INTRODUCTION Chapter 1
A digital image is a numeric representation of a 2-dimensional image. It is represented as a finite set of some digital values, which are picture elements/pixels. Pixel values represent grey levels, colors, intensities, heights etc. Also, a digital image is an estimation of a real scenario, which is explained by digitization.
Digital Image Processing, as name expresses, is processing applied on digital images. It includes a number of techniques which are used to manipulate the digital images by computers. The question arises is that what is the need of processing the digital images? The answer is because the image received from the sensors on the satellite can contain some flaws or deficiencies like
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Therefore, presence of these characteristics makes retinal images complex to understand. Intensity of retinal images is also an issue to be considered because there is less difference in the foreground and background intensities in these images. If we considers the grey level images then we observes that the shape, size and local grey level of blood vessels may vary hugely and many background features can have similar attributes similar to vessels. Signal noise, drift in the intensity of an image and also, lack of contrast in image also pose tough challenges to the extract blood vessels in the retinal images.
In this thesis, a major hindrance in understanding the retinal images, noise, is considered. The process of denoising is a crucial and significant area that is strongly needed to be performed to understand the retinal images better. Noise in Digital Image
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e^(-az)@0 f or z<0)┤ forz≥0 μ=b/a σ^2=b/a^2
K=(a(〖b-1)〗^(b-1))/(b-1)! e^(-(b-1))
1.3.5. Rayleigh noise
Velocity images and radar range and velocity image generally contain noise of Rayleigh distribution. Figure 1.8: Rayleigh Noise (Kamboj and Rani)
Probability density function, Mean, Variance of Rayleigh noise are respectively: p(z)={█(2/b (z-a) e^(〖-(z-a)〗^2/b)@ 0 for z<a)┤ for z≥a μ=a+√(πb/4) σ^2=(b(4-μ))/4
After a brief discussion about noise, it is observed that noise is an unwanted signal but, this is not true for all the noise, that is, noise is always not bad (which can be a special case). But generally, it is important to remove noise because noise is visually unpleasant, it is also bad for compression and for analysis. Presence of noise in an image provides low accuracy to analyze the image. Noise removal can be done in a number of ways. In this thesis, noise is going to be removed through ICA(Independent Component Analysis) which will separate the noise signal and that will be discussed in the chapter 3- Materials and Methods.
In the next section, the very important key stages in digital image processing is discussed that is Image
The output resulted from this focus on the high frequency content in the image without changing anything in the image phase. This result with an image enhanced in contrast sometime this enhancement results with ugly artifacts. 3.5- Logarithmic Transform Domain Transform Domain allow us or gives us the ability to show the frequency content of the image, however it is uninformative or compacted. In figure (3) this will be obvious .By working on the problem we discovered that the solution is to take the logarithm of the image.
Multilinear principal component analysis (MPCA) is a mathematical procedure that uses multiple orthogonal transformations to convert a set of multidimensional objects into another set of multidimensional objects of lower dimensions. There is one orthogonal (linear) transformation for each dimension (mode); hence multilinear. This transformation aims to capture as high a variance as possible, accounting for as much of the variability in the data as possible, subject to the constraint of mode-wise orthogonality. MPCA is a multilinear extension of principal component analysis (PCA).
