Skin segmentation is a challenging task due to several influences such as, for example, unknown lighting conditions, skin colored background, and camera limitations. A lot of skin segmentation approaches were proposed in the past including adaptive (in the sense of updating the skin color online) and non-adaptive approaches. In this paper, we compare three different skin segmentation approaches...

We first describe how to “fuzzify” the estimated binary columns to create a [0,1]-valued column. Werefer to this [0,1] -valued column as the soft segmentation column of the noisy spectrogram column.Similarly to the collection of soft segmentation columns as the soft segmentation image, or simply asthe soft segmentation. The band-dependent posterior probability that the hard segmentation columnv...

Key words-Genetic Algorithm; Adaptive Genetic Algorithm; crossover probability; mutation probability; image segmentation

Weakly supervised point cloud segmentation, i.e. semantically segmenting a with only few labeled points in the whole 3D scene, is highly desirable due to heavy burden of collecting abundant dense annotations for model training. However, existing methods remain challenging accurately segment clouds since limited annotated data may lead insufficient guidance label propagation unlabeled data. Cons...