Digital image enhancement methods for multimedia technology - Free Essay Example

Sample details Pages: 17 Words: 5214 Downloads: 2 Date added: 2017/06/26 Category Statistics Essay Did you like this example? Chapter 1 1.1 Introduction In todays communications network s multimedia is a growing field. There are increasing demands on incorporating visual aspect to other modes of communications. It is therefore unable to be avoided to have situations in which the video and transmitted images being corrupted or degraded in their perceptual quality by variety of ways. 1.2Digital Image Processing An image is defined as two- dimensional function, f(x,y), where x,y are plane coordinates and the amplitude of f at any pair of coordinates (x,y) is called the intensity or gray level of the image. When x, y and the intensity values of f are all finite and discrete quantities, we call the image a digital image. To processing the image by means of computer algorithms is called as digital image processing. As compared to analog image processing, digital image processing has many advantages. It can avoid problems such as signal distortion, image degradation and build-up of noise during processing. Don’t waste time! Our writers will create an original "Digital image enhancement methods for multimedia technology" essay for you Create order 1.2 Image Restoration and Enhancement Methods: Now days digital images have covered the complete world. Images are acquired by photo electronic or photochemical methods. The sensing devices tend to reduce a quality of the digital images by introducing the noise and blur due to motion or misfocus of camera. One of the first applications of digital images was in the news paper industry, when pictures were sent by submarine cable between New York and London. Introduction of cable picture transmission system in the early 1920s reduced the time required to transport a picture across Atlantic from more than a week to less than three hours. Some of the initial problems in improving the visual quality of these early digital pictures were related to the selection of printing procedures and distribution of intensity levels. Digital image processing techniques began in the late 1960s and early 1970s to be used in medical imaging, remote Earth resources observations and astronomy. Tomography was invented independently by Sir Godfrey N. Hounsfield and Professor Allan M.Cormack who shared the 1979 Nobel Prize in medicine for their invention. But, X-rays were discovered in 1985 by Wilhelm Conrad Roentgen. Geographers use the similar technique to study the pollution patterns from aerial and satellite imagery. Image enhancement and restoration procedures are used to process the degraded images of unrecoverable objects or experimental results too expensive to duplicate. The use of a gray level transformation which transforms a given empirical distribution function of gray level values in an image into a uniform distribution has been used as an image enhancement as well as for a normalization procedure.( I. Pitas) Image enhancement refers to increase the image quality by sharpening certain image features (edges, boundaries and contrast) and reducing the noise. Digital image enhancement and restoration are two dimensional filters. They are broadly classified into linear digital filters and non linear filters. Linear digital filter can be designed or implemented either spatial domain or Frequency domain. (K.S. Thyagarajan) In Spatial Domain methods refers to the image plane itself .Image processing methods, spatial domain methods are based on direct manipulation of pixels in an image. The intensity transformations and spatial filtering are two principal categories of spatial domain methods. In Frequency domain methods, first image is transformed to frequency domain. It means that, the Fourier transform of the image is computed and performed all processing on the Fourier transform of the image. Finally Inverse Fourier transform is performed to get the resultant image. (Rafael C.Gonzalez and Richard E.Woods) Image Enhancement Techniques are Median filtering Neighborhood averaging Edge Detection Histogram techniques In 1980, recent work on c.c.d. scanners is reviewed and solid-state scanners which include on-chip signal processing functions are described. Future trends are towards `smart scanners; these are scanners with on-chip real-time processing functions, such as analogue-to-digital conversion, thresholding, data compaction, edge enhancement and other real-time image processing functions.( Chamberlain,1980) The image enhancement algorithm first separates an image into its lows (low-pass filtered form) and highs (high-pass filtered form) components. The lows component then controls the amplitude of the highs component to increase the local contrast. The lows component is then subjected to a non-linearity to modify the local luminance mean of the image and is combined with the processed highs component. The performance of this algorithm when applied to enhance typical undegraded images, images with large shaded areas, and also images degraded by cloud cover will be illustrated by way of examples. (Peli, T., 1981) Enhancement algorithms based on local medians and interquartile distances are more effective than those using means and standard deviations for the removal of spike noise, preserve edge sharpness better and introduce fewer artifacts around high contrast edges. They are not as fast as the mean-standard deviation equivalents but are suitable for large data sets treated in small machines in production quantities.( Scollar,I.,1983) Filtering CT images to remove noise, and thereby enhance the signal-to-noise ratio in the images, is a difficult process because CT noise is of a broad-band spatial-frequency character, overlapping frequencies of interest in the signal.A measurement of the noise power spectrum of a CT scanner and some form of spatially variant filtering of CT images can be beneficial if the filtering process is based upon the differences between the frequency characteristics of the noise and the signal. For evaluating the performance, used a percentage standard deviation, an index representing contrast, a frequency spectral pattern, and several CT images processed with the filter. (Okada., 1985) A two-dimensional least-mean-square (TDLMS) adaptive algorithm based on the method of steepest decent is proposed and applied to noise reduction in images. The adaptive property of the TDLMS algorithm enables the filter to have an improved tracking performance in nonstationary images. The results presented show that the TDLMS algorithm can be used successfully to reduce noise in images. The algorithm complexity is 2(NN) multiplications and the same number of additions per image sample, where N is the parameter-matrix dimension. The algorithm can be used in a number of two-dimensional applications such as image enhancement and image data processing.