\begin{eqnarray*} d_2^{fBm} & = & \frac{\ln{\frac{S}{K}} + \frac{1}{2}(r ( T - t) - \frac{\sigma^2{( T^{2H}
-2 -1 -0.50 0.5 1 2 0.5 x 0 0.1 0.1 0.2 0.3 0.2 0.1 5 5 12/9 KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS ACHB/CE EE315: PROBABILISTIC METHODS IN ELECTRICAL ENGINEERING b) M g ( x) x 2 0 x 0 x 2 0 0 x ( a ) b For ( ) ( ) ( ) [ ( )] ( ) ( ) For ( ) ( ) ( ) ( ) ( ) ( ) ( ) { ( ) ( ) ( ) { ∫ ( ) ( ) ∫ ( ) ( ) ( ) 4 4 4 4 13/9 KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS ACHB/CE EE315: PROBABILISTIC METHODS IN ELECTRICAL ENGINEERING 2 G (x) forb x 0 x 0.5 0 x 1 (b) 2 0 x 2 0 x 4 c) Binomial, Poisson (discrete RV), Uniform, Exponential, Rayleigh (Continuous RV) 2 2 2 2 2 14/9 KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS ACHB/CE EE315: PROBABILISTIC METHODS IN ELECTRICAL ENGINEERING QUESTION 3 a) [ ] ∫ ( ) ∫ ∫ ∫ ∫ ([ ] ∫ ) ( [ ] ) =
The file will be sent along for analysis by an expert. c) Images In this section I shall discuss
HCPCS level 1 uses CPT codes to identify medical services & procedures level 2 is used to identify the products, supplies, and services that are not in CPT codes ICD-10 used for diagnosis and in patient procedures There 's so many different types of services and procedures within the medical field that different codes are needed to specifically identify them properly. Coding was created to make medical billing simple. Proper coding will ensure accurate and timely reimbursements.
(1994, 2004 and 2014). The RS imagery will be preprocess it in order to get a maximum accuracy. The preprocess mean will be ERDAS imagine software and possible GIS
After, the color space transformation we are going to extracts the texture vector from that image using sparse texture model. The texture vectors are represented as a set of distributions which is used to cluster the texture data using K-means clustering algorithm. Finding the number of clusters which consists set of texture distributions used to calculate TD metric. After, calculating the TD metric, the image is over segmented using SRM algorithm, which results the image being divided into large number of regions. Next, each region is independently classified as representing normal skin or lesion based on the textural contents of that region.
Body Cameras Don 't Work If They Are Not Worn or Not Turned On After Michael Brown, the unarmed black teen who was shot in Ferguson, Missouri, America made it known that we want police officers to wear body cameras. Police Departments responded by saying they want officers to wear body cameras, too. So, if everybody wants the officers to wear body cameras why are there still so many incidents of questionable conduct that are not recorded? According to the Huffington Post, only 2 of the 27 large U.S. cities looked at had all of their officers equipped with body cams.
When observing this image it is apparent to indicate that the
Reading assignment number three is important because of the rapid growth in technology. The reading assignment touches on the subject of using visual imagery and learning how to properly analyze what we have seen. Analyzed properly a picture can tell the viewer many things. Visual imagery is becoming a more progressive.
How often have you used your smart phone to look up directions to an unknown location? How often have you GONE for a run and used some kind of app or electronic map to determine the distance of your exercise? Chances are at some point you have used imagery taken by satellites orbiting the earth, to get a better idea of location, distance, or a general understanding of what something looks like. Today, unclassified imagery is utilized by millions of people across the world. The accuracy, availability, clarity, and large area of coverage creates a database of imagery that is at the world’s disposal, and it’s unclassified.
A conventional artwork and drawing can be converted into graphic by scanning into digital form. Graphic can be categorized into two types, Bitmap Graphic and Vector Graphic. Bitmap graphic is made up of many dots arranged in matrix and specific order while Vector graphic is stored as a mathematical formula. Thus, Bitmap graphic does not require special software to read because no processing is require before displaying it. However, due to this reason, it occupies more space compared to Vector graphic and take longer time to transfer over network.
Understanding Pica Eating disorders are serious, life-threating mental illnesses that are on the rise in society today. Obsession with one’s physical appearance, emotionally problems, or sole desire to eat can contribute to an eating disorder. There are serious consequences that come with the disorders that can be very harmful to an individual with an eating disorder, and often even fatal. Most commonly talked about eating disorders include, bulimia nervosa, anorexia nervosa, and binge eating. Pica is another disorder that is on the rise today with very little comprehension on exactly what is it, who it targets, or how it is treated.
Digital architecture involves the use of computer modelling, programming, simulation and imaging to create both virtual forms and physical structures. The ways in which architecture is formed, created, presented, and marketed is transforming – in relation to the transition to a digital society. Digital architecture allows complex calculations that delimit architects and allow a diverse range of complex forms to be created with great ease using computer algorithms. Architecture created digitally might not involve the use of actual materials (brick, stone, glass, steel, wood).