Adaptive Hand Region Detection Thresholding

The patent applies local quality by setting different detection thresholds for different spatial locations within the image. Specifically, pixels near the image boundary (where hands are more likely to extend outside) are assigned lower thresholds, while pixels in the center are assigned higher thresholds. This location-dependent thresholding strategy improves detection accuracy for hands extending outside the image without requiring complex adaptive algorithms throughout the entire image processing pipeline.

The patent implements preliminary action by pre-calculating and storing detection thresholds for all pixels before actual hand detection occurs. The thresholds are computed based on each pixel's distance from image boundaries and stored in a lookup table. During runtime, the system simply retrieves pre-computed thresholds rather than calculating them dynamically, which maintains simplicity while enabling accurate detection of hands extending outside the image frame.

The patent applies local quality by implementing spatially-varying detection thresholds that account for the probability of hand extension at different locations. Pixels near the image boundary, where hand extension is more probable, use lower thresholds to improve detection completeness. Meanwhile, pixels in the image center maintain higher thresholds to minimize false positives. This localized threshold adjustment ensures that false positive rate increases only in regions where it is statistically acceptable.

The patent changes the detection threshold parameter dynamically based on spatial location rather than using a fixed global threshold. The threshold value for each pixel is determined by its distance from image boundaries, creating a gradient of threshold values across the image. This parameter change strategy allows the system to achieve high detection completeness for hands extending outside the image while maintaining high specificity in regions where false positives would be more harmful.