Abstract: Disclosed is directed to a speed-level calculator and calculating method for dynamic voltage scaling. The speed-level calculator comprises a deadline counter, a shifter, and a fixed-point multiplier. The deadline counter calculates the residual time D from current time to each task deadline for completing an episode. The shifter generates a D? value by shifting the D value to the right for e-m bits, and takes the decimal fraction part of the D? value for m bits. The speed-level calculator further comprises a saturation control circuit to detect if an overflow occurs on the D? value. According to a pre-calculated parameter ?i corresponding to each task Ti, the fixed-point multiplier performs the multiplication of D? and ?i. After completing saturation and rounding on the multiplication result, a corresponding clock period is generated by taking the integer part. This clock period is used as speed-level to switch the processor voltage and frequency.

Abstract: Disclosed is directed to a speed-level calculator and calculating method for dynamic voltage scaling. The speed-level calculator comprises a deadline counter, a shifter, and a fixed-point multiplier. The deadline counter calculates the residual time D from current time through to each task deadline for completing an episode. The shifter generates a D? value by shifting the D value to the right for e?m bits, and takes the decimal fraction part of the D? value for m bits. The speed-level calculator further comprises a saturation control circuit to detect if an overflow occurs on the D? value. According to a pre-calculated parameter ai corresponding to each task Ti, the fixed-point multiplier performs the multiplication of D? and ?i. After completing saturation and rounding on the multiplication result, a corresponding clock period is generated by taking the integer part. This clock period is used as speed-level to switch the processor voltage and frequency.

Abstract: A method for aligning gesture features of image is disclosed. An input gesture image is captured, and then a closed curve formed by a binary contour image of the gesture image is determined by processing the gesture image. A curvature scale space (CSS) image of the gesture image is drawn based on the closed curve. A convolution operation is performed with respect to the sequence of a coordinate-peak set formed by the CSS image and a predefined function to designate the coordinate with maximal value of integration as a basis point for obtaining a feature parameter of the gesture image. Finally, comparing the feature parameter of the gesture image with each feature parameter of a plurality of reference gesture shapes, thereby determining a gesture shape corresponding to the gesture image.

Abstract: A background removal apparatus comprises a color normalization module that normalizes an original image, and an image segmentation module that segments the normalized image into several segmented regions. A background mesh generation module simulates the variation of pixel colors in background and generates an interpolated background mesh. A comparison module compares the normalized image and the interpolated background mesh to form a background mask by extracting the coherent regions between them. A refinement module uses a refined rule to determine a final background mask, and a background removal module through which a pure foreground image is obtained. The apparatus combines the efficiency of both color and spatial clustering, and improves the capabilities of current image segmentation method to perform background removal.

Abstract: A method for aligning gesture features of image is disclosed. An input gesture image is captured, and then a closed curve formed by a binary contour image of the gesture image is determined by processing the gesture image. A curvature scale space (CSS) image of the gesture image is drawn based on the closed curve. A convolution operation is performed with respect to the sequence of a coordinate-peak set formed by the CSS image and a predefined function to designate the coordinate with maximal value of integration as a basis point for obtaining a feature parameter of the gesture image. Finally, comparing the feature parameter of the gesture image with each feature parameter of a plurality of reference gesture shapes, thereby determining a gesture shape corresponding to the gesture image.

Abstract: A background removal apparatus comprises a color normalization module that normalizes an original image, and an image segmentation module that segments the normalized image into several segmented regions. A background mesh generation module simulates the variation of pixel colors in background and generates an interpolated background mesh. A comparison module compares the normalized image and the interpolated background mesh to form a background mask by extracting the coherent regions between them. A refinement module uses a refined rule to determine a final background mask, and a background removal module through which a pure foreground image is obtained. The apparatus combines the efficiency of both color and spatial clustering, and improves the capabilities of current image segmentation method to perform background removal.