Abstract: Picture filtering technology generates mathematical mathematical models to represent photographic filters for application to digitized frames of picture stock. A desired filter is determined, and reference pictures, one taken with the filter and one taken without the filter, are selected from a picture library. The selected pictures are digitized and stored. The digitized pictures respectively comprise corresponding sets of pixels or digital data units between which a quantitative differencing is performed. The quantitative difference may be based on any number of variables, such as luminance. When the difference in the luminance values of each corresponding pair of pixels is determined, a mathematical model which is representative of this difference is generated. The mathematical model is essentially a mathematical model of the desired filter, “filtering” digital data in a substantially similar manner in which photographic filters affect light.

Abstract: Polygon reshaping methods either add or subtract sides of lines of a polygon around an object of a digitized frame of picture stock. A line may be added by selecting a line of a polygon and adding a point to the line, thereby defining two lines with the point as their vertex. The point may be move along the line and away from the line to create an angle between the two lines on either side of the vertex. A line may be subtracted from a polygon either by selecting a line of the polygon and simply deleting it or by selecting a vertex of the polygon and deleting the two lines which shared the vertex. In the former line-removal case, the now-free endpoints of the lines adjacent to the removed line are moved and connected at a point defined along the removed line. In the latter vertex-removal case, a new line is drawn between the now-free endpoints of the lines which were respectively adjacent the removed lines.

Abstract: Edge detection or region definition technology in picture colorization masks objects of a frame by identifying pixels in the frame which fall within a determined range of luminance values. A seed point is selected within an object to be masked, and a test area is generated around the seed point. The test area may be any arbitrary shape or size. A range of luminance values of the pixels in the test area is generated, with the boundary values of the range representing the pixels having the highest and lowest luminance values. Pixels within the object to be masked which have luminance values falling within the range are identified and highlighted. The boundary values of the range may then be adjusted depending upon the satisfaction of the colorist. A mask is then defined to comprise the identified and highlighted pixels.