In What Ways Do We Inherit Our Ideas and Beliefs From Our Family and S Essay

In What Ways Do We Inherit Our Ideas and Beliefs From Our Family and Society - Essay Example From the family, the child learns to trust and grow, and family care leads to the development of the behaviours and beliefs of the child. If the environment provided by the family is nurturing and positive, the child grows up to be a successful and happy person. He takes on risk to learn new things as he knows he has the support of the family, and even if he fails, his family is there to support him. The negative support from the family can lead to a completely opposite effect. The example of family beliefs and its negative effect can be clearly seen in the literal work of William Faulkner and his short story â€Å"A Rose for Emily†. The story revolves around a daughter who is so distraught from the death of her father that she keeps his dead body to herself for a couple of days. She is so dependent on her father and secluded from the society that she refuses to let go of his body. Her dependency on father had led to her isolation from society, lack of a maternal figure and has disturbed relationships around. Another example of family and its emphasis on the lives of an individual is highlighted in the literal work of Shirley Jackson â€Å"The Lottery†, which shows how families can turn against one another on basis of a single trigger. Initially, it was highlighted in the story that family bonds were so strong that all families stood together and each family member has to be present. All the actions within the story are carried out due to the effect of family relationships. However, as soon as one of the members Tessie draws out a marked paper, all those family members turn against her and stone her. Another important factor that shapes the ideas and beliefs of an individual is the society, specifically the community. The sociological conditions have been affecting the individuals since the very beginning, however, each generation develops a new set of conditions. The present generation, for example, is socially ruled by the influence of technology, economy and media.  Ã‚  

EasyJet plc and British Airways Research Paper Example | Topics and Well Written Essays - 1500 words

EasyJet plc and British Airways - Research Paper Example esayJet plc is growing from strength to strength. easyJet plc's working capital position is very strong. BA's stability can be threatened, if there are sudden adverse circumstances like the 9/11. It could find itself unable to meet its current debts if the receivables become uncollectible. Gearing has improved in BA by 23.5pp. However, easyJet has resorted to borrowing to fund the capital expenditure. However, the key operating data in BA are encouraging and will appear to provide a firm footing to lift itself out of this prevailing troubled circumstances. Despite the current circumstances, BA is continuing to invest in operating assets to improve its value-added services, which it considers to be the backbone and introducing cost reduction schemes to minimize the financial burden arising from the magnitude of operating expenses. The improvements as seen on the operating data are welcome news to the Senior Management at BA. If the trend continues BA will be on its way to the financial recovery. easyJet plc Consolidated financial and operating data Year ended 30 September Change (unaudited) 2006 2005 % Key performance indicators Return on equity (1) 10.1% 7.1% 3.0pp Profit before tax per seat () (2) 3.32 2.38 39.6 Revenue per seat () (3) 41.66 38.66 7.8 Cost per seat () (4) 38.34 36.28 5.7 Cost per seat excluding fuel () (5) 28.36 28.78 (1.5) Seats flown (millions) (6) 38.9 34.7 12.1 (http://academia-research.com/files/message/603169_EasyJet%20annual_report_2006.pdf) British Airways 2005-06 2004-05 Group results Revenue m up 9.6% 8,515 7,772 Operating profit m up 26.8% 705 556 Profit before tax m up 20.9% 620 513 Attributable profit for the year m up 19.6% 451 377 Net assets m up 48.5%...It could find itself unable to meet its current debts if the receivables become uncollectible. Despite the current circumstances, BA is continuing to invest in operating assets to improve its value-added services, which it considers to be the backbone and introducing cost reduction schemes to minimize the financial burden arising from the magnitude of operating expenses. The Core competence of easyJet plc is the winning combination of 'low cost with care and convenience'. Low cost is by far the most important component. Convenience is not only about their award winning website but also the network of centrally located airports, which give their customers quick access to where they want to go. The Core competence of BA is in being an Internet age airline with online systems and features that make the travel experience simple and hassle free. Providing superb customer service and products, which their customers want is at the heart of our business. The main activities of the Company and its subsidiaries are the operation of international and domestic scheduled and charter air services for the carriage of passengers, freight and mail and the provision of ancillary services. The key achievements of easyJet plc have been given prominence, cleverly projecting and differentiating itself and gaining mileage in image building.

