## Issue Description This bug originates from the implementation of **extract_roi_from_image_for_slide()**, which was introduced in #1. While the function successfully isolates lecture slides in mixed-content frames, it does not handle cases where the slide fills the entire image (without additional elements like a professor, chat window, or overlays), leading to detection failures. Because the slide covers the entire frame, there are no distinguishable edges, causing the contour extraction to not detect the slide as a rectangle. ## Steps to Reproduce Use an image where the lecture slide completely fills the frame. Apply the extract_roi_from_image_for_slide() function. ## Exmaple: | **Step** | **Image** | **Description** | |----------|-----------|-----------------| | **Original Image (Full-Screen Slide)** | {width="1000"} | The lecture slide completely fills the frame, leaving no visible edges for detection. | | **Canny Edge Detection Failure** | {width="1000"} | Since the slide covers the entire image, the edge detection does not find clear edges around the slide. | | **Contour Extraction Issues** | {width="1000"} | The function fails to extract contours around the slide, because no sharp edges exist. | | **Failed RoI Extraction** | {width="1000"} | The expected Region of Interest (RoI) is not properly identified, therefore the biggest rectangle gets choosen as the RoI, leading to incorrect extraction. | ## Expected Behavior The algorithm should still detect the boundaries of the slide and correctly extract the Region of Interest (RoI), even if no additional elements are present in the frame. ## Actual Behavior The Canny edge detection fails to highlight meaningful edges. The contour extraction step does not return usable results. The algorithm cannot determine a valid bounding rectangle for the slide. ## Potential Solutions Use template matching to identify the slide content boundaries. Add padding to the image to ensure the slide has visible edges.