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Advanced Techniques in Background Removal: Process, Challenges, and Solutions

This document discusses the intricate process of background removal in image segmentation, emphasizing the use of binarized masks for per-pixel foreground/background classification. It highlights challenges, such as pixel degradation and issues with video file imports, while suggesting improvements for mask accuracy and edge refinement. The paper proposes next steps, including domain-specific filling algorithms, multi-frame implementations, and edge-finding techniques to enhance the background model and improve final image quality.

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Advanced Techniques in Background Removal: Process, Challenges, and Solutions

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  1. David Harwin Adviser: Petros Faloutsos Background Removal

  2. Background Removal Process

  3. Background Removal Process – An Example

  4. Side Note • In this implementation, the segmenter is set to produce binarized masks corresponding to per-pixel FG/BG segmentation. • Interestingly enough, the non-binarized grayscale difference has potential in background completion

  5. Measuring Accuracy • Raw accuracy (% correct pixels) show how well the removal was performed, but are dependent on the number of foreground pixels • Since this is essentially a classification problem, it is reasonable to define optimal behavior as minimizing both the false accept rate (FAR) and false reject rate (FRR)‏

  6. Sample results and conclusions

  7. Challenges and Ideas • A significant number of pixels either washed out or faded to black, making color differencing problematic • this suggests that black/white pixels should be treated as a special case • no success getting the framework to import video files, however • same method- frames read as still images • video formats not suitible for verification against ground truth data

  8. The next steps • current masks have rough edges and holes • filling algorithms largely domain-specific • smoothing – create averaged map at 1:2^x scale • try edge-finding algorithm and filling techniques • multiframe implementation – supplement BG model with motion likelihood updated each frame

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