The Pros and Cons of Steel Construction to Firefighters Essay

The Pros and Cons of Steel Construction to Firefighters - Essay Example The advantages of steel construction are vast. First, steel is much lighter than other construction materials like wood, brick, and concrete. Second, it is resilient, it has an excellent strength to weight ratio, meaning it is able to support heavy weights with a low mass proportion. Third, it is malleable and is thus available in various shapes and sizes making it more efficient. Finally, it is cost effective. Its alloy nature is a combination of various elements that would have been expensive individually (Connell, 2005 pg 252). These advantages make firefighting easier for that; its light property makes it easier for firemen to move through rubble and save property as well as lives. In other buildings such as those constructed of concrete, maneuvering through the rubble is difficult for firemen making it hard for them to do their jobs. Also, steel is highly flame retardant. This makes it easier to put out fires and it less costly for fire departments. The main disadvantage of steel construction is that steel is an excellent conductor of heat and has a poor ability to resist fire (Connell, 2005 pg 251). In the event that the architect did not put in place specific measures for the particular building, the occurrence of a fire or high temperatures causes building disintegration. The improper placing of steel beams, joists and studs would cause a building to fall immediately in catastrophic events. These poor connections are what make steel construction hazardous to firemen. The high probability of steel to disintegrate makes it difficult for firemen to perform their duties during fires as there are chances of the building collapsing on them. Therefore extra precautions are required which may take time. Generally, steel construction makes firefighting easier.  

EasyJet plc and British Airways Research Paper Example | Topics and Well Written Essays - 1500 words

EasyJet plc and British Airways - Research Paper Example esayJet plc is growing from strength to strength. easyJet plc's working capital position is very strong. BA's stability can be threatened, if there are sudden adverse circumstances like the 9/11. It could find itself unable to meet its current debts if the receivables become uncollectible. Gearing has improved in BA by 23.5pp. However, easyJet has resorted to borrowing to fund the capital expenditure. However, the key operating data in BA are encouraging and will appear to provide a firm footing to lift itself out of this prevailing troubled circumstances. Despite the current circumstances, BA is continuing to invest in operating assets to improve its value-added services, which it considers to be the backbone and introducing cost reduction schemes to minimize the financial burden arising from the magnitude of operating expenses. The improvements as seen on the operating data are welcome news to the Senior Management at BA. If the trend continues BA will be on its way to the financial recovery. easyJet plc Consolidated financial and operating data Year ended 30 September Change (unaudited) 2006 2005 % Key performance indicators Return on equity (1) 10.1% 7.1% 3.0pp Profit before tax per seat () (2) 3.32 2.38 39.6 Revenue per seat () (3) 41.66 38.66 7.8 Cost per seat () (4) 38.34 36.28 5.7 Cost per seat excluding fuel () (5) 28.36 28.78 (1.5) Seats flown (millions) (6) 38.9 34.7 12.1 (http://academia-research.com/files/message/603169_EasyJet%20annual_report_2006.pdf) British Airways 2005-06 2004-05 Group results Revenue m up 9.6% 8,515 7,772 Operating profit m up 26.8% 705 556 Profit before tax m up 20.9% 620 513 Attributable profit for the year m up 19.6% 451 377 Net assets m up 48.5%...It could find itself unable to meet its current debts if the receivables become uncollectible. Despite the current circumstances, BA is continuing to invest in operating assets to improve its value-added services, which it considers to be the backbone and introducing cost reduction schemes to minimize the financial burden arising from the magnitude of operating expenses. The Core competence of easyJet plc is the winning combination of 'low cost with care and convenience'. Low cost is by far the most important component. Convenience is not only about their award winning website but also the network of centrally located airports, which give their customers quick access to where they want to go. The Core competence of BA is in being an Internet age airline with online systems and features that make the travel experience simple and hassle free. Providing superb customer service and products, which their customers want is at the heart of our business. The main activities of the Company and its subsidiaries are the operation of international and domestic scheduled and charter air services for the carriage of passengers, freight and mail and the provision of ancillary services. The key achievements of easyJet plc have been given prominence, cleverly projecting and differentiating itself and gaining mileage in image building.

