Plato Vs Nozick Essay - 1152 Words

Utopia is â€Å"an imagined place or state of things in which everything is perfect.† according to en.oxforddictionaries.com. Although this is the ideal definition, there are many varying definitions of what a utopia is. Throughout history, many philosophers have argued their different views on what utopia is. This paper argues why two philosophers Plato and Nozick, disagree on utopia and how they might challenge one another’s ideas. The Republic is considered one of the first pieces of philosophy that touch on utopia. In order to create the perfect city, in which Plato describe as a polis, he argues would be run by a perfectly developed society. To achieve this perfectly developed society, there needs to be specialization and class†¦show more content†¦For example, on page 396, it states â€Å"Neither the shoemaker nor the farmer should ever attempt to do one another’s job, because they would do so poorly, or, at the very least, do so without the job’s highest potential ever being fulfilled†. (396e) Plato argues that each individual can practice one pursuit well but cannot practice many of them well because being skilled at one thing is most important. If one â€Å"tried to do this and dabbled in many things, he would surely fail to achieve distinction in all of them† ( 394e). Plato might challenge another philosopher like Nozick, in questioning the effectiveness of a freeform society. Plato believes that having each citizen do a single job to the best of their ability, will allow the city to work like a perfect system. A free-flowing system would in Plato’s eyes be unorganized and chaotic, with no structure. In Anarchy, State and Utopia, Nozick discusses his ideal society. He begins by addressing a fundamental idea he believes occurs in any development of utopia. That is, people are different, and their preferences for an ideal community also differ. He states â€Å"The best of all possible worlds for me will not be that for you. The world, of all those I can imagine, which I would most prefer to live in, will not be precisely the one you would choose. Utopia, though, must be, in some restricted sense, the best for all of us; the best world imaginable, for each of us.† (Nozick, p.298). Nozick’s solution to this is a free-formShow More RelatedPrinciples of Microeconomics Fifth Canadian Edition20085 Words   |  81 Pagesanticompetition, 384–385 asymmetric information, 493–495 collusion, 378–379 common resources, 240–242 consumer choice, 475–476, 478–479 consumption tax, 255–256 corporate income tax, 261–263 deadweight loss, 171–173 discrimination, 429–431 drugs, monopoly vs. generic, 324–325 fixed and variable costs, 300 gasoline taxes, 216–217 gas prices, 122–123 Giffen good, 475–476 income effects on labour supply, 478–479 income redistribution, 437–438 income tax, 255–256 minimum wage, 127–130 monopolies, 324–325 payroll

Spatial Filtering Fundamentals Free Essays

string(35) " determined by the ranking result\." 4/28/2008 Spatial filtering fundamentals by  Gleb  V. Tcheslavski:  gleb@ee. lamar. We will write a custom essay sample on Spatial Filtering Fundamentals or any similar topic only for you Order Now edu http://ee. lamar. edu/gleb/dip/index. htm Spring 2008 ELEN 4304/5365 DIP 1 Mechanics of spatial filtering Considering frequency domain filtering, the effect of LPF applied to an image is to blur (smooth) it. Similar smoothing effect can be achieved by using spatial filters (spatial masks, kernels, templates, or windows). We discussed that a spatial filter consists of a neighborhood and a pre-defined operation performed on the image pixels defining the neighborhood. The result of filtering – a new pixel with coordinated of the neighborhood’s center and the value defined by the operation. g y p If the operation is linear, the filter is said to be a linear spatial filter. Spring 2008 ELEN 4304/5365 DIP 2 1 4/28/2008 Mechanics of spatial filtering Assuming a 3 x 3 neighborhood, at any point (x,y) in the image, the response of the spatial filter is g ( x, y ) = w(? 