Computer Science - Student Works

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    496 Capstone: AR.t
    (2020) Driedger, Andre; Ansorger, Anneliese; Galay, Chance; Lafitte, Chanelle; Cobzas, Dana
    Original artwork is often very expensive; being able to see how a painting will look on a wall before you buy is advantageous. As a collaborative project between the MacEwan Computer Science and Design departments, we set out to do develop an AR app that can be used by by consumers to shop for art on the walls of their homes and offices. Existing mobile AR applications cannot identify vertical surfaces, such as walls. Our solution is to implement a target image that can be posted onto vertical surfaces to be detected by our app. We developed an OpenCV prototype to test this method of using object-detection to set a starting point for subsequent tracking. The prototype was successful in rendering 3d objects, true to scale, onto walls. Next, we developed an Android version utilizing Google's ARCore toolkit. This also delivered good results. Ultimately, we were successful in showcasing art on walls using smartphones in real-time.
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    AR.T deliver report
    (2020) Lafitte, Chanelle; Galay, Chance; Cobzas, Dana
    Original artwork is often very expensive; being able to see how a painting will look on a wall before you buy is advantageous. As a collaborative project between the MacEwan Computer Science and Design departments, we set out to do develop an AR app that can be used by by consumers to shop for art on the walls of their homes and offices. Existing mobile AR applications cannot identify vertical surfaces, such as walls. Our solution is to implement a target image that can be posted onto vertical surfaces to be detected by our app. We developed an OpenCV prototype to test this method of using object-detection to set a starting point for subsequent tracking. The prototype was successful in rendering 3d objects, true to scale, onto walls. Next, we developed an Android version utilizing Google's ARCore toolkit. This also delivered good results. Ultimately, we were successful in showcasing art on walls using smartphones in real-time.
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    Android app demo
    (2020) Driedger, Andre; Ansorger, Anneliese; Galay, Chance; Lafitte, Chanelle; Cobzas, Dana
    For the app, we developed an Android version utilizing Google's ARCore toolkit. The Design students prototyped screens for user profiles, buying art, as well as filtering and browsing functionality. This functionality has not yet been implemented, and we instead chose to focus on the AR screens. The user can browse through and preview different paintings and frames.
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    OpenCV laptop demo
    (2020) Driedger, Andre; Ansorger, Anneliese; Galay, Chance; Lafitte, Chanelle; Cobzas, Dana
    When we started the project, we had decided to make a program that would use feature matching to recognize a specific image (eg. a poster or sticker), find it’s orientation, and then display some kind of useful AR artifacts in the 3D space of our recognized image. We have implemented this in OpenCV, to show that we have an in-depth understanding of how AR works.
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    Visual assistance software for the visually impaired
    (2020) Gupta, Vasu
    The proposed project involves the creation of a visual assistance software for the visually impaired to identify people in their surroundings to avoid potential collisions. Object detection technique using TensorFlow Lite Object Detection API is performed to identify the people in the view of a visually impaired person. Vibrations of different lengths were used to notify the visually impaired about the location of the person in the view. The software is tested in various conditions to verify its working.