Fashion Photography and it's affects on young women Research Paper

Fashion Photography and it's affects on young women - Research Paper Example The essay "Fashion Photography and it's affects on young women" focuses on the influence of fashion photography on the women. The manner in which fashion photography has begun to distract the attention of the young women from different priorities in their lives leaves a very bad taste in the mouth, especially when it is being debated upon. They often emphasize more on what the celebrities are doing and what kind of new photographs are coming out in the open for them. They are reliant on fashion for looking after their own requirements, which are insignificant if seen properly. They perceive true beauty to be hidden within fashion photography which is simply untrue. They would rather emphasize on how they could derive value from fashion than looking at the reality aspects. The negative effects of fashion photography are important to understand here. This is because the young women start believing that fashion photography is for real and that they must follow the celebrities and stars into becoming somewhat similar. They are of the view that the world of media and fashion is filled with glamour which is not anywhere close to reality. This element essentially makes them feel that they must do something collectively to welcome what is being shown to them through the different media outlets and domains. As far as their self image and self esteem issues are concerned, these young women need to understand where they are going wrong. Since self image is something that shapes up one’s own thinking.

Detecting Complex Image Data Using Data Mining Techniques

Detecting Complex Image Data Using Data Mining Techniques Detecting complex image data using data mining techniques IMRAN KHAN ABSTRACT The Internet, computer networks and information are vital resources of current information trend and their protection has increased in importance in current existence. The intrusion detection system (IDS) plays a vital role to monitors vulnerabilities in network and generates alerts when found attacks. Today the educational network services increasing day today so that IDS becomes essential for security on internet. The Intrusion data classification and detection process is very complex process in network security. In current network security scenario various types of Intrusion attack are available some are known attack and some are unknown attack. The attack of know Intrusion detection used some well know technique such as signature based technique and rule based technique. In case of unknown Intrusion attack of attack detection is various challenging task. In current trend of Intrusion detection used some data mining technique such as classification and clustering. The process of c lassification improves the process of detection of Intrusion. In this dissertation used graph based technique for Intrusion classification and detection. This dissertation proposes efficient intrusion detection architecture which named IDS using improved ensemble techniques (IDSIET). The IDSIET contains a new improved algorithm of attribute reduction which combines rough set theory and a method of establishing multiple rough classifications and a process of identifying intrusion data. The experimental results illustrate the effectiveness of proposed architecture. Our proposed work is implemented in MATLAB .for implementation purpose write various function and script file for implementation of our proposed architecture. For the test of our hybrid method, we used DARPA KDDCUP99 dataset. This data set is basically set of network intrusion and host intrusion data. This data provided by UCI machine learning website. Proposed method compare with exiting ensemble techniques and generate the improved ensemble technique to getting better result such as detection rate, precision and recall value. Keywords- Intrusion Detection System (IDS), IDSIET, Neural Network, rough set theory, Network Security, MATALAB, KDDCUP99 Dataset. PROPOSED METHODOLOGY AND ARCHITECTURE Comparison with linear scale-space representation While not being used explicitly in SURF, we take interest here in the approximation of Gaussian kernels by box filters to understand the advantages and the limitations of the SURF approach. 