Masksvalues

Masksvalues is a term used in data processing and computer vision to denote the numeric values associated with masks, which are binary indicators selecting subsets of data such as pixels, voxels, or data samples. The concept combines a mask, which specifies a region of interest, with a value or set of values that quantify the importance, confidence, or weight of that region.

In practice, a masksvalues object is typically represented as an array or tensor aligned with the domain being masked. It can take the form of a per-pixel weight matrix with values in [0,1], a per-region set of weights, or a mapping from mask indices to scalar values. A mask can be binary (0 or 1), while the corresponding masksvalues provide the magnitude.

Binaries vs probabilistic: binary masks designate coverage simply; masksvalues allow soft influence. They are used in weighting feature maps, attention mechanisms, segmentation post-processing, image compositing, and privacy-preserving masking where masked areas contribute less to computations or outputs.

Generation and normalization: masksvalues may be assigned manually, computed from model predictions (as confidence scores or likelihoods), or learned during training. Values are often normalized to a fixed range to ensure stable integration with subsequent computations.

Examples include applying a spatially varying blur where masksvalues weight the blur strength, or using a predicted mask with conditional values to emphasize certain regions in a neural network's feature map.

Related concepts include masks, attention maps, and soft masks in segmentation. The term is primarily used in contexts where precise quantitative weighting of masked regions is required for downstream analysis.