From developing an end-to-end solution to taking up an existing project, we deliver a whole range of expert computer vision development services. Our team of professional programmers, experienced data engineers, and Computer Science Ph.D. holders will help you draw useful insights out of images and videos to unlock the data potential hidden in them.
Our qualification is backed up with:
Today, computer vision (CV) is becoming one of the most popular subdivisions of machine learning applied across industries. The purpose of this technology is to make machines understand the contents of digital images and videos, allowing to filter this content, recognize and track objects, detect patterns, perform image classification, detect people in images and distinguish their emotions.
Here at Mediterra, we possess solid competence in building visual recognition models for custom computer vision applications by harnessing the power of up-to-date machine learning and deep learning techniques. Recognizing the unique needs of your business, we will help you outline the computer vision project and deliver the custom task-oriented algorithms able to extract the required valuable data from images.
We are passionate about implementing computer vision capabilities into real-world commercial tasks
Integrating optical character recognition into custom systems enabling to process typed or handwritten texts. Our team will assist in choosing the optimal machine learning approach to reach your OCR goals.
Building state-of-the-art models able to detect, track, and count different objects, including people or animals. Precise multi-object tracking and motion detection adapted to real-world limitations such as lack of light or poor resolutions.
Analyzing and interpreting visual video contents to recognize human actions, track multiple objects in movements, as well as identify abnormal events in image sequences for security alerts.
Training machine learning models to automate image analysis, including localization, image categorization, and pixel-wise segmentation.
Automatic face analysis involving person identification with face features, facial expressions, emotion recognition, real-time gesture, and movement recognition powered by deep learning models.
Computer Vision is one of our primary fields of expertise. We have a long record of delivering successful projects in Finances, Sports, Telecommunications, Agriculture, Engineering, Retail, and other industries.View All
The goal of the project was to analyze videos of athletes using multiple cameras and detect and track athletes appearing on video in real-time. To deliver the project we built the deep learning model based on TensorFlow Framework; as a tracking system, we used custom algorithms based on Kalman filters. We also created a communication layer between the system and video server using a high-performance custom protocol based on TCP-sockets usage.Learn more
The goal of the project was to define characteristics of the general traffic of store visitors, including traffic volume, time of entrance and exit, how long visitors stay on a trading floor, as well as personal visitor characteristics to identify regular customers and clients’ traffic maps. From the technical viewpoint, we used data from 2 video cameras, Ubuntu as the main OS and Python with such frameworks as Caffe, Pytorch, Numpy, Sklearn, Scipy, and OpenCV. As a result of the project, the client managed to boost store revenues by 16%.Learn more
The goal of the project was to analyze images of grape fields from drones in order to find and detect grape rows, as well as estimate start and endpoints, and the length and width of each row. Additionally, the application supported image processing functionality such as colors, brightness, and contrast manipulation, drawing primitives, zoom, saving results as shapefiles. The application included a cross- platform design written in C++/Qt for Mac/Win/Linux platforms.Learn more
Our client breeds elite racing horses with excellent physical characteristics. The project objective was to build an application that processes videos of a specific horse and calculates the probability of this horse being elite. We created machine learning algorithms and models based on provided data such as horse heart rate, weight, body size, cardiac index, ultrasound/echo, and DNA. At the next step, we built a database and web application that manages current and new data about horses and predicts eliteness. Then we created Web Jobs for automatic retraining of the machine learning models. As a result, we delivered a web app that proves a correlation with more than 80% accuracy for elite thoroughbred horses. To implement the project, we utilized Python and Django for the web app and OpenCV with Neural networks for the Machine Learning module.Learn more
We offer our clients flexible cooperation options based on their preferences and project specifics that allow them to save costs.
We respect our clients’ confidentiality and are ready to work under NDA.
A budget-friendly and low-risk model used when the scope and requirements of the project are well defined and documented.
Better suited for projects with less clear specifications and timelines, and provides the possibility to balance team size, as well as project workloads.
This model is used for long-term contracts, where a dedicated team of specialists is allocated for the project with an agreed fixed monthly payment for the engaged human resources.