3.1 Scale-space representation linear scale space The linear scale-space representation of a real valued image u : R2 7→ R defined on a continuous domain is obtained by a convolution with the Gaussian kernel uÏÆ' := GÏÆ' âˆâ€"u (1) where GÏÆ' is the centered, isotropic and separable 2-D Gaussian kernel with variance ÏÆ'2 ∀(x,y) ∈R2, GÏÆ'(x,y) := 1 2Ï€ÏÆ'2 e−x2+y2 2ÏÆ'2 = gÏÆ'(x)gÏÆ'(y) and gÏÆ'(x) = 1 √2π ·ÃÆ'e− x2 2ÏÆ'2 . (2) The variable ÏÆ' is usually referred to as the scale parameter. Discrete scale space In practice, for the processing of a numerical image u, this continuous filter is approximated using regular sampling, truncation and normalization: ∀i,j ∈J−K,KK GÏÆ'(i,j) = 1 CK GÏÆ'(i,j) , where CK = K Xi,j =−K GÏÆ'(i,j). (3) The scale variable ÏÆ' is also sampled, generally using a power law, as discussed later in  § 3.2. Discrete box space Making use of the aforementioned box filter technique, such a multi-scale representation can be (very roughly) approximated using a box filter with square domain Γ = J−Î ³,ÃŽ ³KÃâ€"J−Î ³,ÃŽ ³K uÃŽ ³ := 1 (2ÃŽ ³ + 1)2 BΓ âˆâ€"u. (4) The question now is how to set the parameter ÃŽ ³ ∈ N to get the best approximation of Gaussian zoom-out. Second moment comparison One may for instance choose to match the second order moment ÏÆ'2 of the 1D Gaussian gÏÆ' and the variance of the corresponding box filter, as suggested by [7]. This leads to the relation ÏÆ'2 ÃŽ ³ = ÃŽ ³ Xi =−Î ³ i2 2ÃŽ ³ + 1 = (2ÃŽ ³ + 1)2 −1 12 = ÃŽ ³(ÃŽ ³ + 1) 3 , (5) where ÏÆ'2 ÃŽ ³ is the variance of the centered 1D box filter with width 2ÃŽ ³ + 1. Thus, for large values of filter size (ÃŽ ³ 1), we get approximately ÏÆ'ÃŽ ³ ≈ ÃŽ ³ √3 ≈ 0.58ÃŽ ³. Since ÃŽ ³ ∈ N takes integer values, ÏÆ'ÃŽ ³and ÏÆ' cannot match exactly in general. Moreover, due to the anisotropy of the box filter in 2D, it is impossible to match the covariance matrices. SURF scale parameter analogy Note that box filters are only used to approximate first and second order of Gaussian derivatives in SURF algorithm, and not to approximate Gaussian filtering like in [7]. However, when considering the approximation of second order Gaussian derivative Dxx GÏÆ'(x,y) = Dxx gÏÆ'(x)Ãâ€"gÏÆ'(y) = 1 ÏÆ'22 ÏÆ'2 −1gÏÆ'(x)Ãâ€"gÏÆ'(y) By these condition order box filter operator DLxx, we can see that the1D Gaussian filter gÏÆ'(y) is approximated by 1D box filter with parameter ÃŽ ³ = L−1 2. The authors of SURF claim that the corresponding Gaussian scale is ÏÆ' = 1.2 3 L ≈ 0.8ÃŽ ³for ÃŽ ³ 1, which is close but dià ¯Ã‚ ¬Ã¢â€š ¬erent to the value given by Formula (5): ÏÆ'ÃŽ ³ ≈ 0.58ÃŽ ³. Other analogies could have been made for scale variables, for instance by considering zero crossing of second order derivative of Gaussians, second moment of Gaussian derivatives, mean-square error minimization, but each one provides dià ¯Ã‚ ¬Ã¢â€š ¬erent relations. In conclusion, defining a relation between the box parameters (L and `(L)) and the Gaussian scale variable ÏÆ' seems quite arbitrary. Visual comparison Figure 8 illustrates the dià ¯Ã‚ ¬Ã¢â€š ¬erence between the linear scale-space representation obtained by Gaussian filtering and the box-space, that is its approximation by box-filters when using relation (5). While being roughly similar, the approximated scale-space exhibits some strong vertical and horizontal artifacts due to the anisotropy and the high