( Hadhoud,M.M.,1988) Image processing techniques are used to determine the range and alignment of a land vehicle. The approach taken is to establish a state vector of quantities derived from an image sequence, and to refine this over the mission. The image processing techniques applied fall into the generic categories of enhancement, detection, segmentation, and classification. Approaches to estimating the alignment and range of a vehicle in computationally efficient ways are presented. The estimates of quantities extracted from single image frames are subject to errors. This approach facilitates the integration of results from multiple images, and from multiple sensor systems.( Atherton, T.J.,1990) The JPEG coder has proven to be extremely useful in coding image data. For low bit-rate image coding (0.75 bit or less per pixel), however, the block effect becomes very annoying. The edges also display `wave-like appearance. An enhancement algorithm is proposed to enhance the subjective quality of the reconstructed images. First, the pixels of the coded image are classified into three broad categories: (a) pixels belonging to quasi-constant regions where the pixel intensity values vary slowly, (b) pixels belonging to dominant-edge (DE) regions which are characterized by few sharp and dominant edges and (c) pixels belonging to textured regions which are characterized by many small edges and thin-line signals. An adaptive mixture of some well-known spatial filters which uses the pixel labeling information for its adaptation is used as the adaptive optimal spatial filter for image enhancement. (Kundu, A.1995) The videotexts are low-resolution and mixed with complex backgrounds; image enhancement is a key to successful recognition of the videotexts. Especially in Hangul characters, several consonants cannot be distinguished without sophisticated image enhancement techniques. In this experiment, after multiple videotext frames containing the same captions are detected and the caption area in each frame is extracted, five different image enhancement techniques are serially applied to the image: multi-frame integration, resolution enhancement, contrast enhancement, advanced binarization, and morphological smoothing operations and tested the proposed techniques with the video caption images containing both Hangul and English characters from various video sources such as cinema, news, sports, etc. The character recognition results are greatly improved by using enhanced images in the experiment. (Sangshin Kwak.,2000). The use of an adaptive image enhancement system that implements the human visual system (HVS) has the properties for contrast enhancement of X-ray images. X-ray images are poor quality and are usually interpreted visually. The HVS properties considered are its adaptive nature, multichannel mechanism and high nonlinearity. This method is adaptive, nonlinear and multichannel, and combines adaptive filters and homomorphic processing. The median filtering method is a simple and efficient way to remove impulse noise from digital images. This novel method has two stages. The first stage is to detect the impulse noise in the image. In this stage, first one identify the noise pixel and second one the pixels are roughly divided into two classes, which are noise-free pixel and noise pixel. Then, the second stage is to eliminate the impulse noise from the image. In this stage, only the noise-pixels are processed. The noise -free pixels are directly copied to the output image. Here, hybrid of adaptive median filter with switching median filter method is used. The adaptive median filter framework in order to enable the flexibility of the filter to change it size accordingly based on the approximation of local noise density. The switching median filter framework in order to speed up the process and also allows local details in the image to be preserved. (Kong, NSP., 2008) One of the advantages of Level-2 Improved tolerance based selective arithmetic mean filtering technique is that this filtering technique is to detect and remove the noisy pixels and restore the noise free information. However the removal of impulse noise is often accomplished at the expense of blurred and distorted features of edges. Therefore it is necessary to preserve the edges and fine details during filtering. (Deivalakshmi,S., 2010) An efficient non-linear cascade filter is used to removal of high density salt and pepper noise in image and video. This method consists of two stages to enhance the filtering. The first stage is the Decision based Median Filter (DMF) which is used to identify pixels likely to be contaminated by salt and pepper noise and replaces them by the median value. The second stage is the Unsymmetrical Trimmed Filter, either Mean Filter (UTMF) or Midpoint Filter (UTMP) which is used to trim the noisy pixels in an unsymmetrical manner and processes with the remaining pixels The basic idea is that, though the level of denoising in the first stage is lesser at high noise densities, the second stage helps to increase the noise suppression. Hence, this method is very suitable for low, medium as well as high noise densities even above 90%. This algorithm shows better image and video quality in terms of visual appearance and quantitative measures. ( Balasubramanian, S.,2009) The enhancement algorithm enhances CR image detail and CR image enhanced has good visual effect, so the method id suit for edge detail enhancement of CR medicine radiation image. (Zhang., 2010). Three dimensional TV is considered as next generation broadcasting service.TOF sensors are a relatively new technology allowing real time capture of both photometric and geometric scene information. In order to generate the natural 3D video, first we develop a practical pipeline including TOF data processing and MPEG-4 based data transmission and reception. Then we acquire colour and depth videos from TOF range sensor. Then Alpha matting and enhancement are performed to handle fuzzy and hairy objects (Ji-Ho Cho Sung-Yeol Kim Lee, 2010). Chapter 2 2.1 Median Filtering Median Filtering is a non -linear signal enhancement technique for the smoothing of signals, the suppression of impulse noise, and preserving of edges. In the one dimensional case it consists of sliding a window of an odd number of elements along the signal, replacing the centre sample by the median of the samples in the window. Noise is any undesirable signal. Noise is everywhere and thus we have to learn to live with it. Noise gets introduced into