Xxxxxx Operator Sample Clauses

Xxxxxx Operator. E Xxxxxxxxxx ............................................................................................................. Reclaim Operator II.. Sweeper I E Switch Engine Operator .............................................. . Annex Helper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assistant Operations Supervisor’s I Helper Pool . . . . . . . . . . . . . ..“. F Bin Floor Worker II F Cleaner IV F Oiler F Pellet Plant Helper F Scale Floor Helper F Switch Engine Operator Assistant ..................................................................... F Tunnel Worker F Winch Operator F Electrician VI G Labourer G Millwright G 4 Months After Placement 8 Months After Placement (See Schedule criteria for Millwright VI and Electrician VI) Starting Rate Months After Hire Rates could change during year, NOTE: Maintenance Assistant upon effect of application of Article Only) Paid at the Unit Rate. Control Operator A Electrician I A Grain Inspector I A Xxxxxxxxxx I A S Control Operator II. B Electrician II B Millwright II B Weigh I B Cleaner Operator I C Electrician III C Grain Inspector II C Millwright III C Xxxxxxxxxx XXX (Hydraulic Mechanic) C Pellet Plant Operator I C Track Shed Worker II C Weigh Operator II C : : . : : : : : : : : : : : : : : : :. : : :. :: :. :: :. :: :. :: .: : :: : ::. ::: . : : : : : :: :

Xxxxxx Operator. The Harris­Stephens operator [74] (often simply called the Xxxxxx operator) locates informative image structure (corner type areas), within an image. This operator has been shown to be robust, and can return highly repeatable image sections [33]. The idea of repeatability is an important one, as only segments, which appear in numerous images of an object class, should be used in the matching process. The Xxxxxx operator functions by analysing the auto­correlation or second moment matrix of a set region, in the following way: Representing the spatial image gradient as [ Ex , Ey ]T , where Ex is created through partial differentiation, such that E = ∂ E x ∂ x . A matrix C, which is the auto­correlation matrix of the region Q, can be formed using the formula: [ ∑ E 2 ∑ E E ] ∑ Ex Ey ∑ E 2 (5.9) Q may be of any size, but is often between 5 and 21 pixels per side [32]. To find areas of interest using the Xxxxxx operator, the Eigenvalues of the matrix C are then found. Once obtained, these values can be used to locate corner points. If a corner is present, the lowest eigenvalue should be above a pre­defined threshold. In this situation, the eigenvalues represent the strength of the corner detected, whilst the eigenvectors describe the direction of the corner found. As the direction of the corners found are not usually of interest, only the eigenvalues need be monitored. It is important that we here introduce the idea of using multi scale Gaussians in conjunction with the second moment matrix used in corner point detection, as this idea will be used in later sections when scale and affine invariance is added to the basic detector. The use of multi scale Gaussians in this way is an important idea, as this allows for the extraction of different size structure from an image, without spurious readings. This is achieved by blurring images using the Gaussian blur, while increasing the size of the corner region (Q) under scrutiny. The blurring effectively removes the smaller corner features, relative to the corner region size, thereby suppressing smaller corners in larger regions and extracting only the larger features. In smaller windows, the Gaussian blur is reduced and smaller corner features are available for extraction. In this case, two scales, the derivation scale σ d and the integration scale σ i , define the second moment matrix. In the case of an infinite Gaussian window centred on point X, [33] shows the formula for computing the second moment matrix is given...