frequencies of the box kernels. Again, while it is not being used explicitly in SURF, these artifacts may explain some of the spurious detections of the SURF approach that will be exhibited later on. 3.2 Box-space sampling Because of the dentition of first and second order box filters, the size parameter L cannot be chosen arbitrarily. The sampling values and the corresponding variables used to mimic the linear scale space analysis. The following paragraphs give more detailed explanations. Octave decomposition Alike most multi-scale decomposition approaches (see e.g. [13, 15]), the box-space discretization in SURF relies on dyadic sampling of the scale parameter L. The box length representation is therefore divided into octaves (similarly to SIFT [14, 13]), which are indexed by parameter o ∈{1,2,3,4}, where a new octave is created for every doubling of the kernel size. Note that, in order to save computation time, the filtered image is generally sub-sampled of factor two at every octave, as done for instance by SIFT [14]. As pointed out by the author of SURF [2], sub-sampling is not necessary with the use of box filters, since the computation time complexity does not depends on scale. However, while not being explicitly stated in the original paper [2], but as done in most implementations we have reviewed (for instance, this approximation is used in [3] but not in [5]), we choose to use sub-sampling to speed up the algorithm. More precisely, instead of evaluating the multi-scale operators at each pixel, we use a sampling†step† which depends on the octave level (this sampling is detailed in the next sections). Note that this strategy is consistent with the fact that the number of features is decreasing with respect to scale. Level sampling Each octave is also divided in several levels (indexed here by the parameter i ∈ {1,2,3,4}). In the usual discrete scale space analysis, these levels correspond directly to the desired sampling of the scale variable ÏÆ', which parametrizes the discretized Gaussian kernels GÏÆ' (see definition in Eq. (16)). In SURF, the relation between scale L, octave o and level i variables is L := 2o i + 1 . (6) These values are summarized in Table 2. Note that because of the non-maxima suppression involved in the feature selection, only intermediate levels are actually used to define interest points and local descriptors (i ∈{2,3}). On comparison of the box space and the linear scale space. (Top) Convolution with squared and centered box filters with radii ÃŽ ³ = 5 and ÃŽ ³ = 20 (respectively from left to right). (Middle) Corresponding Gaussian filters with respective scales ÏÆ'5 ≈ 3.16 and ÏÆ'20 ≈ 11.83, according to formula (5). Dià ¯Ã‚ ¬Ã¢â€š ¬erence between Gaussian and Box filters (using a linear transform for visualization). We can see here that the box space is a rough approximation of the Gaussian scale space, that exhibits some artifacts due to the anisotropy and the high frequencies of the box kernels. Scale analogy with linear scale space As discussed before in Section 3.1, we can define a scale analysis variable by analogy with the linear scale space decomposition. In [2], the scale parameter ÏÆ'(L) associated with octave o and level i is obtained by the following relation ÏÆ'(L) := 1.2 3(2o Ãâ€"i + 1) = 0.4L. (7) Since the relation between the scale ÏÆ'(L) of an interest point is linear in the size parameter L of box filters operators, we shall speak indià ¯Ã‚ ¬Ã¢â€š ¬erently of the former or the latter to indicate the scale. Remark A finer scale-space representation could be obtained (i.e. with sub-pixel values of L) using a bilinear interpolation of the image, as suggested in [2]. This is not performed in the proposed implementation. 