[ ] Apply Kalman filtering techniques to estimate the temporal alignment between RGB and depth frames. Utilize the predicted alignment to adjust the timing of one of the streams for synchronization.
[ ] Use compression standards like JPEG or PNG for RGB images and compression algorithms like run-length encoding or delta encoding for depth data.
[ ] Utilize techniques like blob detection, edge detection, or texture analysis to extract meaningful features.
[ ] Normalize depth values to a consistent range to improve comparability between frames and scenes. Techniques such as min-max normalization or z-score normalization can be used depending on the application.
[ ] Experiment with using depth sensor data for foreground isolation within OpenCV
[ ] Apply appropriate filters to reduce noise in both RGB and depth images. Common filters include Gaussian or median filters.
[ ] Consider using adaptive filtering techniques that adjust filter parameters based on local image characteristics.
[ ] Perform depth map enhancement techniques to improve depth perception, such as edge-preserving smoothing or hole filling algorithms.
[ ] Consider techniques like bilateral filtering or guided filtering to preserve depth edges while smoothing.
[ ] Ensure proper alignment of RGB and depth images through calibration and registration processes.
[ ] Use techniques like feature-based registration or intensity-based registration to align the two modalities accurately.
Combining depth sensing with OpenCV color filter for even more accurate object detection
General Ideas: