Students Almaz Ermilov and Modhubroty Dey Barnile explored the capabilities of computer vision in improving baggage handling systems.
They focused on understanding how the physical characteristics of various luggage items could be translated into digital features. To detect and differentiate them, they utilized a mix of traditional techniques, such as edge detection and texture analysis, and advanced machine learning approaches like Convolutional Neural Networks. This work is a little step forward in seamlessly connecting physical objects with their digital counterparts to ensure efficient baggage handling.
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