The function improfilecan either be applied on pre-selected pixels or as a command prompt function for the user in order to manually select the desired area. Apart from this, a profile of intensities can also reveal information about local intensity variations which can be used to model small details. It can be obtained using the function imhist.This information can, for example, be used for selecting an appropriate threshold value. % Image visualisation with automatically and manually selected % intensity ranges, respectively figure imshow(im, ) figure imshow(im, ) Ī histogram is a useful intensity representation as it reveals the pixels’ intensity distribution. If this vector is left empty, the minimum and maximum grey-scale values of the image are displayed as black and white pixels, respectively, with the intermediate values as various shades of grey. The function imshowcan also be accompanied by a two-element vector with which the user specifies the display range of the grey-scale image. % Export processed image as file imwrite(im_proc, 'im_processed.tif', 'tif') % Image import as variable im=imread('image.tif') % Image visualisation figure imshow(im) % Application of image processing im_proc=. The following example represents how this sequence can be achieved. Although the name of the saved image can be chosen freely, when building a large pipeline, it is suggested that the name of each resulting image be representative of the processing step in order to maintain process coherence. variable), the format and the final name have to be specified.
Saving an image can be achieved using the function imwritewhere the desired image (i.e. If, instead, imshowis used on its own, the new image replaces the last one in the last open figure window. Image visualisation is achieved using the function imshow to produce a new figure, a call to imshowhas to be preceded by a call to figure. If we consider an image file with name ‘image’ and format ‘tiff’, using the function imread, the image can be loaded as a 2D or 3D matrix, depending on the image type. Just as any other data set in MATLAB, images are represented by a variable.
MATLAB 3D MATRIX TO IMAGETYPE CODE
The functions and code provided in this chapter are adopted from the MATLAB documentation, unless otherwise stated. Each section is accompanied by MATLAB example code. The second part is dedicated to GPU acceleration of image processing techniques either by using the built-in PCT functions or through writing our own functions. These include retrieving information about digital images, image adjustment and processing as well as feature extraction and video handling.
In this chapter, the first part is dedicated to some essential tools of the IPT that can be used in image analysis and assessment as well as in extraction of useful information for further processing and assessment. However, for many image processing applications we still need to write our own code, either in MATLAB or, in the case of GPU-accelerated applications requiring specific control over GPU resources, in CUDA (Nvidia Corporation, Santa Clara, CA, USA). Moreover, the built-in Parallel Computing Toolbox ™ (PCT) has recently been expanded and now supports graphics processing unit (GPU) acceleration for some functions of the IPT. The Image Processing Toolbox ™ (IPT) includes all the necessary tools for general-purpose image processing incorporating more than 300 functions which have been optimised to offer good accuracy and high speed of processing. Since images can be represented by 2D or 3D matrices and the MATLAB processing engine relies on matrix representation of all entities, MATLAB is particularly suitable for implementation and testing of image processing workflows. More than 40 toolboxes are available in the current release (R2013b released in September 2013), which include numerous built-in functions enhanced by access to a high-level programming language.
MATLAB 3D MATRIX TO IMAGETYPE SOFTWARE
MATLAB ® (The MathWorks, Natick, MA, USA) is a software package for numerical computing that can be used in various scientific disciplines such as mathematics, physics, electronics, engineering and biology.
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