1. Field
The present disclosure is generally related to detecting thin lines in image data.
2. Description of Related Art
Image data comprises a number of pixels, each pixel corresponding to a defined location in the image. The pixels may have a number of components that contribute to defining the image, such as color and intensity. Binary image data has two possible values for each pixel, black (represented by the number “1”) or white (represented by the number “0”). Binary images may also be represented as pixels that are “on” or “off” in color images where pixels are represented by color planes. Images that have a large range of shades are referred to as grayscale image. For example, grayscale images have an 8-bit value per pixel comprising 256 tones or shades of gray for each pixel in the image (gray level of 0 to 255). Grayscale image data may also be referred to as continuous tone or contone image data.
A technique for manipulating image data for output includes segmentation (or auto-segmentation). Generally, auto-segmentation techniques are known, and are used to select the most appropriate method to render the various object types (e.g., black and white or color images, text, photos, etc.) present in an image. In some segmentation techniques, separation modules are used to detect and separate text from objects of image data (e.g., photographs or other pictorial parts) so that text image data may be compressed and/or processed differently as compared to the object image data. Image segmentation plays an important role in delivering image quality for copy and scan services. While the technology for image segmentation has been improving over the years, some current methods in use still have limitations in a number of areas—the robustness in thin line detection being one of them. Sometimes, when robust separation of image and text/line art is needed, some kind of two-pass operation, such as auto-windowing, is used. These solutions, however, may not be suitable for all situations due to the cost and complexity associated with them. Also, such methods may not necessarily produce high quality images, as the detection of thin lines may be scarce or eliminated, thus reducing the output quality of the image data. Furthermore, known segmentation methods are sometimes incapable of accurately distinguishing edges from halftone dots or lines.
Therefore, a simpler, yet effective method for manipulating image data for output and for determining thin lines in input image data is desirable.