**Muhammad Sarfraz**

Department of Information Science, College of Life Sciences, Kuwait University, Sabah AlSalem University City, Shadadiya, Kuwait

**IV**

stress–strain diagram.

advanced video coding). HEVC complexity is mainly a return to the motion estimation (ME) module that represents the important part of encoding time, which has made several researchers turn around the optimization of this module. Some researchers are interested in hardware solutions exploiting the parallel processing of field programmable gate array, graphics processing unit, or other multicore architectures, while other researchers are focused on software optimizations by inducing fast mode decision algorithms. In this context, this chapter proposes a fast HEVC encoder configuration to speed up the encoding process. Fast configuration uses different options such as early skip detection, early control unit termination, and coded block flag fast method modes. Regarding the algorithm of ME, diamond search is used in the encoding process through several video resolutions. A time saving of around 46.75% is obtained with acceptable distortion in terms of video quality and bit rate compared to the reference test model HM.16.2. This chapter can

be compared to techniques in the literature for better evaluation.

Saldaña-Heredia et al., in Chapter 3, discuss "Experimental and Theoretical Investigation on the Shear Behaviour of High Strength Reinforced Concrete Beams Using Digital Image Correlation." They show that digital image processing is a useful tool that improves pictorial information for human interpretation and is mainly used for storage, transmission, and representation of different data. In this chapter, the authors introduce an optical technique that couples physical analysis with image processing for a measurement system. Optical methods have been used to obtain the stress–strain relation by different invasive and non-invasive methods, and the chapter talks about a novel non-invasive methodology to measure stress–strain evolution. The proposed technique is based upon a single laser beam reflected on a cross-section of ductile materials (steel and aluminum) while they are under a compression load. A laser beam has been measured by using Gaussian beam propagation equations. It has been proposed that the reflection area of the laser will change as the material surface area is compressed and these differences are analyzed by using digital image processing. This technique has enabled the construction of a

The next chapter, by Tahenni and Lecompte, is on "Experimental and Theoretical Investigation on the Shear Behaviour of High Strength Reinforced Concrete Beams Using Digital Image Correlation." In this chapter, an experimental investigation is carried out on high-strength concrete (HSC) beams with and without transverse reinforcement. The beams were tested by bending under two concentrated loads using the digital image correlation technique. In the experimental device, the shear zone between the support and the loading point was digitized by a high-resolution camera. The numerical analysis of the recorded images is performed by Gom-Aramis software to obtain the deformation of concrete and to monitor the crack evolution in terms of width, spacing, and length. The different models to determine the shear capacity of reinforced concrete beams, used by the principal universal design codes such as the American ACI 318, British Standard BS 8110, European Eurocode 2, New Zealand NZS 3101, and Indian Standard IS456, have been extrapolated to HSC to evaluate the applicability of these regulations originally developed for ordinary concrete to HSC. The experimental results show that all the code models underestimate the shear contribution of HSC and at the same time greatly overestimate the transverse reinforcement contribution. Among the four models, Eurocode 2 yields the best predictions of the ultimate shear strength of HSC.

Munera et al. are motivated by an industrial approach of the use of artificial vision in Chapter 5, "Implementation of an Artificial Vision System for Welding in the

**1**

**Chapter 1**

Image Processing

sional analog signals by analog means [1, 2].

wide range of areas [3–30].

software, and applications [31–57].

*Muhammad Sarfraz*

**1. Introduction**

Introductory Chapter: On Digital

An image would be called as an analog image if its pictorial representation can be represented in analog wave formats, whereas an image would be called as a digital image if its pictorial representation can be represented or stored in the data in digital form. Similarly, field of image processing can be categorized into digital image processing and analog image processing. Digital image processing (or digital imaging), in the area of computer science today, is defined as processing digital images through some algorithms using digital computers, whereas, analog image processing is any image processing task that can be conducted on two-dimen-

After the invention of digital computers, digital image processing took various advantages over analog image processing. A broad range of techniques and methods, in the form of a variety of algorithms, came into existence. One can find a rich literature toady which can be applied to the input image data to solve various problems. These problems may include converting images into digital data, calibration, removing the build-up of noise and distortion during processing, etc. Since images are defined over two dimensions (and perhaps more) digital image processing may be modeled in the form of multidimensional systems. Digital image processing has evolved rapidly with the development of computers, mathematics, and the real-life demand for a variety of applications in

In the current age and time, digital imaging is used widely in various real-life applications. There is a number of potential digital imaging applications that include different areas such as environment, industry, medical science, agriculture, military, film, television, photography, robotics, remote sensing, medical diagnosis, reconnaissance, architectural and engineering design, art, crime prevention, geographical information systems, communication, intellectual property, retail catalogs, nudity-detection, face finding, industrial applications, and others. The increasing trends, needs, and applications of imaging make it more difficult to process images for desired objectives. This leads to the idea of capturing, storing, finding, retrieving, analyzing, and using images in everyday life under the computing environment. Being a computer-based technology, digital imaging carries out automatic processing, manipulation, and interpretation of visual information. It plays a significant and important role in various aspects of real life. It is also highly useful in many areas, disciplines and fields of art, and science and technology. This chapter is specifically dedicated to digital imaging history, methodologies, tasks,