data via any electrical system used for storage, transmission, and/or processing. In addition, nature will always play a noisy trick or two with data under observation. When encountering an image corrupted with noise you will want to improve its appearance for a specific application. The Techniques applied are application-oriented. Also, different procedures are related to the types of noise introduced to the image. Some important types of noise are: Gaussian or white, Rayleigh, Salt-pepper or impulse noise, periodic, sinusoidal or coherent, uncorrelated, and granular. In statistics, a median is described as the numeric value separating the higher half of a sample, a population, or a probability distribution, from the lower half. The median of a finite list of numbers can be found by arranging all the numbers from lowest value to highest value and picking the middle one. For example: The observations are [7,5,6,8,1,3,8,5,4]. First, we are arranging in ascending order or lowest value to highest value. [1, 3, 4, 5, 5, 6, 7, 8, 8] Then the middle one is picked. Here, number of observations n=9, it is an odd number. The middle value=5. So, the median =5. If there is an even number of observations, then there is no single middle value; the median is then usually defined to be the mean of the two middle values. For example: observations are [7,5,6,8,1,3,8,5,4,6]. First, we are arranging in ascending order or lowest value to highest value. [1, 3, 4, 5, 5, 6, 6, 7, 8, 8] Then the middle one is picked. Here, number of observations n=10, it is an even number. So, averaging the observation 5 and 6 and gets the median value. The observation values are 5 and 6. The averaging value of 5 and 6 gives 5.5. So, the median =5.5. Most scanned images contain noise caused by the scanning method (sensor and its calibration-electrical components, radio frequency spikes) this noise may look like dots of black and white. Median filter helps us by erasing the black dots, called the Pepper, and it also fills in white holes in an image, called salt Impulse Noise. Its like the mean filter but is better in pixels and will not affect the other pixels significantly. This means that mean does that. Preserving sharp edges Median value is much like neighbourhood Median filtering is popular in removing salt and pepper noise and works by replacing the pixel value with the median value in the neighbourhood of that pixel. When applied on: 1. We do brightness -ranking by first placing the brightness values of the pixels from each neighbourhood in ascending order. 2. The median or middle value of this ordered sequence is then selected as the representative brightness value for that neighbourhood. 2.2Median Filter Action The median filter is also sliding -window spatial filter, but it replaces the centre pixel value in the window by the median of all pixel values in the window. As for the mean filter, the kernel is usually square but can be any shape rectangular, circular, etc depends on an image. An example of median filtering of a single 3*3 window of values is shown in figure 2.1. Unfiltered Values 6 2 0 3 97 4 19 3 10 To arrange the pixel value in ascending order: 0,2,3,3,4,6,19,97 The median value=4(Here no of items=9) The centre pixel value 97 is replaced by the median value 4 as shown below. Median filtered Values * * * * 4 * * * * Figure 2.2 This illustrates one of the celebrated features of the median filter: its ability to remove impulse noise. The median filter is also widely claimed to be edge-preserving since it theoretically preserves step edges without blurring. However, in the presence of noise it blurs edges in images slightly. 2.3 Synthetic Image Let us consider 6*6 window size. 3 1 5 6 9 2 7 3 8 4 5 1 2 4 3 3 2 8 1 8 9 6 2 7 1 9 2 6 5 8 3 5 7 9 8 2 Here, we take 3*3 mask size, to find out the median value. 3 1 5 6 9 2 7 3 8 4 5 1 2 4 3 3 2 8 1 8 9 6 2 7 1 9 2 6 5 8 3 5 7 9 8 2 The order of the pixel value:1,2,3,3,3,4,5,7,8.The median value of this mask size=3. Here, the centre pixel value 3 is replaced by the median value 3. 3 1 5 6 9 2 7 A B C D 1 2 E F G H 8 1 I J K L 7 1 M N O P 8 3 5 7 9 8 2 Here, we find out the A to P value as shown in figure 2.5. First, we find out the median value for 3*3 mask size and replacing the original centre pixel value by these values. To find A: Order: 1, 2, 3,3,3,4,5,7,8. Median=3. To find B: Order: 1, 3, 3,3,4,4,5,6,8. Median=4. To find C: Order: 2, 3, 3,4,4,5,6,8,9. Median=4. To find D: Order: 1, 2, 2,3,4,5,6,8,9. Median=4. Similar way, we have to calculate F to P. To find P: Order: 2, 4,5,5,5,8,8,9 Median=5. The final output of synthetic image of 6*6 window as shown in figure 2.6. 3 1 5 6 9 2 7 3 4 4 4 1 2 4 4 4 4 8 1 4 4 4 5 7 1 4 4 5 5 8 3 5 7 9 8 2 By checking the synthetic image output by using Matlab. To Refer the Matlab Coding in Appendix A. Output: 3 1 5 6 9 2 7 3 4 4 4 1 2 4 4 4 4 8 1 4 4 4 5 7 1 4 4 5 5 8 3 5 7 9 8 2 Both Hand calculation synthetic image output and Matlab synthetic image output are same. 2.4 Median Filter Implementation on Mat lab: In past years, linear filters become the most popular filters in image processing. The reason of their popularity is caused by the existence of robust mathematical models which can be used for their analysis and design. However, there exist many areas in which the nonlinear filters provide significantly better results. The advantage of non linear filters lies in their ability to preserve edges and suppress the noise without loss of details. The success of nonlinear filters is caused by the fact that image signals as well as existing noise types are usually nonlinear. Due to the imperfection of image sensors, images are often corrupted by noise. The impulse noise is the most frequently referred type of noise. The most cases, impulse noise is caused by malfunctioning pixels in camera sensors, faulty memory locations in hardware, or errors in data transmission. We distinguish two common types of impulse noise. They are Salt-and-Pepper noise and the random valued shot noise. For images corrupted by salt-and-pepper noise, the noisy pixels have only maximum or minimum values. In case of random valued shot noise, the noisy pixels have arbitrary value. Traditionally, the impulse noise is removed by a median filter which is the most popular non linear filter .A standard median filter gives poor performance for images corrupted by impulse noise with higher intensity. A simple median filter utilizing 3*3 or 5*5 pixel window is sufficient only when the noise intensity is less than approximately 10-20%. Here, we implement the median filter using Matlab. To refer the Matlab coding in Appendix B. Output: problem The Noisy Image is corrupted by Salt-and-Pepper noise. By using median filter, 3*3 mask size most of noise has been eliminated. If we smooth the noisy image with larger median filter 7*7 mask size, all the noisy pixels disappear as shown above figure. 