Embedded Image Processing on the TMSCTM DSP: Examples in Code Composer StudioTM and MATLAB [Shehrzad Qureshi] on The TMSC Line of DSPs (Image Processing). Even though this topic has a narrow focus, it calls for a wide array of tools, some hardware (DSP. Written for practicing signal and image processing engineers, this book discusses the implementation of advanced image processing algorithms on.

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Another motivation for my writing this book, aside from the fact that there is not currently a book on the market covering embedded image processing, is to shed some light on the “black magic” that seems to accompany embedded DSP development.

The downstream analysis may come in the form of image segmentation or pattern recognition algorithms, which could potentially be used here to discern the locations of imaged tanks or other battlefield components. GPPs are designed to perform a diverse range of computing tasks many of them not numerically oriented and typically run heavy-weight operating systems definitely not suited for embedded and especially real-time systems.

Spatial filtering is best used when dealing with unstructured or stochastic noise, where the location and magnitude of the noise is not known in advance, but development of a general model of the degrading process is feasible. An optical filter placed on a camera’s lens is used to accentuate or attenuate certain global characteristics of the image seen on film.

Embedded Image Processing on the TMSC DSP, w. CD-ROM Buch

In this case, the gray- level transform function is erroneously constructed because the scale of the two discrete PDFs are no longer the same. If QMULT is to be used as part of another computation further down the road, we will need to realign this number by bit shifting to isolate the fractional bits we wish to retain, taking extreme care to guard against overflow. An 8-bit image […]. NET- compliant language fully aware of the.


There is no better way to learn than by example, and the book offers plenty of them. Thanks for telling us about the problem.

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Embedded Image Processing on the TMS320C6000 DSP, w. CD-ROM

Non-linear filters are precluded from taking advantage of this embeedded, as the convolution theorem does not apply to them.

We conclude the section by introducing the two TI development environments used in this book. This type of processing is shown diagrammatically in Figurewhich is a high-level illustration of a 3×3 mask being applied to an image. To experiment with different filters, all that is required is to generate the coefficients via f s p e c i a l and copy the coefficients into the source code.

The situation is exacerbated when the system gain of the acquisition device is increased, as any imae present with the system is amplified alongside the actual signal. When embdeded 2D convolution in the frequency domain, edge artifacts will be present as the FFT assumes a periodic signal and so in essence, the image is extended such that it wraps around at all four of its edges i. Floating-point numbers basically represent this set in scientific notation, and they are capable of representing a large range of values.

This has two important ramifications for embedded DSPs: The CDF is closely related to the probability density function; simply put, the CDF of a random variable in this case the set of pixel values in the input image is a nondecreasing function yielding the probability that a random variable pixel intensity takes on a value less than or equal to some number.

Histogram equalizing an RGB image. The Author and Springer shall not be liable for damage in connection with, or arising out of, the furnishing, performance or use of this CD-ROM.

Embedded Image Processing on the Tms320c6000 DSP: Examples in Code Composer Studio and MATLAB

There were so many issues that to be truthfully honest I have lost track of many of them. For example, the following MATLAB command initializes a cell array consisting of a few numbers, a string, and a matrix: Both of these pr a b Figure When interpreting a Q0.


One means of performing high-pass filtering is to first repeatedly low-pass filter an image, and then subtract the smoothed image from the original, thereby leaving only the high spatial frequency components.

Use or reproduction of the information provided on this CD-ROM for commercial gain is strictly prohibited. Remember, a floating-point processor is a superset of a fixed-point processor, and there is not much required to get a working fixed-point algorithm running efficiently on a floating-point architecture.

In addition, since most noise contributes to the high frequency content of an image, high-pass filters have the unfortunate side effect of also accentuating noise along with the fine details of an image. A complete listing of the source code is not shown here, but the following explanation pertains to the code in WindowLevelDlg. The operating system is configured by a GUI that allows one to tailor the operating system to the application at hand, and it contains various “modules” basically C APIs that mirror the functionalities in the CSL.

Rather, what you find is that DSP applications are dominated by tightly coded critical loops.

Another major component to the embedded is the I m a g e 8 b p p class, whose interface is given in Listing Provide numerous Visual Studio. This requirement leads to a simplification and optimization of the algorithm, and is useful to consider for that reason. The former can be set from the project settings within CCStudio, and the latter is achieved by either explicitly adding the static library to the project, or adding it lmage the linker configuration in the project settings.

The minimum pixel value in Figure a is 81, and the maximum pixel value is