3.3 Comparison with Gaussian derivative operators 3.3.1 First order operators The first order box filters DL x and DL y defined at scale L are approximations of the first derivatives of Gaussian kernel at the corresponding scale ÏÆ'(L) (see Eq. (7)), respectively corresponding to Dx GÏÆ'(x,y) = − x ÏÆ'2(L) GÏÆ'(x,y) and Dy GÏÆ'(x,y). These operators are used for local feature description, in detailed we compares the first order box filter impulse response with the discretized Gaussian derivative kernel. DL x ÃŽ ´ (Eq. (6)) Dx GÏÆ'(L) Illustration of the discrete derivative operator DL x (defined in Section 2.3.1) and discretization of the Gaussian derivative kernel Dx GÏÆ'(L) when using scale relation ÏÆ'(L) from Eq. (7). 3.3.2 The second order operators Second order dià ¯Ã‚ ¬Ã¢â€š ¬erential operators are computed in the scale-space for the detection of interest points [9, 10]. In the linear scale-space representation, this boils down to the convolution with second derivatives of Gaussian kernels Dxx GÏÆ'(x,y) = 1 ÏÆ'22 ÏÆ'2 −1GÏÆ'(x,y), Dyy GÏÆ', and Dxy GÏÆ'(x,y) = xy ÏÆ'4 GÏÆ'(x,y). (8) In the SURF approach, the convolution with theses kernels are approximated by second order box filters, previously introduced respectively as DL xx, DL yy , and DL xy . A visual comparison between second order derivatives of Gaussian and their analogous with box filters. These operators are required for local feature selection step in section 4. 3.3.3 Scale Normalization According to [12], dià ¯Ã‚ ¬Ã¢â€š ¬erential operators have to be normalized when applied in linear scale space in order to achieve scale invariance detection of local features. More precisely, as it can be seen from Equation (21), the amplitude of the continuous second order Gaussian derivative filters decreases with scale variable ÏÆ' by a factor 1 ÏÆ'2. To balance this eà ¯Ã‚ ¬Ã¢â€š ¬ect, second order operators are usually normalized by ÏÆ'2, so that we get for instance (a) (b) (c) (d) On comparison of second order box filters and second order derivative of Gaussian kernels. (a) operator DL yy; (b) discretizedsecondorderGaussianderivative D2 y GÏÆ'; (c) operator DL xy; (d) discretized second order Gaussian derivative Dxy GÏÆ'; For comparison purpose, we used again the scale relation ÏÆ'(L) from Eq. (7). †¢ the scale-normalized determinant of Hessian operator: DoHÏÆ' (u) :=uÏÆ' −(Dxy uÏÆ')2; (9) †¢ the scale-normalized Laplacian operator: à ¢Ã‹â€ Ã¢â‚¬  ÃÆ' u := ÏÆ'2à ¢Ã‹â€ Ã¢â‚¬   uÏÆ' = ÏÆ'2à ¢Ã‹â€ Ã¢â‚¬   GÏÆ' âˆâ€"u = ÏÆ'2(Dxx + Dyy)GÏÆ' âˆâ€"u = ÏÆ'2(Dxx uÏÆ' + Dyy uÏÆ'), (10) where à ¢Ã‹â€ Ã¢â‚¬  ÃÆ' GÏÆ'(x,y) = ÏÆ'2(Dxx +Dyy)à ¢- ¦GÏÆ'(x,y) =x2+y2 ÏÆ'2 −1GÏÆ'(x,y) is the multi-scale Laplacian of Gaussian. Observe that this operator can be obtained from the Trace of the scalenormalized Hessian matrix. These two operators are widely used in computer vision for feature detection. They are also approximatedinSURF,asdetailedinthenextsections. Asaconsequence, suchascale-normalization is also required with box filters to achieve similar invariance in SURF. To do so, the authors proposed that amplitude of operators DL xx , DL yy , and DL xy should be reweighted so that the l2 norms of normalized operators become constant over scales. The quadratic l2 norm of operators are estimated from the squared Frobenius norm of impulse responses kDL xxk2 2 := kDL xx ÃŽ ´k2 F = kDL yy ÃŽ ´k2 F =1 + 1 + (−1)2L(2L−1) = 6L(2L−1), so that kDL xxk2 2 ≈ 12L2 when L=1, and kDL xyk2 2 := kDL xy ÃŽ ´k2 F =1 + 1 + (−1)2 + (−1)2LÃâ€"L = 4L2. This means that box filters responses should be simply divided by the scale parameter L to achieve scale invariance detection. Interest point detection: In the previous sections, second order operators based on box filters have been introduced. These operators are multi-scale and may be normalized to yield scale invariant response. We will now take interest in their use for multi-scale local feature detection. Once the integral image has been computed, three