optical imager
Infrared camera
X-ray images
Radar imagery
Platforms
Mobile
Web
Cloud