3.0 Neighbourhood Averaging Filters Neighborhood averaging filters are similar to mean filters. The Neighborhood averaging filter is the simplest low pass filter; here all coefficients are identical. These filters sometimes are called Averaging filters. The characteristics of neighborhood averaging are defined by kernel height, width and shape. When Kernel size increases, the smoothing effect also increases. The idea behind these filters is straight forward. By replacing the every pixel value in an image by the average of the intensity levels in the neighborhood defined by the filter mask, this process results in an image with reduced sharp transitions in intensity levels. The window is usually square, but can be any shape like rectangular, circular, etc. depending on the size of an image. Each point in the smoothed image, is f(x,y)obtained from the average pixel value in a neighbourhood of (x,y) in the input image. For example, if we use a 33 neighbourhood around each pixel we would use the mask 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 Each pixel value is multiplied by 1/9, summed, and then the result placed in the output image. This mask is successively moved across the image until every pixel has been covered. That is, the image is convolved with this smoothing mask (also known as a spatial filter or kernel). However, one usually expects the value of a pixel to be more closely related to the values of pixels close to it than to those further away. This is because most points in an image are spatially coherent with their neighbours; indeed it is generally only at edge or feature points where this hypothesis is not valid. Accordingly it is usual to weight the pixels near the centre of the mask more strongly than those at the edge. Some common weighting functions include the rectangular weighting function above (which just takes the average over the window), a triangular weighting function, or a Gaussian. In practice one doesnt notice much difference between different weighting functions, although Gaussian smoothing is the most commonly used. Gaussian smoothing has the attribute that the frequency components of the image are modified in a smooth manner. Smoothing reduces or attenuates the higher frequencies in the image. Mask shapes other than the Gaussian can do odd things to the frequency spectrum, but as far as the appearance of the image is concerned we usually dont notice much. The arithmetic mean is the standard average, often simply called the mean. The mean may be confused with the median, mode or range. The mean is the average of a set of values, or distribution; however, for probability distributions, the mean is not necessarily the same as the median, or the mode. For example: The observations are [7,5,6,8,1,3,8,5,4]. First, we find out the total value for these observations. Total=7+5+6+8+1+3+8+5+4=47 Then, finding the average one. Here, number of observations n=9. Average=total/9. =47/9 Average=5.22(Equivalent to 5) So, the average =5. 3.1 Synthetic image Let us consider 6*6 window size. 3 1 5 6 9 2 7 3 8 4 5 1 2 4 3 3 2 8 1 8 9 6 2 7 1 9 2 6 5 8 3 5 7 9 8 2 Figure 3.1 Here, we take 3*3 mask size, to find out the Neighbourhood averaging value. 3 1 5 6 9 2 7 3 8 4 5 1 2 4 3 3 2 8 1 8 9 6 2 7 1 9 2 6 5 8 3 5 7 9 8 2 The order of the pixel value:1,2,3,3,3,4,5,7,8.The averaging value of this mask size=4. Here , the centre pixel value 3 is replaced by the averaging value 4. By using this method, we have to calculate the median value for whole window size 6*6. 3 1 5 6 9 2 7 A B C D 1 2 E F G H 8 1 I J K L 7 1 M N O P 8 3 5 7 9 8 2 We find out the A to P value by using the figure 3.3 as shown above. First find out the median value for 3*3 mask size and replacing the original centre pixel value by these values. To find A: Order:1,2,3,3,3,4,5,7,8. Averaging=(1+2+3+3+3+4+5+7+8)/9=4. To find B: Averaging=(1+3+3+3+4+4+5+6+8)/9. Averaging=5. To find C: Averaging=(2+3+3+4+4+5+6+8+9)/9. Averaging=5. To find D: Averaging=(1+2+2+3+4+5+6+8+9)/9. Averaging=5. Similar way, we have to calculate F to P. To find P: Averaging=(2+4+5+5+5+8+8+9)/9 Averaging=6. The final output of synthetic image of 6*6 window as shown in figure 3.4. 3 1 5 6 9 2 7 4 5 5 5 1 2 5 5 5 5 8 1 5 6 5 6 7 1 4 5 6 6 8 3 5 7 9 8 2 By checking the synthetic image ouput by using matlab. To refer the Matlab coding in the Appendix C. ouput: 3 1 5 6 9 2 7 4 5 5 5 1 2 5 5 5 5 8 1 5 6 5 6 7 1 4 5 6 6 8 3 5 7 9 8 2 Both Hand calculation synthetic image output and Mat lab synthetic image output are same. 3.3 Neighbourhood Averaging Filter Implementation on Mat lab To refer the Matlab coding in the Appendix D. Output: The Noisy Image is corrupted by Salt-and-Pepper noise. By using neighbourhood averaging filter, 3*3 mask size most of noise has been eliminated. If we smooth the noisy image with larger neighbourhood averaging filter 7*7 mask size, all the noisy pixels disappear as shown above figure. Chapter 4 Histogram Equalization In Histogram Equalization, the goal is to obtain a uniform histogram for the output image. In other words, the goal of histogram equalization is to distribute the gray levels within an image so that every gray level is equally likely to occur. Histogram equalization increase the brightness and contrast of dark and low contrast image making features observable that were not visible in the image. It also used to standardize the brightness and contrast of image. The process of histogram equalization is the mapping function that maps the input histogram function to the uniformly distributed output histogram function. Consider for a moment continuous intensity values and let the variable r denote the intensities of an image to be processed. As usual, where r is in the range [0, L-1], with r=0 representing black and r=L-1 representing White. For r satisfying these conditions, we focus on the transformations of the form. s=T(r) 0T(r) L-1 The output intensity level is s and for every pixel in the image the intensity is r. T(r) is a monotonically increasing function in the interval 0rL-1; and 0 T(r) L-1 for 0 r L-1. T(r) is a strictly monotonically increasing function in the interval 0rL-1. (a) T(r) is monotonically increasing that output intensity values will never be less than corresponding input values. (b) (b)Range of output intensities is same as the input. The mapping from s back to r will be one-to-one, thus preventing ambiguities. Histogram Equalization Action: The probability of occurrence of intensity level rk in a digital image is approximated by Pr (rk) =nk/MN where k=0, 1, 2 , L-1 Where MN is