consecutive steps are performed which are detailed in the following sections: 1. Feature filtering based on a combination of second order box filters; 2. Feature selection is combining non-maxima suppression and thresholding; 3. Scale-space location refinement ( § 4.3) using second order interpolation. This interest point detection task is summarized in Algorithm 1. Step-1 Filtering Image by Integration: Integral image and box filters Let u be the processed digital image defined over the pixel grid à ¢Ã¢â‚¬Å¾Ã‚ ¦ = [0,N-1]Ãâ€"[0.M-1], where M and N are positive integers. In the following, we only consider quantized gray valued images (taking values in the range [0; 255]), which is the simplest way to achieve robustness to color modifications, such as a white balance correction. The integral image of I for(x,y) à Ã¢â‚¬Å¾ à ¢Ã¢â‚¬Å¾Ã‚ ¦ is Flow Diagram: Figure3.1: showing the flow chart of the process for object detection Step 2: Point Detection: During the detection step, the local maxima in the box-space of the determinant of Hessian† operator are used to select interest point candidates. These candidates are then validated if the response is above a given threshold. Both the scale and location of these candidates are then refined using quadratic fitting. Typically, a few hundred interest points are detected in a megapixel image. input: image u, integral image U, octave o, level i output: DoHL(u) function Determinant_of_Hessian (U; o; i) L 2oi + 1 (Scale variable, Eq. (19)) for x := 0 to M à ´Ã¢â€š ¬Ã¢â€š ¬Ã¢â€š ¬ 1, step 2oà ´Ã¢â€š ¬Ã¢â€š ¬Ã¢â€š ¬1 do (Loop on columns) for y := 0 to N à ´Ã¢â€š ¬Ã¢â€š ¬Ã¢â€š ¬ 1, step 2oà ´Ã¢â€š ¬Ã¢â€š ¬Ã¢â€š ¬1 do (Loop on rows) DoHL(u)(x; y) Formula (24) (with (4), (10) and (11)) end for end for return DoHL(u) end function Algo input: image u output: listKeyPoints (Initialization) U IntegralImage(u) (Eq. (1)) (Step 1: filtering of features) for L 2 f3; 5; 7; 9; 13; 17; 25; 33; 49; 65g do (scale sampling) DoHL(u) Determinant_of_Hessian (U; L) end for (Step 2: selection and refinement of keypoints) for o := 1 to 4 do (octave sampling) for i := 2 to 3 do (levels sampling for maxima location) L -> 2o i + 1 listKeyPoints -> listKeyPoints + KeyPoints(o; i;DoHL(u)) end for end for return listKeyPoints So that the scale normalization factor C(L) for second order box filters should be proportional to 1 L2 However, the previous normalization is only true when L1. Indeed, while we have kDxxGÏÆ'k2 2 kDxyGÏÆ'k2 2 = 3 at any scale ÏÆ', this is not exactly true with box filters, where: kDL xxk2 2 kDL xyk2 2 = 3(2L−1) 2L ≈ 3 when L1. To account for this dià ¯Ã‚ ¬Ã¢â€š ¬erence in normalization for small scales, while keeping the same (fast) un-normalized box filters, the author of SURF introduced in Formula (24) a weight factor: w(L) = kDL xxk2 kDL xyk2  ·kDxyGÏÆ'k2 kDxxGÏÆ'k2 =r2L−1 2L . (26) The numerical values of this parameter are listed in the last column of Table 2. As noticed by the authors of SURF, the variable w(L) does not vary so much across scales. This is the resaon why the weighting parameter w in Eq. (10) is fixed to w(3) = 0.9129. Feature selection: In our methodology, interest points are defined as local maxima of the aforementioned DoHL operator applied to the image u. These maxima are detected by considering a 3 Ãâ€" 3 Ãâ€" 3 neighborhood, andperforminganexhaustivecomparisonofeveryvoxelofthediscretebox-spacewith its 26 nearest-neighbors. The corresponding feature selection procedure is described in Algorithm 3. Algorithm 3 Selection of features input: o,i,DoHL(u) (Determinant of Hessian response at octave o and level i) output: listKeyPoints (List of keypoints in box space with sub-pixel coordinates (x,y,L)) function KeyPoints (o,i,DoHL(u)) L ↠ 2oi + 1 for x := 0 to M −1, step 2o−1 do (Loop on columns) for y = 0 to N −1, step 2o−1 do (Loop on rows) if