IPT - The Image Processing Toolbox for O-Matrix
The Image Processing Toolbox (IPT) provides a comprehensive set of functions
for image manipulation, analysis, digital imaging, computer vision, and digital image processing.
The IPT capabilities include image file I/O, color space transformations,
linear filtering, mathematical morphology, texture analysis, pattern recognition,
image statistics and others. The IPT contains a full reference manual with
mathematical descriptions of various algorithms and over 100 code examples of
the function usages. The use of the O-Matrix interactive
programming environment coupled with the performance of the multithreaded and
hardware optimized IPT functions enable rapid and convenient code development.
The IPT is designed to aid engineers and scientists in a wide range of areas such as
medical imaging, microscopy, industrial inspection and measurement,
surveillance and biometrics. The IPT is also a valuable tool for learning image
The key capabilities are described below:
The Image Processing Toolbox excels at the processing of large image
data sets and performance-demanding image processsing, digital imaging, computer
digital image processing applications. Solutions that can
take hours to run in Matlab, IDL, and even hand-coded implementations
can often be run in minutes with IPT. See the
IPT Benchmarks page for details.
The provided functions allow you to convert between standard color spaces
such as RGB, YUV, HSV, NTSC as well as device-independent spaces such as
CIE XYZ, CIE Lab, CIE Luv and others. Transformation can be applied
directly to 8bit or floating point data.
Spatial coordinate transformations of gray and color images can be performed.
You can use predefined transformation such as resizing, rotation, affine
and perspective transformations, or define the coordinate transformation
yourself. All operations support various interpolation methods.
Linear Filters and Image Transforms
Pre-defined filters such as Gaussian smoothing, high-pass, Sobel derivative
and many others can be applied. You can also define linear filters of your own.
FFT (part of O-Matrix), Discrete Cosine Transform,
Radon transform and reconstruction by back-projection can be used.
Morphological operations on binary and gray level images can be used.
Standard operations such as erosion, dilation, opening and closing as well as
more advanced operations such as skeleton, morphological reconstruction,
distance transform, connected components labeling and others are available.
A collection of functions allow you to: apply noise reduction filters, such as
median and adaptive (Wiener) filter; generate synthetic noise; and apply
histogram equalization. Motion blurred and out-of-focus images can be
improved using various deconvolution methods.
These tools allow you to extract information from images.
You can: compute the pixel level histogram and co-occurrence matrix;
analyze local properties and textures using non-linear filters such as
standard deviation filter and entropy filter; use Hough transform for
line detection; use normalized cross correlation and sum of square
differences for image registration.
Pricing and Ordering Information
Download Evaluation Copy