the total number of pixels in the image, nk is the number of pixels that have intensity rk, and L is the number of possible intensity levels in the image. A plot of pr(rk) versus rk is commonly referred to as a histogram. The discrete form of transformation is given by Thus, output image is obtained by mapping each pixel in the input image with intensity rk into a corresponding pixel with level sk in the output image. The T (rk) in this equation is called as Histogram Equalization. Let us consider 3 bit image (L=8) of size 64*64 pixels (MN=4096) has the intensity distribution shown in Table where the intensity levels are integers in the range [0, L-1] = [0, 7]. rk Nk pr(rk)=nk/MN r0=1 790 0.19 r1=2 1023 0.25 r2=3 850 0.21 r3=4 656 0.16 r4=5 329 0.08 r5=6 245 0.06 r6=7 122 0.03 r7=8 81 0.02 By using pr (rk) values, we calculate s values. To find s0: s0=7*0.19=1.33 To find s1: s1=7pr (r0) +7pr (r1) =7*0.19+7*0.25 = 3.08 To find s2: s2=7pr (r0) +7pr (r1) +7pr (r2) =7*0.19+7*0.25+7*0.21 =4.55 To find s3: s3=7pr (r0) +7pr (r1) +7pr (r2) +7pr (r3) =7*(0.19+0.25+0.21+0.16) =5.67 To find s4: s4=7pr (r0) +7pr (r1) +7pr (r2) +7pr (r3) +7pr (r4) =7*(0.19+0.25+0.21+0.16+0.08) =6.23 Similarly, s5=6.65, s6=6.86 and s7=7 At this point, the s values still have fractions because they were generated by summing probability values, so we round them to the nearest integer. s0=1.33-1 s4=6.23-6 s1=3.08-3 s5=6.65-7 s2=4.55-5 s6=6.86-7 s3=5.67-6 s7=7.00-7 These are values of the equalized histogram. Observe that there are only five distinct levels. Synthetic image: Let us consider 6*6 window size. 3 1 5 6 9 2 7 3 8 4 5 1 2 4 3 3 2 8 1 8 9 6 2 7 1 9 2 6 5 8 3 5 7 9 8 2 First, we calculate rk,nk and pr(rk) The intensity levels are integers in the range [0, L-1] = [0, 9]. Rk Nk pr(rk)=nk/MN r0=0 0 0.0 r1=1 4 0.11 r2=2 6 0.17 r3=3 5 0.14 r4= 2 0.06 r5 4 0.11 r6 3 0.08 r7 3 0.08 r8 5 0.14 r9 4 0.11 Here, MN=6*6=36. By using these values, we can calculate s values. To find s0: s0=9*0.0=0 To find s1: s1=9pr (r0) +9pr (r1) =9*0.0+9*0.11 = 0.99 To find s2: s2=9pr (r0) +9pr (r1) +9pr (r2) =9*0.0+9*0.11+9*0.17 =2.52 To find s3: s3=9pr (r0) +9pr (r1) +9pr (r2) +9pr (r3) =9*(0.00+0.11+0.17+0.14) =3.78 To find s4: s4=9pr (r0) +9pr (r1) +9pr (r2) +9pr (r3) +9pr (r4) =7*(0.00+0.11+0.17+0.14+0.06) =4.32 To find s5: s5=s4+9pr (r5) =4.32+9*0.11=5.31 s6=s5+9pr (r6) =5.31+9*0.08=6.03 s7=s6+9pr (r7) =6.03+9*0.08=6.75 s8=s7+9pr (r8) =6.75+9*0.14=8.01 s9=s8+9pr (r9) =8.01+9*0.11=9 At this point, the s values still have fractions because they were generated by summing the probability values, so we round them to nearest integer. s0=0.0-0 s5=5.31-5 s1=0.99-1 s6=6.03-6 s2=2.52-3 s7=6.75-7 s3=3.78-4 s8=8.01-8 s4=4.32-4 s9=9.00-9 By checking the synthetic image output by using Matlab. To refer the Matlab coding in the Appendix E. Output: Histogram Implementation on Matlab: To Refer the Matlab Coding in the Appendix F. Output Chapter 5: 5.0 Edge detection Edge detection is a problem of fundamental importance in image analysis. In typical images, edges characterize object boundaries and are therefore useful for segmentation, registration, and identification of objects in a scene. In this section, the construction, characteristics, and performance of a number of gradient and zero-crossing edge operators will be presented. An edge is a jump in intensity. The cross section of an edge has the shape of a ramp. An ideal edge is a discontinuity (i.e., a ramp with an infinite slope). The first derivative assumes a local maximum at an edge. For a continuous image , where x and y are the row and column coordinates respectively, we typically consider the two directional derivatives and . Of particular interest in edge detection are two functions that can be expressed in terms of these directional derivatives: the gradient magnitude and the gradient orientation. The gradient magnitude is defined as (5.1) and the gradient orientation is given by -(5.2) When the first derivative achieves a maximum, the second derivative is zero. 5.2 Edge Detection Action An example of median filtering of a single 3*3 window of values is shown in figure. Before edge detected values 6 2 0 3 97 4 19 3 10 Let us consider f(x,y)=97. Then we calculate the Horizontal Edge H(x,y)=f(x,y)-f(x+1,y) -(5.3) =97-3 =94 Calculate Vertical Edge, V(x,y) V(x,y)=f(x,y)-f(x,y+1) (5.4) =97-4 =93 Calculate Positive Diagonal Edge: M(x,y)=f(x,y)-f(x+1,y+1)(5.5) =97-10 =87 Calculate Negative Diagonal Edge: N(x,y)=f(x,y)-f(x+1,y-1) -(5.6) =97-19 =78 Here, Threshold value set to 40.Then H(x,y)40 || V(x,y)40 || M(x,y)40 ||N(x,y)40 f(x,y)=0 Otherwise , f(x,y)=97. For this example, f(x,y)=0 Edge Detected * * * * 0 * * * * This illustrates one of the celebrated features of the Edge Detection: its ability to detect the edges of the image. 5.3 Synthetic Image Let us consider 6*6 window size. 3 1 5 6 9 2 7 3 8 4 5 1 2 4 3 3 2 8 1 8 9 6 2 7 1 9 2 6 5 8 3 5 7 9 8 2 Figure 5.1 For this synthetic image, we assume threshold value=4. Let us consider the f(x,y)=3 then we calculate H(x,y),V(x,y),M(x,y) and N(x,y) Horizontal Edge(x,y) H(x,y)=f(x,y)-f(x+1,y)=3-4=-1 Vertical Edge,V(x,y) V(x,y)=f(x,y)-f(x,y+1)=3-8=-5 Positive Diagonal Edge: M(x,y)=f(x,y)-f(x+1,y+1)=3-3=0 Negative Diagonal Edge: M(x,y)=f(x,y)-f(x+1,y-1)=3-2=0 Then H(x,y)Threshold || V(x,y)Threshold || M(x,y)Threshold||N(x,y)Threshold f(x,y)=0 Otherwise, f(x,y)=9. For this, f(x,y)=9 Similarly, we calculate the rest of f(x,y). 3 1 5 6 9 2 7 9 0 9 0 1 2 9 9 9 9 8 1 0 0 0 9 7 1 0 9 0 0 8 3 5 7 9 8 2 Figure 5.2 By checking the Synthetic image output with Matlab. To refer the Matlab coding in the Appendix G. ouput 3 1 5 6 9 2 7 9 0 9 0 1 2 9 9 9 9 8 1 0 0 0 9 7 1 0 9 0 0 8 3 5 7 9 8 2 By checking the synthetic image ouput by using matlab 5.3 Edge Detection Implementation on Mat lab Chapter 6 6.0 Conclusion: This report investigates a new hardware structure of a content based median filter, capable of performing impulse noise removal for gray-scale images. The noise detection procedure takes into account the differences between the central pixel and surrounding pixels of a neighbourhood. From this investigation, it can remove up to 95% of noise from highly corrupted image. The impulse noise (Salt-and -pepper noise) is removed using median filtering technique; the embedded C Code is implemented in order to achieve the clear image. The details inside the image are preserved and the RMSE value is small. At present it is being used in digital cameras to overcome the noise produced during transmission of data and noise produced due to malfunctioning pixels in camera sensor. In future high responsive median filter will be used which will give more reduced Image and enhancement method will be used to implement a 3D Television.

