EasyImage - Pre-Processing Images

EasyImage ^{.NET Python} operations prepare images so that further processing gets better results by:

• Isolating defects using thresholding or intensity transformations

• Compensating perspective effects (for non-flat surfaces such as a bottle label)

• Processing complex or disconnected shapes using flexible masks

The main functions are:

Intensity transformations	Change the gray-level of each pixel to clarify objects (histogram stretching).
Thresholding	Transforms a binary image into a bi- or tri-level grayscale image by classifying the pixel values.
Path profiling	Samples a series of pixel values sampled along a line, a path or a contour in an image.
Arithmetic and logic	Functions manipulating pixels in two images, or one image and a constant.
Linear filtering	Functions using linear combinations of neighboring pixels (convolution) to highlight features in an image.
Non-linear filtering	Functions using non-linear combinations of neighboring pixels (using a kernel) to highlight a shape, or to remove noise.
Geometric transforms	Moves selected pixels to realign, resize, rotate and warp.
Noise reduction and estimation	Functions ensuring that noise does not unacceptably influence other operations (thresholding, high-pass filtering).
Scalar gradient	Generates a gradient direction or gradient magnitude map from a gray-level image.
Vector operations	Extracts 1-dimensional data from an image into a vector, for example to detect scratches or outlines, or to clarify images.
Canny edge detector	Returns a BW8 image of the edges found in a BW8 image.
Harris corner detector	Returns a vector of points of interest in a BW8 image.
Overlay	Overlays an image on top of a color image.
Operations on interlaced video frames	Eliminates interlaced image artifacts by rebuilding or re-aligning fields.
Computing image statistics	Displays different statistics about an object or an image.
Fourier transform	Represents an image by its frequential components to process the image and perform specific filtering.
Gabor filter	Analyzes textures or creates features for a classifier by convolving the image with a wavelet to detect specific frequency content.
HDR fusion	Fuses different images into a single one that contains all the details exacerbated at each exposure level.
Image stitching	Combines partial images to build a final image that appears to have been captured in a single shot.