Extract meaningful information from images and videos with our tailored made models.
Trusted by governments, leading telecoms and banks
Here at Mindtitan, we develop AI solutions focused on deriving valuable information from visual inputs like images and videos at a scale and speed impossible for humans.
Our experience in this domain allows us to provide our partners with solutions tailored to their specific datasets, scale and performance needs.
AI can perform human tasks with an incredible speed reducing business costs
Detect production issues or defects in manufacturing
Assess what activities a person is carrying out
Image recognition is tasked with detecting and identifying people, items, places, writing or otherwise specific features on an image. Vision data like pictures from different manufacturing steps in a production line, for instance, could be used to make real-time decisions based on real-time detections of any quality or process deviation in real-time.
Historical data could also be batch processed to gain insight on the current production procedure to seek continuous improvement.
We can provide vision systems that can be leveraged across multiple fields by leveraging different tools: in addition to the classification models described above, we can use Image Segmentation methods that are used to divide (segment) each image in multiple areas so that each pixel belongs to a known label.
Object detection adds localization to the former family of models by providing exact information regarding the position of multiple objects within the images. The application field for these models is extremely varied and includes, for example:
Optical character recognition models convert images of typed, handwritten or printed text into machine-encoded text and are used for translation services, licence plate recognition and road-side signage information extraction.
Our services also include ad-hoc AI systems like Face Recognition models that match known pictures of individuals to new images even when partial occlusion or angle variations are present.
Using distinctive facial features like the distance between the eyes or the shape of the cheekbones, the algorithms produce a condensed representation of the detected face for easy comparison against a database.
Such capability can be used for:
Moving from image to Video Analytics, we can leverage additional information to a series of images (frames in a video) by taking into account the features that are relevant across multiple frames.
Video inputs can provide supplementary information in subsequent images such as a new angle/view of an object or the evolution of a person’s motion. We can translate this extra data into a deeper knowledge and insight through video processing algorithms that are at the core of functionalities like:
Going further from video classification, Object tracking models leverage the change in object localization of the detected labels over different video frames enabling technologies like
The AI implementation speeded up the procedure of object detection by 15%. It highlights insulators of the image made by drones and classifies whether it needs maintenance.
The detection became more accurate as the computer vision system allows finding on average 11 more defects per line kilometer.
Such capability can be used for:
– access control to restricted areas
– assist law enforcement in identifying criminals and missing persons, even among crowds
– identity validation in a purchase process
As an agency of the Estonian government, the Consumer Protection and Technical Regulatory Authority employs our models to detect unlawful advertising in regulated categories like alcohol and tobacco.
Automatic fault detection in powerline insulators via processing of aerial inspection images was developed for Hepta Airborne. The machine learning service was implemented across all stages of the functionality development process, from data augmentation and creation to fault classification.
Co-founder, CEO
Kristjan has been studying and working on machine learning projects for more than 5 years.
After acquiring a Master’s Degree in Computer Science and Machine Learning, he started working at Milrem Robotics as the Team Lead for Autonomous Vehicles, helping to build self-driving vehicles. Kristjan also has experience in building intelligent systems for data centers, robots and electric formulas; also with computer vision and image recognition. He is especially fascinated by how people from different industries combine their knowledge with data science, arrive at new insights and help to accelerate innovation.
Co-founder, Data science lead
Markus’s career started as a bioinformatician and he was soon drawn towards data science and developing machine learning projects. While acquiring his Master’s degree in Computer Science, he spent two years at STACC (Software Technology and Applications Competence Center) where he developed data analytics driven tools for personalized medicine applications. Markus also has experience with deep learning algorithms, natural language processing, and image recognition.
Co-founder, Data science lead
Robert’s background in physics and software engineering, coupled with his love for analytical thinking led him to become a Data Scientist in the research lab for STACC (Software Technology and Applications Competence Center). There, his work was focused on implementing machine learning methods to mine patterns from national medical records. Robert also holds a Master’s degree in Computer Science & Machine Learning and he’s exceptionally well-versed in classical machine learning methods. He enjoys working in the field of AI due to the incredible pace at which the impossible becomes possible.
We take great pride in our team of more than 20 data scientists, software engineers, and machine learning engineers who come from nine different countries.
Working closely with your company, we will identify the key areas where AI can bring the most value. This involves meetings with all stakeholders and developing a roadmap for action together.
Based on our meetings and data analysis, we’ll share with you the possible AI use cases for your company. We will work hand-in-hand to agree on the desired outcome.
We will build and apply various machine learning models to your business data, to find the best solution. As a result, we will develop algorithms that accomplish the desired goal
We integrate the machine learning model with an API or front-end product, making it user-friendly and accessible to the end-user.
Any system might require time to time maintenance, and we are happy to support our customers with that.
Computer vision is a field of technology that focuses on teaching computers how to understand and interpret visual information, such as images and videos. Thanks to machine learning techniques, computers are now able to perform a wide range of data processing tasks with very little input or intervention from humans.
In general, if a decision or choice needs to be made based on visual medium features, if a human can do it, then a machine can do it. It just becomes a question of how much data is available for training and how much effort is required to build the models necessary to achieve the business goals.
Data collection is most often on the client side as it is connected to the peculiar business problem to be solved. We can help you with that, especially if it is a data source we have worked with before.
Labeling can be on the client side if very specific knowledge is required for labeling or it can be outsourced to a labeling company or us. Hybrid solutions are also available, and in all cases, we provide proper labeling training and guidelines tailored to the computer vision task.
If you imagine the process made up of iterations of the cycle: data collection, labeling, model development + training, testing & evaluation and deployment into the customer solution, usually the first 2 steps take 20-50% of the time (the smaller the project / standard problem, the higher the percentage) while the split between the latter 3 is really dependent of the novelty of the problem being solved, the required performance level and the complexity of deployment. The computer vision project range is from 3 months to multi-year collaborations.
During the project evaluation, we provide a feasibility assessment and, for problems in which we have experience, we can provide a more precise prediction at the level of the computer vision metrics and discuss the forecasted minimum level of performance.
