Area (S) refers to the sum of pixels in the hand region
in the image:
The fact that both skin and prints found on it in the hand region
and brain are ectodermal derivatives is the basis for studying dermatoglyphic features.
Many of the techniques in hand region
segmentation worked on color space-based detection like skin-color detection, YCbCr/HSV color space filtering, and so on.
Multiclass posture detection problem is often addressed by two-stage methods [20-23], in which hand region
proposals are firstly obtained by techniques like skin, motion, or saliency detection which are robust to hand deformation and viewpoint variation, and then these regions are classified by multiple binary models or single multiclass model to achieve the final posture recognition.
Firstly, a hierarchical chamfer matching algorithm (HCMA)  is used to locate the whole hand region
in the binary image produced by combining skin color detection and background subtraction.
It can be computed as the coordinates of pixels in hand region
. The centroid can be obtained by using Equations (6) and (7).
It is interesting to note that the regions with decreased ReHo, fALLFF and ICC values were not only in the cortical map representing the face, but also in the hand region
of the cortex; regions defined by hand and mouth task-related fMRI.
According to the visual 2D gesture recognition and tracking, segmenting the hand region
through color or motion, then carries on the feature extraction processing and so on.
In these cases, one turns to classification methods, in which a previously stored set of gestures is used to determine if the captured hand region
In the hand region
, limited joint mobility (9.5%), carpal tunnel syndrome (9%), trigger finger (3.8%), and Dupuytren's contracture (1%) were found more frequent as compared to controls, while in shoulder region of diabetic subjects, adhesive capsulitis and tendonitis was found in 10.9% and 9.5% respectively as compared to 2.5% and 2% in controls.
The input depth hand image from the Kinect sensor eventually becomes a labeled hand region
through the Random Forest classifier.
In [12, 13], the authors detect the hand region
from input images and then track and analyze the moving path to recognize America sign language.