This week, I focused on designing the framework for the computer vision portion of the project. Specifically, I explored image preprocessing techniques to improve input quality for ball detection and categorization, including:
- normalization and resizing image
- converting image to the CIE LAB color space for better color representation
- noise reduction using a filter bank.
To meet computational constraints, I proposed an initial method for ball categorization by using white color detection to separate the cue and striped balls from solids, reducing the reliance on a computationally expensive neural network for this phase. I collaborated closely with Luke Han to align these methods with our embedded hardware’s computational capabilities.
Additionally, I began investigating edge and hole detection for the pool table. For edge detection, I am testing algorithms like Canny and Harris, while for hole detection, I am experimenting with thresholding to isolate shaded regions that represent pockets.
I am on schedule and expect to finalize the image preprocessing pipeline next week, including categorization and detection methods. My next steps will include testing these algorithms on sample table images to evaluate their accuracy and runtime efficiency.