Exposit
Top Artificial Intelligence Company in Gdansk, Poland
62 visitsExposit About
Exposit IT solutions company delivering custom software.
$25 - $49/hr
IT solutions company providing turn-key custom development of Web and Mobile software. We combine deep tech expertise and a business-centered thinking to deliver valuable software solutions addressing to your needs.
2012
50 - 249
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Exposit Services
- Deep Learning
Exposit Portfolio
Wizart
Wizart is a neural network-based computer vision and augmented reality solution for visualizing finishing materials and creating interior designs. The product provides customers with a unique opportunity of trying out wallpapers, painting, and flooring in your own home before buying. You can instantly see the new materials in your own interior with the help of a simple photo: you just turn on a phone camera, select a suitable type and material and get a photorealistic result preserving the perspectives, scale, and shadows. Task: Development of CV Interior Assistant solving the problem of the imagination gap. Solution: Exposit took part in the development of the own product Wizart including an AI-driven iOS application and Web application that can be easily integrated with an E-Commerce website. Together we created a solution that uses a neural network to show customers new interior design ideas in just a few clicks. After receiving a photo, the application recognizes the ceiling, floor, furnishings, and decorations. Then, users have a chance to apply their favorite finishing materials options on the photo and the existing materials are replaced by a new choice. Users can try different types and colours until they satisfied with the results and finished the room repair. We also added an Augmented Reality mode designed for a more accurate overlay. At the end of the project, we developed an iOS mobile application with a working algorithm and a web application embedded in E-Commerce websites. After the project completion, Wizart became a separated company bringing competitive advantages to retailers and unleashing customers' creativity. Integration options: A plug-in for E-Commerce websites that allows working with app as a with regular online store; A workspace or self-service terminal for the offline store. Technologies: Swift, Python, Keras, Tensorflow, CoreML, Numpy, cv2, OpenCV, ARkit, Docker Learn more: https://www.exposit.com/portfolio/wizart/
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Not Disclosed
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52 weeks
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Retail
Meteor Football
Football Analytics is a computer vision system designed to improve the quality of training analytics and in-club competitions. Task: Analyze the overall feasibility of a CV system to improve the training analysis. Development of a software solution solving specific tasks of offline football schools. Solution: Our team worked on a soft-and-hardware solution that allows users to track the movement of football players and balls on the field and use the collected data to create individual training programs. During the development, we took into account cost optimization in terms of equipment, processes automation, and a large amount of video data. We reviewed the existing options for sports games analytics to create a solution that meets all the requirements: wearable tracking systems and optical tracking systems. During the research, we found that these tracking systems are often used in conjunction for greater accuracy of football analytics. Thus, we created a solution that combines the capabilities of wearable and optical systems to collect and process various types of data without using expensive equipment. This solution turned out to be a computer vision system. To test the solution, our team developed a prototype of football analytics based on CV technologies, as well as an operating algorithm: from calibrating cameras and ground marking to receiving a statistical report. The prototype can detect the player’s position on the football field during a match or training session and identify a specific player using the “reference” histograms. Histograms graphically illustrate the number of pixels at each color intensity level creating a unique graph for each player. The result of the system is a file containing data on the coordinates of the players. Technologies: OpenCV, Tensorflow, Keras, Mask R-CNN, Deep SORT, Open Pose
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Not Disclosed
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52 weeks
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