Direction analysis algorithm using statistical approaches

Abstract

Statistical approaches become very important tools that interfere and overlap in our daily life and become inevitable event that help us in every tiny details of our life, in this paper; we are going to present a new technique for analyzing the two principal component of any given object by calculating the direction over the occupied coordinates using mean, variance, and covariance statistical functions, and by finding some relationship between those statistical functions; we have extracted the angle degree of the processed object, for pattern recognition applications; this object can be adjusted accordingly to overcome the rotation perturbation shortcoming that hinders the extraction of a unified features especially for object recognition purposes in which we have to present many samples per single pose which makes the processing of this increasing size of the database is a noticeable burden, we have achieved a dramatically results with almost zero time of calculation since the statistical functions applied need little processing time to finish.

Experiencing Various Color Models on Colored Images

“…The selection of proper color model for a specific application depends on the properties of the model and the application natures. These color modes are classified mainly into two kinds: device independent and device dependent, further classification is analog use or digital use as well, the application of the color model likewise useful can be found in gesture recognition [12][13][14][15][16][17][18][19][20][21] which adopted mainly to extract the background especially cluttered and non-uniform background in which the human face or hand can be easily spotted despite the changing in color pigment of human skin which can be managed by eliminating the chrominance of the modeled color and the luminance will be the judge. …”

Gaussian Fitting Model for Non-Geometric Features in Gesture Recognition System: Analysis Study

“…Hand center is calculated , direction is calculated as well using the algorithm in [3], borders of the hand is located as seen figure 1.…”

“…However, rotation perturbation is considered as the most sophisticated problem and usually ignored by many researchers or providing many patterns for each single rotated gesture which increases the dataset volume used for training purposes [3], for example, 2280 different patterns to detect the rising finger in [4], furthermore, 6, 40, 124, and 20 patterns for each single posture in [5], [6], [7], and [8] respectively; but, however, some researchers kept this number low and of course, their method will suffer from rotation variance, this can be found in [9][10] [11] and [12].…”

“…The solution is to unify the direction of all presented objects [3] and in this case all gestures are having the same direction which reduces the need for the multiple-gesture for each gesture rotation.…”