1, ? 1) f ( x ? 1, y ? 1) + w(? 1, 0) f ( x ? 1, y ) + †¦ + w(0, 0) f ( x, y ) + †¦ + w(1,1) f ( x + 1, y + 1) Filter coefficient Pixel intensity In general: g ( x, y ) = s =? a t =? b ? ? w(s, t ) f ( x + s, y + t ) a b Spring 2008 ELEN 4304/5365 DIP 3 Mechanics of spatial filtering Here a mask size is m x n. m = 2a + 1 n = 2b + 1 Where a and b are some integers. For a 3 x 3 mask Spring 2008 ELEN 4304/5365 DIP 4 2 4/28/2008 Spatial correlation and convolution Correlation is a process of moving the filter mask over the image and computing the sum of products at each location as previously described. Convolution is the same except that the filter is first rotated by 1800. For a 1D case, we first zeropad f by m-1 zeros on each size. We compute a sum of products in both cases†¦ Spring 2008 ELEN 4304/5365 DIP 5 Spatial correlation and convolution Correlation is a function of displacement of the filter. A function containing a single 1 with the rest being zeros is g g g called a discrete unit impulse. Correlation of a function with a discrete unit impulse yields a rotated version of a function at the location of the impulse. To perform a convolution, we need to pre-rotate the filter by 1800 and perform the same operation as in correlation. Spring 2008 ELEN 4304/5365 DIP 6 3 4/28/2008 Spatial correlation and convolution In a 2D case, for a filter of size m x n, we pad the image with m-1 rows of zeros at the top and bottom and n-1 columns of zeros on the left and right. For convolution, we pre-rotate the mask and perform the sliding sum of products. Spring 2008 ELEN 4304/5365 DIP 7 Spatial correlation and convolution Correlation of a filter w(x,y) of size m x n with an image f(x,y) is w( x, y ) f ( x, y ) = s =? a t =? b ? ? w(s, t ) f ( x + s, y + t ) ? ? w(s, t ) f ( x ? s, y ? t ) a b a b Convolution of a filter w(x,y) of size m x n with an image f(x,y) is w( x, y ) ? f ( x, y) = s =? a t =? b Spring 2008 ELEN 4304/5365 DIP 8 4 4/28/2008 Vector representation of linear filtering It is convenient sometimes to represent a sum of products as R = ? wk zk = w T z k =1 Filter coeffs Image intensities mn For example, for a 3 x 3 filter: p , R = ? wk zk = w T z k =1 Spring 2008 ELEN 4304/5365 DIP 9 9 Generating spatial filter masks Generating an m x n linear spatial filter requires specification of mn mask coefficients. These coefficients are selected based on what the filter is supposed to do keeping in mind that all we can do with linear filtering is to implement a sum of products. Assuming that we need to replace the pixels in an image with the average pixel intensities of a 3Ãâ€"3 neighborhood centered on those pixels. If zi are the intensities, the average is R= 9 1 9 ? zi 9 i =1 Which is: R = ? wi zi = w T z; i =1 ELEN 4304/5365 DIP wi = 1 9 10 Spring 2008 5 4/28/2008 Smoothing spatial filters Smoothing filters are used for blurring and noise reduction. Blurring may be implemented in preprocessing tasks to remove small details from an image prior to large object extraction. The output of a smoothing (averaging or lowpass) linear spatial filter is the average of the pixels contained in the neighborhood of the filter mask. By replacing the value of every pixel in an image by the average of the intensity levels in the neighborhood defined by a filter mask, the resulting image will have reduced â€Å"sharp† transitions in intensities. Since random noise typically corresponds to such transitions, we can achieve denoising. Spring 2008 ELEN 4304/5365 DIP 11 Smoothing spatial filters However, edges (characterized by sharp intensity transitions) will be blurred. Examples of such masks: 1) A box filter – spatial averaging filter 3Ãâ€"3; 2) Weighted average filter – attempt to reduce blurring: g a g ( x, y ) = s =? a t =? b ? ? (s, t ) f ( x + s, y + t ) s =? a t =? b b ? ? w(s, t ) 12 a b Spring 2008 ELEN 4304/5365 DIP 