Shadow detection based on HSV color space characteristic and Gabor screener

The statistical features of color histogram of three independence variables in HSV color space,and independent characteristics of hue,saturation,and brightness are adopted to express the information of color of wood surface in this paper.

Then get the coarse shadow area where the shadow detection based on the HSV color space is only applied in.

An object extraction method based on the HSV color model was proposed,which could be used for testing the blended ratio of cashmere/wool yarns.

By this concept and together with HSV color model, we can successfully segment the face image.

A support vector machine(SVM) object extraction method was proposed based on the HSV color model,which could be used for testing the blend ratio of cashmere/wool yarns.

The experiment indicates YUV color space model can overcome the adverse effects of illumination changing for accomplishing pixel segmentation correctly.

A novel thresholding approach is proposed based on color space model and is embedded into background subtraction.

Then,based on the features of H and S sub-channels of HSV color space,this paper uses K-means algorithm to classify those areas into nine parts.

The algorithm employs information of the color image in HSV color space to construct a five_scale gray image, pre_processes the image with morphological method, then locates and segments the vehicle license plate by analyzing and discriminating the statistical_letter_frequency property of every possible regions.