Learning Style The Factor That Affecting Learning...

Learning style is one of the factor that affecting learning-relating performance.{148} The method an individual choose to approach a learning situation has an impact on his performance and learning outcomes. Learning define as a process that lead to change which occur as a result of experience, and increases the potential for improving performance and future learning. Any concern about selection of effective teaching, must begin with a consideration of students preference learning styles. According to theories of learning style, people are thinking and learning in different ways. There is a different between cognitive style and learning style. The cognitive style is a person s ideal mode of thinking, problem solving and remembering. While the learning style is individual s approach to learn a task.{148} An individual can used different strategies to learn a task. Learning style can be visual, aural, interactive, print, kinesthetic or olfactory.{147} It is crucial to know how the lea rning style work and how to best foster it. Learning and performance are best fostered when students engage in practice that focuses on a specific goal or criterion targets and appropriate level of challenge and is of sufficient quantity and frequency to meet the performance criteria. Simon Cassidy described in his research paper about different learning style theoretical models and he made comparison between models characterization.{148} Dr. Cassidy discussed Kolb s learning theory, whichShow MoreRelatedImplications of Hegelian Dialectic to Thinking and Education1988 Words   |  8 Pagesintroduce new ideas and values among them (Khalid, 1983). Education is the process through which knowledge is transmitted from one individual or section of society to another individual or section. The teacher is the most important factor of the teaching-learning process. The school’s most important influence is the teacher. He sets the tone of the classroom and establishes the mood of the group. He is the authority figure providing the direction for behavior. He is a model and is consciouslyRead MoreBusiness Strategies Of The Automobile Industry1553 Words   |  7 Pagesindividual mobility (BMW Group, 2015). BMW was renowned as the most reputable company in the world by forbes.com that caters strong innovative engineering excellence which derives elevated performance. (Forbes, 2014).Despite of such supremacy BMW has being going through a rough phase because of strategic issues relating to economic circumstances, legal and regulatory risks , severe completion, globalization and diversification that have made BMW producers struggle for market share. (BMW Annual ReportsRead MoreThe Characteristics Of A Learner2429 Words   |  10 Pagesfeature that affects the rate of skill acquisition. Within the characteristics of a learner there are five factors which are prior experience, confidence, heredity, ability and personality. These features can have either a positive or a negative impact on the way I progressed in the skill. These f eatures can impact on the way I develop the skills. The first characteristic of the leaner affecting the rate of skill acquisition is prior experience. â€Å"Prior experience is if you have perviously learned aRead MoreThe Leadership Style Of A School2353 Words   |  10 PagesIntroduction A leadership style is defined by the approach of the leader in the process full of activity. The school leadership style is one of the most important and key factors in achieving team progress due to its impact on contentment, motivation, obligation, quality and the stage or level of teacher?s acting as well as its influence on the success of the students. I have realized that the main role of a leader in a school is to use the most effective leadership style so as to bring about theRead MoreEssay on SimVenture Evaluation Report LeedsMetUn12393 Words   |  50 Pages‘significant  confidence’  and  Ã¢â‚¬Ëœhigh  confidence’.         6   Table  2.0  Ã‚   Table  3.0  Ã‚   Table  4.0  Ã‚   Table  5.0  Ã‚   Table  6.0  Ã‚   Table  7.0  Ã‚   Table  8.0  Ã‚   Table  9.0  Ã‚   Table  10  Ã‚   Table  11  Ã‚      Appendices   Appendix  A   Appendix  B   Appendix  C   Copy  of  questionnaire   All  charts  relating  to  each  tables  per  section   Qualitative  data      8   9   10 11 12 13 14 15 16 17       24 28 69 1.  Introduction         This   report   outlines   a   process   of   evaluation   of   the   business   game   simulation   ‘SIMVenture’,   undertaken  on  behalf  of  the  Institute  for  EnterpriseRead MoreBehavioural Difficulties : Behavioral Difficulties1645 Words   |  7 Pages‘behaviour as if it is the problem’ and labelling the child with ‘behavioural difficulties’ is problematic. Langley explains â€Å"‘†¦challenging’ behaviour as a name for unacceptable performances is not a diagnosis, nor is it a special education condition.† (Langley, 2009, p.9) He goes on to explain that â€Å"One of the key factors in behaviour management is in understanding why challenging behaviour occurs† (Langley, 2009, p.1). After research, I have discovered there are two main things that need to occurRead MoreReflection Paper On The Gibbs Reflective Cycle1891 Words   |  8 Pagesthe whole experience and finally focus on an action plan should I be faced with a similar situation in future. I chose this reflective model above others as I felt that each stage of the cycle complimented both the essay structure and my writing style. This reflective process will enable me as a practitioner to think about the positives as well as areas for improvement. This type of critical thinking as a form of reflection is the cornerstone of nursing practice according to Turner and Beddoes 2007Read MoreThe Impact of Icts on Students Academic Performance at University4866 Words   |  20 Pagesgood a good performance and job opportunities in their future. The main purpose of ICT is to create a highly skilled and educated peoples or learners in the everyday life. Due to this, it is good to provide ICT tools like internet, computer and network hardware and software and other equipment and services associated with these technologies in order to go similar with globalization. So the integration of ICT in academic performance will help learners (students) to expanding learning opportunitiesRead MoreStrategic Management Assignment2850 Words   |  12 Pages †¢ To develop knowledge and practice in the application of strategic management concepts and techniques. †¢ To develop analytical, evaluative and enterprise skills/competences associated with the formulation of organisational strategy. The learning outcomes assessed in this assignment include the student’s ability to: 1. Understand and apply a range of strategic management principles/concepts and contribute to the development of strategic options. 2. Critically evaluate how analysis of theRead More: Critical Analysis of Organisational Structure and Culture in Relation to Business Performance5081 Words   |  21 Pagesthem not as calamities but challenges, and absorb their lessons†¦..† We can hypothesise that ‘underlying culture ‘refers to organisational culture; ‘bad patch’ refers to a period where business performance is low or employees are moving away form the organisational culture resulting in low business performance; ‘Properly regarded, setbacks can be instructive’ means that the organisation can learn from their mistakes and overcoming obstacles, and ‘Enduring cultures’ are cultures that are long lasting