6 4/28/2008 Smoothing spatial filters The effect of filter size. The original 500Ãâ€"500 image And the results of smoothing with a square averaging filter of sizes m = 3, 5, 9, 15, 25, and 35 pixels. Spring 2008 ELEN 4304/5365 DIP 13 Smoothing spatial filters Frequently, b lurring is desired for ease of object detection: an original Hubble image, the result of applying a 15Ãâ€"15 averaging mask to it and the result of thresholding with a threshold of 25% of the highest intensity. Spring 2008 ELEN 4304/5365 DIP 14 7 4/28/2008 Order-statistic (nonlinear) filters Order-statistic filter are nonlinear spatial filters whose response is based on ordering (Ranking) the pixels in the neighborhood and then replacing the value of the center pixel by the value determined by the ranking result. You read "Spatial Filtering Fundamentals" in category "Papers" The median filters are quite effective against the impulse noise (salt-and-pepper noise). The median of a set of values is such that half the values in the set are greater than the median and half is lesser than it: Ex: the 3Ãâ€"3 neighborhood has values (10, 20, 20, 20,15, 20, 100, 25, 20). These values are ranked as (10, 15, 20, 20, 20, 20, 20, 25, 100). The median will be 20. There are also max and min filters. Spring 2008 ELEN 4304/5365 DIP 15 Order-statistic (nonlinear) filters Original image with salt-andpepper noise Spring 2008 Noise reduction with a 3Ãâ€"3 averaging mask ELEN 4304/5365 DIP Noise reduction with a 3Ãâ€"3 median mask 16 8 4/28/2008 Sharpening spatial filters: foundations The main objective of sharpening is to highlight transitions in intensity. Since averaging is analogous to spatial integration, we y g g g p g can assume that sharpening is analogous to differentiation in space. The derivatives of a digital function are defined in differences. The first derivative must be: 1) Zero in areas of constant intensity; 2) Non-zero at the onset and end of an intensity step or ramp; 3) Non-zero along ramps of constant slope. The second derivative must be: 1) Zero in areas of constant intensity; 2) Non-zero at the onset and end of an intensity step or ramp; 3) Zero along ramps of constant slope. Spring 2008 ELEN 4304/5365 DIP 17 Sharpening spatial filters: foundations The first-order derivative: ?f = f ( x + 1) ? f ( x) ? x The second-order derivative: ?2 f = f ( x + 1) + f ( x ? 1) ? 2 f ( x) ? x 2 It can be verified that these definitions satisfy the conditions for derivatives. Spring 2008 ELEN 4304/5365 DIP 18 9 4/28/2008 Sharpening spatial filters: foundations The circles indicate the onset or end of intensity transitions. The sign of the second derivative changes at the onset and end of a step of ramp. The second derivative enhances fine details much better than the first derivative. This is suitable for sharpening. Spring 2008 ELEN 4304/5365 DIP 19 Using the second derivative for image sharpening – the Laplacian We consider isotropic filters – the response is independent of the direction of the discontinuity in the image Such filters are image. rotation invariant. The simplest isotropic derivative operator is the Laplacian: ?2 f ? 2 f ? f = 2 + 2 ? x ? y 2 Therefore: ? 2 f = f ( x + 1, y ) + f ( x ? 1, y ) + f ( x, y + 1) + f ( x, y ? 1) ? 4 f ( x, y ) The Laplacian is a linear operator since derivatives are linear operators. Spring 2008 ELEN 4304/5365 DIP 20 10 4/28/2008 Using the second derivative for image sharpening – the Laplacian The Laplacian can be implemented by these filter masks Since the Laplacian is a derivative operator, its use highlights intensity discontinuities in the image and deemphasize regions with slow varying intensity levels levels. It tends to produce images having grayish edge lines and other discontinuities, and a dark, feature-less background. Spring 2008 ELEN 4304/5365 DIP 21 Using the second derivative for image sharpening – the Laplacian Background features can be preserved together with the sharpening effect