By Khan Iftekharuddin, Abdul Awwal
Electronic imaging is vital to many industries, corresponding to distant sensing, leisure, safeguard, and biotechnology, and plenty of processing thoughts were constructed over the years. This SPIE Field Guide serves as a source for regularly occurring image-processing suggestions and instruments; with this origin, readers will higher know how to use those instruments to varied difficulties encountered within the box. subject matters comprise filtering, time-frequency-domain processing, and snapshot compression, morphology, and recovery.
Table of Contents
* Image-Processing Basics
* Spatial-Domain Filtering
* Frequency-Domain Filtering
* photograph Restoration
* Segmentation and Clustering
* picture Morphology
* Time-Frequency-Domain Processing
* snapshot Compression
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Additional resources for Field Guide to Image Processing
Thus, opening is the logical sum of all of the structuring elements that fit an image. In the following figure, the image on the right is obtained by eroding the image on the left and then dilating the image in the middle. One application of opening is the removal of pepper noise from an image. It can be argued that erosion could achieve the same thing; however, erosion alone will also shrink the image. Opening helps restore the image, to a certain extent. Closing is dilation followed by erosion.
Currently, there is no broad theory available for segmentation and clustering. Some of the most commonly used image-segmentation techniques include: • Thresholding • Region growing • Hough transform Thresholding involves setting a specific threshold and keeping or removing features above and below this threshold value T . These features include • intensity • reflectance • luminance • texture • color or • other appropriate image characteristic variables For example, a simple model of an image f ( x, y) is given as f ( x, y) = r ( x, y) i ( x, y) where r( x, y) is reflectance, and i ( x, y) is the luminance features.
The higher the count, the more edges are collinear in the image space. 4. For simultaneous detection of multiple lines, search for all local maxima of the counter array. 5. Find a peak in the counter array by thresholding. This is a “bright” point in the parameter space. 6. The intersecting points in the parameter space show up as a cluster of points due to the discrete-point nature of the lines in the image space. ) For digital implementation, all of the lines and points are quantized and displayed, as shown in the figure below.