Image Processing for Pocket PC 2002

LEADTOOLS Imaging Library for Pocket PC 2002 has over 200 image processing functions. The image processing functions are separated into three categories: Transforms, Filters and Color Conversion.

To change the appearance of the image in a bitmap, you can do the following:

• Automatically straighten (deskew) a 1-bit image. See Transforms.
• Remove specks (despeckle) a 1-bit image.
• Automatically trim a bitmap to remove blank space around the edges.
• Rotate the image. (Angles can be precise to 100th of a degree.)
• Do fast rotation in 90-degree increments.
• JPEG and CMP compressed images can be rotated in 90 degree increments or flipped with no loss of quality.
• Change the orientation by flipping the image horizontally or vertically.

Adjust colors and intensities as follows:

• Change brightness using a flat scale.
• Change brightness using gamma correction.
• Change contrast.
• Stretch the range of intensities.
• Remap intensities using a lookup table.
• Create look up tables based on points on a curve or a mathematical function.
• Invert colors.
• Change hue of the entire image or just a range of colors.
• Change saturation.
• Histogram equalize and histogram contrast.
• Fill with a specified color.
• Get and put colors of individual pixels.
• Color balancing.

Apply the following kinds of conversions:

• Halftone.
• Sharpen or blur.
• Posterize, specifying the number of color planes.
• Mosaic, specifying the tile size.
• Emboss, specifying the lighting direction.
• Soften an image using an average filter.
• Reduce noise using a median or Gaussian filter.
• Add noise in any or all color planes.

Apply filters to do the following:

• Detect ranges of intensity.
• Detect edges using gradient or Laplacian edge detection.
• Detect lines using Sobel, Prewitt, shift and difference, or line segment detection.
• Apply morphological (binary) filters to erode or dilate black objects.
• Implement your own spatial filters.
• Combine images using boolean and arithmetic operators, and color masking. (This is useful for combining filtered images with originals.)
• Combine two images so that one appears to be an underlying texture of the other.
• Combine multiple images to create a new image that resembles the current image.
• Combine a list of images to find the average difference between the images.

Use low-level functions to change parts of an image as follows:

• Get and put rows of image data.
• Get and put parts of rows.
• Get and put the colors of individual pixels.

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