of the Laplacian by adding the Laplacian image to the original. If the definition of the Laplacian has a negative central coefficient, the Laplacian image must be subtracted rather than added to obtain a sharpening result. In general: g ( x, y ) = f ( x, y ) + c 2 f ( x, y ) ? ? ? Output intensity Input intensity -1 – if the center is negative; +1 otherwise Spring 2008 ELEN 4304/5365 DIP 22 11 4/28/2008 Using the second derivative for image sharpening – the Laplacian The Laplacian Laplacian with scaling The original (blurred) image The image sharpened with mask 2 The image sharpened with mask 1 Spring 2008 ELEN 4304/5365 DIP 23 Unsharp masking and highboost filtering An approach used for many years to sharpen images is: 1. Blur the original image; 2. Subtract the blurred image from the original (the result is called the mask): g mask ( x, y ) = f ( x, y ) ? f ( x, y ) Original Blurred image 3. Add the mask to the original: g ( x, y ) = f ( x, y ) + k ? g mask ( x, y ) Here k is a weight. Spring 2008 ELEN 4304/5365 DIP 24 12 4/28/2008 Unsharp masking and highboost filtering When k = 1 – unsharp masking; k 1 – highboost filtering; k 1 – de-emphasize the contribution of a mask. The shown intensity profile can be viewed as a horizontal scan through a vertical edge transition from a dark to li ht t a light region. i This approach is similar to Laplacian method. Spring 2008 ELEN 4304/5365 DIP 25 Unsharp masking and highboost filtering Original ( slightly blurred) image Smoothed with a Gaussian smoothing filter 5Ãâ€"5 Unsharp mask Result of using unshapr mask (k = 1) Result of using highboost filtering with k = 4. 5 Spring 2008 ELEN 4304/5365 DIP 26 13 4/28/2008 Gradient method First derivatives can be implemented for nonlinear image sharpening using the magnitude of the gradient: ? ? f ? g x ? ? ? x ? ? ? f ? grad ( f ) ? ? ? = ? ? ? g y ? ? ? f ? ? ? y ? ? ? The gradient vector points in the direction of the greatest rate of g (x,y). g (length) gradient change of f at location ( y) The magnitude ( g ) of g 2 2 M ( x, y ) = ? f = g x + g y Is the value of rate of change at (x,y) in the direction of gradient. Spring 2008 ELEN 4304/5365 DIP 27 Gradient method M(x,y) is an image of the same size as the original and is called the gradient image. Magnitude makes M(x,y) non-linear. It is more s itable in some applications to use: suitable se M ( x, y ) ? g x + g y For an image where z5 represent the pixel f(x,y) and z1 represent the pixel f(x-1,y-1), the simplest (Roberts) definitions for gradients are: M ( x, y ) = ( z9 ? z5 ) + ( z8 ? z6 ) 2 2 M ( x, y ) ? z9 ? z5 + z8 ? z6 However, Roberts cross-gradient operators lead to masks of even sizes, which is inconvenient. ELEN 4304/5365 DIP 28 Spring 2008 14 4/28/2008 Gradient method The smallest masks with central symmetry (ones we are interested in) are 3Ãâ€"3. The gradient can be approximated for such masks as following: ?f = ( z7 + 2 z8 + z9 ) ? ( z1 + 2 z2 + z3 ) ? x ? f gy = = ( z3 + 2 z6 + z9 ) ? ( z1 + 2 z4 + z7 ) ? y Therefore, the mask could be: gx = M ( x, y ) ? ( z7 + 2 z8 + z9 ) ? ( z1 + 2 z2 + z3 ) + ( z3 + 2 z6 + z9 ) ? ( z1 + 2 z4 + z7 ) Roberts operators They are Sobel operators. Spring 2008 ELEN 4304/5365 DIP 29 Gradient method The coefficients in all masks shown sum to zero. This indicates that mask will give a zero response in an area of constant intensity as expected of a derivative operator operator. Original image of contact lens Sobel gradient Defect Spring 2008 ELEN 4304/5365 DIP 30 15 4/28/2008 Combining spatial enhancement techniques Frequently, Frequently a combination of several methods is used to enhance an image†¦ 1) Original image – 2) Laplacian – 3) image sharpened by Laplacian – 4) Sobel gradient of the original image – 5) Sobel image smoothed with a 5Ãâ€"5 averaging filter – 6) product of Sobel image with its smoothed version – 7) sharpened image (a sum of the original and 6) – 8) power-law transformation. Spring 2008 ELEN 4304/5365 DIP 31 Spring 2008 ELEN 4304/5365 DIP 32 16 How to cite Spatial Filtering Fundamentals, Papers

