**2.3.1 The issues that must be considered in real-time imaging system**

### **2.3.1.1 Hardware and display issues**

134 Advanced Topics in Measurements

three primary (RGB) colours at the same time. Each sensor cell has its own spectrally selective filter and thus it stores only a single measurement. Therefore, the CFA image constitutes a mosaic-like grayscale image with only one colour element available in each pixel location. The two missing colours must be determined from the adjacent pixels using a digital processing solution called demosaicking. Such an architecture represents the most cost-effective method currently in use for colour imaging, and for this reason, it is almost

This architecture acquires colour information using a beam splitter to separate incoming light into three optical paths. Each path has its own red, green or blue colour filter having different spectral transmittances and sensors for sampling the filtered light. Because the camera colour image is obtained by registering the signals from three sensors. The mechanical and optical alignment is necessary to maintain correspondence among the images obtained from the different channels. Besides the difficulties of maintaining image registration, the high cost of the sensor and the use of a beam splitter make the threesensor architecture available only for some professional digital cameras (Lukac &

Real-time systems are those systems in which there is urgency to the processing involved. This urgency is formally represented by a deadline. Because this definition is very broad, the case can be made that every system is real-time. Therefore, the definition is usually specialized. A "firm" real-time system might involve a video display system, for example, one that superimposes commercial logos. Here, a few missed deadlines might result in some tolerable flickering, but too many missed deadlines would produce an unacceptable broadcast quality. Finally, a "soft" real-time system might involve the digital processing of photographic images. Here, only quality of performance is at issue. One of the most common misunderstandings of real-time systems is that their design simply involves improving the performance of the underlying computer hardware or image processing algorithm. While this is probably the case for the mentioned display orphotographic

Here, guaranteeing that image processing deadlines are never missed is more important than the average time to process and render one frame. The reason that one cannot make performance guarantees or even reliably measure performance in most real-time systems is that the accompanying scheduling analysis problems are almost always computationally complex. Therefore, in order to make performance guarantees, it is imperative that the bounded processing times be known for all functionality. This procedure involves the guarantee of deadline satisfaction through the analysis of various aspects of code execution and operating systems interaction at the time the system is designed, not after the fact when trial-and-error is the only technique available. This process is called a schedulability analysis. The first step in performing any kind of schedulability analysis is to determine, measure or otherwise estimate the execution of specific code units using logic analyzers, the systemclock, instruction counting,

processing systems, this is not necessarily true for the target tracking system.

universally utilized in consumer-grade digital cameras (Lukac & Plataniotis, 2007).

**2.2.3 Three-sensor device** 

Plataniotis, 2007).

**2.3 Real-time imaging systems** 

An understanding of the hardware support for colour imaging graphics is fundamental to the analysis of real-time performance of the system. Some specialized hardware for realtime imaging applications involves high-performance computers with structural support for complex instruction sets and imaging coprocessors. Inexpensive pixel processors are also available, and scalable structures are increasingly being used for real-time imaging applications. But building systems with highly specialized processors is not always easy, particularly because of poor tool support. Therefore, many commonly deployed colour imaging systems use consumer-grade personal computers. There are many architectural issues relating to real-time performance, such as internal/ external memory bus width, memory access times, speed of secondary storage devices, display hardware issues, and colour representation and storage, to name a few. Collectively, these design issues involve three trade-off problems — schedulability versus functionality, performance versus resolution, and performance versus storage requirements. Real-time design of imaging systems, then, involves making the necessary decisions that trade one quality for another, for example, speed versus resolution. For example, one of the main performance challenges in designing real-time image processing systems is the high computational cost of image manipulation. A common deadline found in many processing systems involves screen processing and update that must be completed at least 30 times per second for the human eye to perceive continuous motion.

Because this processing may involve more than a million pixels, with each colour pixel needing one or two words of storage, the computational load can be staggering. For the purposes of meeting this deadline, then, the real-time systems engineer can choose to forgo display resolution, or algorithmic accuracy. Finding improved algorithms or better hardware will also help meet the deadlines without sacrifice — if the algorithms and hardware behaviorare bounded, which, as mentioned, is not always the case. In this section, however, we confine our discussion to two important hardware issues (Lukac & Plataniotis, 2007).

#### **2.3.1.2 Colour representation and real-time performance**

Our interest is in the appropriate representation of colour in the physical hardware, because as previously noted, this has real-time performance implications. The colour buffer may be implemented using one or more of the following storage formats:


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The software has PC-based image processing without the need of extensive programming knowledge. The complete C source code of these DLLs is part of the standard pack. Point, local and global, morphological operations texture, image sequence histograms procedures, colour transformations, automatic counting and interactive measuring, pattern recognition

Adds machine vision and image processing functionality to LabVIEW and ActiveX

It is an analytical Imaging - complete commercial image analysis program for windows which is used in biological and industrial measurement environments. Optimas implements hundreds of measurement, image processing, and image management operations, all

It is an easy to use graphical software application for creating scientific and general purpose imaging application. Use the Point & Click script to develop applications quickly. Simply configure the tools you want to use and add them to your script to create powerful imaging

Image Processing Toolbox™ provides a comprehensive set of reference-standard algorithms and graphical tools for image processing, analysis, visualization, and algorithm development. You can perform image enhancement, image deblurring, feature detection, noise reduction, image segmentation, spatial transformations, and image registration. Many functions in the toolbox are multithreaded to take advantage of multicore and

ImageJ is a public domain Java image processing program inspired by NIH Image. It runs, either as an online applet or as a downloadable application, on any computer with a Java 1.4 or later virtual machine. Downloadable distributions are available for Windows, Mac OS,

Myriad image processing software was also used for comparing images and measuring surface area of images. This program has two steps for measuring images. First step was

**3. Commonly used image processing and analysis softwares** 

**3.1 AdOculos** 

**3.2 IMAQ vision** 

**3.3 Optimas** 

can be done by using this software.

containers (National Instruments).

**3.4 GLOBAL LAB image** 

multiprocessor computers.

Mac OS X and Linux.

**3.6 ImageJ** 

**3.7 Myriad** 

available from the graphical user interface.

applications without writing any code.

**3.5 Matlab mage processing toolbox** 

 Three bytes per pixel (true or RGB colour), which yields approximately 16.8 million colours.

RGB is often considered the standard in many programming languages, and it is used in many important colour image data formats, such as JPEG and TIFF. True colour uses 24 bits of RGB colour, 1 byte per colour channel for 24 bits. A 32-bit representation is often used to enhance performance, because various hardware commands are optimized for groups of 4 bytes or more (Lukac & Plataniotis, 2007).