Individually write a monologue Essay Example For Students

Individually write a monologue Essay During the scene the group decided to go behind the bins for a cigarette. Then they encountered Jake who Russell had been bullying. We showed that this was a regular encounter by making Jake look scared on the immediate arrival of Russell and the gang. We showed Russells aggression towards Jake by using a lot of physical and verbal, threatening behaviour. We showed Carol and Natasha jeering, laughing and egging Russell on. However Polly attempted to stick up for Jake and Russell was unsure of how to react. In the end he left, rather angrily because his fun has been spoiled. Our role-play communicated to the audience the stronger of the characters and the position and comfort zones within the group. We felt it was important to show the attitudes towards each other, because that is what the main change, during the play, concerns. In the after role-play, we wanted to show the changes in the social status and the relationship changes between the characters. Again we set the scene in the schoolyard and the situation was again similar. I decided to do this so that the contrast between the scenes would be very pronounced and obvious. In this scene, it was very important that we showed Natasha and Carols individuality and Russell to be less arrogant. At first we found it very challenging to show this especially without the use of costume and props. We tried to do this by making Jake as popular and arrogant as Russell once was however it didnt work as it was too much of a character change. In the end we resorted to making Carol and Natasha have different opinions and more importantly instead of Carol copying Natashas words we had Carol speaking first. Russells stage presence influenced his original character quite prominently so it was important that we used the same technique in the after scene. We did this by having him take a back seat so he wasnt stage centre and the most influential character on the stage. It was necessary to show a change in Jake, however that change we felt was quite hard to show, we decided to make him more confident within the group but we didnt want his overall personality to change. It was also critical to get the right balance of change in the way in which the characters treated each other, it was quite easy to show this by removing the aggression that Russell had towards Jake, but at the same time, Jake had previously been bullied by Russell so there was still a certain degree of awkwardness between the two characters so we had to keep tension in the scene. It was also important that the characters were played by the same people, so that the audience could focus on the change, rather than try and work out the confusion a different cast would cause. So, I again played Polly. She is the one character that didnt change in the play, although because the group had warmed to Jake, she didnt have to stick up for him. I kept her personality the same, by repeating my actions from before. During the scene the group were again planning to smoke however Jake was invited. Carol decided not to tag along and went somewhere else with Polly, but Natasha went with the boys. This showed the audience that Carol now felt confident enough to do what she wanted and that Jake is now accepted in the group. The role-play activity gave me a deeper insight into the play, in the terms that I had to look in detail at the characters to suggest how they would have acted before the play and then look at the transformation of the character during the play, to suggest the behaviour change for after the play. Overall I think our role-plays worked effectively as planned, however I think they could have been executed more clearly and fluently with a little more practise and with our group having more confidence in the role they were playing. Thematic Analysis Of Things Fall Apart EssayNow look at him prince bloody charming. I just wish someone would stand up to him, tell them the truth, a few punches would do him the world of good. I would, but who will I hang round with then. I like his company when he isnt being bolshy, and plus all the skirt comes our way when Im with him, like flies around shit! Mind you I think Natasha is a bit fed up with me. I dont know why like, it must be that time of the month or something, Ive done nothing wrong! We went out Saturday night, had a laugh. She was happy enough then, so why get all moody with me. You know what, I sound daft but I thought we had something, maybe I was wrong. I did make a mistake though trying to be like Russell I should have known that wouldnt impress her. In fact she is fun to be around. When you wipe away that bravado and all the make-up, she has a great smile; in fact she is dead canny. You know all this red alert crap, I think its just for attention. She is never like that when its just me and her. I thought we were made for each other, you know. What can I do, she must have a decent reason, or is it the its not you, its me or maybe she wants us to just be friends, how can I tell her I want it to be more? I never know what to say without sounding stupid, cmon how do you tell a girl like Natasha James that you love her? After producing the monologue, one of us had to perform it to the class, I volunteered. I decided that if I wanted to perform it effectively and as if Shane was actually saying it, I would have to do it without a script. To enhance my performance I thought that my use of stage and facial expressions would engage the audience and I also thought that performances with movement and different levels were more exciting so I incorporated these points into my piece. The other members of my group directed me which gave me some good points, which I alone wouldnt have thought of. During my performance I made eye contact with my audience and used pauses and silences to my advantage. Within the monologue there is an array of emotions so it was important to emphasise these with my actions and tone. I was pleased with my performance however it could have been improved if I added more depth to my voice and varied the pace in correspondence to the words, but overall I got a lot of positive feedback.