Fast Food Essay Example For Students

Fast Food Essay Imagine yourself behind the counter or in the drive- through window at McDonalds. You are programmed how to act and what to say. You have been working there for three years and earn a salary of $5.50 an hour. You have never exceeded 29 hours while working there. These circumstances are true for over 40 percent of six million people employed in restaurants today (Ritzer 59). The reason for these circumstances are due to the change in our society by which the consumer wants the biggest, fastest, and best product they can get for their money. This change in society can be attributed to a process known as McDonaldization. Although McDonaldization can be applied to many other parts of our society, this paper will focus on its impacts on the inequalities in the workplace, along with some theoretical discussions on the topic. My belief is that the process of McDonaldization, where the ideology of McDonalds has come to dominate the world, has caused many restaurants to emulate McDonalds styl e of running a franchised restaurant chain in terms of efficiency, calculability, predictability, and control (Ritzer 60). First, before I discuss the impact of Mcdonaldization on restaurants, I will define what McDonaldization is. McDonaldization is the process by which the principles of fast-food restaurants are coming to dominate more and more sectors of American society, as well as, of the rest of the world (Ritzer). George Ritzer created this concept of McDonaldization as a continuation of Max Webers theories on bureaucracies (Ritzer 61). Max Weber defines a bureaucracy as a goal-oriented organization designed according to rational principles in order to efficiently attain their goals.Its three main characteristics are that it has a division of labour, hierarchy of authority, and an impartial and impersonal application of rules and policies (www.faculty.rsu.edu/Theorists/Weber/Whome.htm). Thus, from that definition of a bureaucracy, one would conclude that McDonalds is a bureau cracy. The fact that it is bureaucracy is supported by the fact that each assigns workers to a specific job where each worker individually contributes to the overall success of the restaurant by doing his or her job. For example, McDonalds workers are assigned to work at the grill, register, or drive-through window. The restaurant also has ranks while on the job such as worker, shift manager, crew chief, and franchise owner. These ranks demonstrate the hierarchy of authority. Furthermore, the restaurant enforces the impartial and impersonal application of rules and policies. Through the eyes of C. Wright Mills and many other theorists, this bureaucratic demiurge causes alienation of its workers. It also creates powerless workers that follow the orders from the managers. Mills states that modern bureaucratic capitalism alienates its workers from both the process and the product of work (Wallace 107-108). The Frankfurt theorists also believe that alienation is the central issue when discussing the effects of bureaucratic capitalism on personality (Wallace 103). The workers in bureaucracies are denied such basic needs as creativity and identity. This causes their work to be entirely impersonal. They have no love for working; they just complete their work. Basically, they explain bureaucracies as dysfunctional and creativity blockers that deform human personalities. In the short video, Fast Food Women, the process of McDonaldization and the insights from Mills and the Frankfurt school can be clearly observed. The video illustrates women in Kentucky that work in various fast food restaurants. Even though their jobs all differ, these women are very similar. They are programmed workers that act the same, they get paid minimum wage, and they have no health benefits at all.Words/ Pages : 624 / 24