DoHL(u)(x,y) > tH then (Thresholding) if isMaximum (DoHL(u),x,y) then (Non-maximum suppression) if isRefined (DoHL(u),x,y,L) then addListKeyPoints (x,y,L) end if end if end if end for end for return listKeyPoints end function Remark A faster method has been proposed in [21] to find the local maxima without exhaustive search, which has been not implemented for the demo. Thresholding: Using four octaves and two levels for analysis, eight dià ¯Ã‚ ¬Ã¢â€š ¬erent scales are therefore analyzed (see Table 2 in Section 3.2). In order to obtain a compact representation of the image -and also to cope with noise perturbation- the algorithm selects the most salient features from this set of local maxima. This is achieved by using a threshold tH on the response of the DoHL operator DoHL(u)(x,y) > tH . (27) Note that, since the operator is scale-normalized, the threshold is constant. In the demo, this threshold has been set to 10 assuming that the input image u takes values in the intervalJ0,255K. This setting enables us to have a performance similar to the original SURF algorithm [2, 1] (see Section 6 for more details). Figure 13 shows the set of interest points detected as local box-space maxima of the DoHL operator, and selected after thresholding. For visualization purpose, the radii of the circles is set as 2.5 times the box scale L of the corresponding interest points.

Narsapur vs. America :: Feminism Feminist Essays

Narsapur vs. America This Women’s Studies Senior Seminar class has provided the opportunity to read about many cross-cultural issues pertaining to women. In the article, â€Å"Women Workers and Capitalist Scripts: Ideologies of Domination, Common Interests, and the Politics of Solidarity† by Chandra Talpade Mohanty, issues of â€Å"poor women worker in the global capitalist arena† (3) are addressed. Mohanty focuses on the plight of exploited, poor Third-World women. She illuminates specific issues that relate to the transformation of developing countries to capitalism. Mohanty’s article is split up into sections, the section that I want to focus on in order to compare key issues between Narsapur and America is called â€Å"Housewives and Homework: The Lacemakers of Narsapur." In this specific article Mohanty illuminates the effects that capitalism has on areas that are being developed, she portrays its effects on women as well as men. In Narsapur the lace making industry skyrocketed between the years 1970 and 1978. As a result of the increased demand, the process of making lace and the final product, which is lace, has been feminized while the trade or exportation of the lace is viewed as business, as a masculinize activity. Women working outside the home in this culture are defined as housewives, hence the job of being a lacemaker is defined as housework. Mohanty argues that the â€Å"definition of women as housewives also suggests the heterosexualization of women’s work - women are always defined in relation to men and conjugal marriage† (12). As a result of the heterosexualization of women’s work plus the feminization of the process and product and the masculinization of the trade â€Å"men sell women’s products and live on profits from women’s labor† (12). I think there are similarities between the hegemony in Narsapur and in the United States. Our society’s practices and treatment towards women’s work and the treatment of women’s work in Naraspur can be compared. One comparison in the U.S. is the treatment of women’s work outside of the job force. By sheer lack of acknowledgement, women’s work inside the home is overlooked and hence not considered to be work at all. Work that receives no recognition is invisible and invisibility of work carries with it no economic power. American women are still perceived as primarily being housewives first, then they are doctors or lawyers or you can fill in the blank.