Protest songs in the 1960s

Table of Contents Protest Songs Artists/Musicians involved and their messages Conclusion Works Cited As one author quoted, â€Å"Music cleanses the understanding; inspires it and lifts it into a realm which it would not reach if it were left to itself† (Henry Ward Beecher). He couldn’t have been more right. Protest songs are considered to have span over the centuries, continents and race.Advertising We will write a custom essay sample on Protest songs in the 1960s specifically for you for only $16.05 $11/page Learn More They are songs that majorly address issues that range from social injustices like racism and slavery to political matters like wars. They either inspire crowds for mass actions or simply just create awareness of the problem. Protest songs are, however, known to be associated with peaceful social movements. In the 1960’s, the prevalent issues then were the Civil Rights movement and the Vietnam War. This article exp lores the many artists/musicians who were actively involved during this time and the protest songs that they sung that meticulously addressed the then current issues. Protest Songs Protest songs stem from the folk music (Protest Song 1) that was present in the early 19th century. The folk music of that period addressed matters of social injustices and folk music eventually transitioned to protest songs in the1960s as a result of the dissatisfaction of the public and their current political and social environment especially after the end of World War 2 (Hurry et al. 162). Folk songs were generally characterized by their simplicity and repetitive choruses (Hurry et al. 162). These songs were also appropriate alternatives to the other genres especially jazz (Gonipraw 4). Protest songs however transitioned from the â€Å"acoustic-oriented folk styling to rock-based rhythms† (Protest Music 1) although this entirely depended on the musician. Gonipraw attributes this transition to à ¢â‚¬Å"artistic decisions, record company involvement and a growing disillusionment among young people† (Gonipraw 5). The then artists/musicians were known to contribute greatly to protests that sought to address the civil rights concerns and the Vietnam War (Gonipraw 4). Gonipraw has argued that: â€Å"when the needs and goals of the people were sung together by the people, a force was created, capable of defeating alienation† (Gonipraw 4). Peter Seeger who was a known musician in the 1960s summarized it rightly.Advertising Looking for essay on art and design? Let's see if we can help you! Get your first paper with 15% OFF Learn More Artists/Musicians involved and their messages Some of the popular artists and groups of this time were: â€Å"Woody Guthrie, Peter Seeger, Bob Dylan, Phil Ochs, Joe Hill, The Weavers, Leedbelly, Joan Baez, Neil Young, The Doors, Country Joe and the Fish, Jefferson Airplane among others† (Cogan et al. 126). Peter, Paul a nd Mary were also another famous trio during this time (Protest Music 1). Woody Guthrie and Peter Seeger were very vigorous in their approach since they traversed their country and actively became involved with the political events of that time (Gonipraw 3). For the Vietnam War, songs that were composed had messages that basically advocated the end of the war or showed its ineffectiveness. â€Å"Where have all the flowers gone?† (Perone 20) is one such a song written by Peter Seeger, and it even had the privilege to be sung in Vietnam by the soldiers. The song’s story is about â€Å"young girls picking flowers that eventually end on the graves of their dead soldier husbands† (Ruehl 1). Another song by Peter Seeger â€Å"Last train to Nuremberg† (Perone 61) is seen to directly point accusing fingers to the President Nixon and others as to blame for the blood shed in Vietnam. Barry McGuire is also one such artist whose song â€Å"Eve of Destruction† in 1965 had a stern message against the war in Vietnam (Protest Music 1). Bob Dylan is also a famous artist whose music mostly addressed â€Å"world problems† (Cogan et al. 128). His lyrics unlike other musicians were not as direct but were â€Å"deliberately trying to obscure the meaning of his politics in a political context† (Cogan et al. 129). He is considered to have been the best of them all since â€Å"he redefined what protest music said and what it sounded like† and he is also know to have mentored many others (Gonipraw 7). Some of his songs that were really popular include â€Å"Blowin in the wind† and â€Å"Master of War†. During the Civil Rights Movement, the African Americans really made use of song as a way to â€Å"send hope, calm sorrows and heal† themselves even as they struggled to find freedom (Freeman 2).Advertising We will write a custom essay sample on Protest songs in the 1960s specifically for you for o nly $16.05 $11/page Learn More The songs they sang had been used in the slave era and were changed to work for them during their other struggles: for instance â€Å"Oh Freedom† sang by Joan Baez was initially â€Å"written about freedom in heaven†¦Ã¢â‚¬  (Freeman 2) but it was adapted to describe â€Å"the harsh realities of segregation that included shootings, burning and bombing of churches† (Freeman 2). The protests were to fight for equality with other Americans and they were sang â€Å"almost nightly in the churches from the South† (Freeman 5). The fact that these musicians even stood by Dr Martin Luther King Jr. is very remarkable, since it signifies that it was not just about African American civil rights struggle but it was more of a joint effort from leaders and musicians alike. In about 1968, the vitality of protests song was on the decline and this could be attributed to many things: President Nixon’s administration may ha ve had a hand in it (Protest Music 1). Nevertheless, John Lennon’s song â€Å"Give Peace a Chance† in 1969 seems to have brought a good closure of the protest songs to more of peaceful songs (Ruehl 1). Its message was just simply summarized in its title and its popularity did not stop then but continued throughout time especially where there was any â€Å"peace movement involved†. Not only did the songs lose their power, but time also saw some very influential musician like Phil Ochs who started out very well, lose ground as a musician (Gonipraw 6). The question however is, did the songs achieve the purpose for which they were composed? They did. Simply because protest songs were a part and parcel of the movements in 1960 and the two went in hand in hand (Gonipraw 6). The popularity was more on the musicians as compared to politicians since they had much more impact than the latter (Gonipraw 6). Conclusion Songs and music definitely have a very great impact be i t in mobilizing masses or passing a message across from time immemorial. The protest songs in their time are known to have had a great influence on the leaders, the Vietnamese soldiers and even unifying many from all races during the Civil Rights movement. Their popularity in the 1960s fully served their purpose and some of the songs are even sung to date and they even persisted through the 1970s. Though not as popular, there are songs which are sung presently that could fall in the category of protest songs.Advertising Looking for essay on art and design? Let's see if we can help you! Get your first paper with 15% OFF Learn More Works Cited Cogan et al. Encyclopedia of Politics, the Media and Popular Culture. Santa Barbra CA, 2009. Print. Freeman, Tracy. Spiritual and Freedom Songs. Coded Communication, n.d. Web. Goniprow, Dan. Where have all the Protest Songs Gone? Senior Honors Projects, 2007. Web. Hurry et al. Heinemann Advanced Music. London, UK: Heinemann, 2001. Print. Perone John. Songs of Vietnam Conflict. New York, NY: Greenwood, 2011. Print. Protest Music. American Renaissance. Music, n.d. Web. Protest Song. Music. Neo Himanism, 2004. Web. Ruehl, Kim. Best Classic Anti-War Songs. Folk Music, 2011. Web. This essay on Protest songs in the 1960s was written and submitted by user Jon Kline to help you with your own studies. You are free to use it for research and reference purposes in order to write your own paper; however, you must cite it accordingly. You can donate your paper here.