text: Chapter 1 Stitching IC Images Michael Baumann1 , Chris Bose2 , Lorraine Dame2 , Isabelle Dechene3 , Maria Inez Goncalves2 , Robert Israel1 , Edward Keyes4 (presenter), Frithjof Lutscher5 , David Lyder6 , Colin Macdonald7 , Jenny McNulty8 , Joy Morris9 , Nancy Ann Neudauer10 , Benjamin Ong7 , Robert Pich´11 (editor), e ˇ 12 13 14 15 Ar¯ nas Salkauskas , Karen Seyffarth , Brett Stevens , Tzvetalin Vassilev , Brian Wetton1 , u Fabien Youbissi16 1.1 Introduction Errors in stitching (or mosaicing) of integrated circuit (IC) images cause errors in the automated generation of the schematic. This increases costs and introduces delays as engineers must step in with interactive computer tools to correct the stitching. Our goal was to develop automated image stitching methods that keep stitching errors under F/2, where the minimum feature size F is around 10 pixels. Although image stitching software is used in many areas such as photogrammetry, biomedical imaging, and even amateur digital photography, these algorithms require relatively large image overlap. For this reason they cannot be used to stitch the IC images, whose overlap is typically less than 60 pixels for a 4096 by 4096 pixel image. 1 University of British Columbia 2 University of Victoria 3 McGill University 4 Semiconductor Insights Inc. 5 University of Alberta 6 King's University College 7 Simon Fraser University 8 University of Montana 9 University of Lethbridge 10 Pacific University 11 Tampere University of Technology 12 Sorex Software Inc. 13 University of Calgary 14 Carleton University 15 University of Saskatchewan 16 Laval University 1 2 CHAPTER 1. STITCHING IC IMAGES IC images are currently stitched as follows. First, the offset between each image and its neighbours is determined by minimising an error function that measures the difference between features in overlapping image pairs. These offsets are then used to stitch the images together in some predetermined pattern. Errors tend to accumulate as the images are added sequentially to the grid. In Section 1.2 we use algorithmic graph theory to study optimal patterns for adding the images one at a time to the grid. In the remaining sections we study ways of stitching all the images simultaneously using different optimisation approaches: least squares methods in Section 1.3, simulated annealing in Section 1.4, and nonlinear programming in Section 1.5. 1.2 Stitching using Graph Theory Here we present a graph theoretic approach to the problem. This approach is purely local in the sense that we only use the fact that any given pair of adjacent images can be stitched together perfectly, i.e., all features match, due to the offset data from pairwise correlation. At present, these local correlations are used for stitching following a certain pattern. We propose different patterns which should give better global results. To that end, we formulate the problem in a graph theoretic way and introduce some error measures. We find and compare several alternative stitching patterns which have smaller errors than the one currently used. Finally, we extend the approach to include information about the reliability of the pairwise correlation data. 1.2.1 Notation We assume the images are arranged in a rectangular shape of m rows and n columns, where m ≥ n. Define the graph G by letting each image correspond to a vertex. Two vertices are joined by an edge if the corresponding images are adjacent. Hence, G is an m × n grid graph. We denote by E(G) the edges of G, and for adjacent u, v ∈ G we write uv ∈ E(G). We use the stitching pattern to define a spanning tree T of G, i.e., a connected subgraph without cycles. An edge of G is an edge of T if the corresponding images are stitched together perfectly according to the offset values. The current method of row-wise stitching corresponds to the row pattern given by the solid lines in Figure 1.1. Here, the images are stitched together row-wise and then the strips are stitched together along a "spine" We assume that the error between u and v with respect to the true offset values increases with the distance between u and v in the tree T . To quantify this error, we define the following. For each uv ∈ E(G), dT (u, v) denotes the distance between u and v in the tree T ; dT (u, v) = dT (u, v) − 1; S(T ) = Σuv∈E(G) dT (u, v) (i.e., the sum of all the distances, in T , between adjacent vertices of G); M (T ) = max dT (u, v). uv∈E(G) π 1.2. STITCHING USING GRAPH THEORY 3 Figure 1.1: 14 × 8 Row Pattern, R Note that we subtracted 1 from the distance in the tree to get dT because we assume that pairwise stitching is free of error. We address the following optimization problems. 1. Find a spanning tree T of G that minimizes S(T ), the sum of the distances of the tree. 2. Find a spanning tree T of G that minimizes M (T ), the maximum of the distances of the tree. 3. Find a spanning tree T of G that minimizes S(T ), the sum of the distances of the tree, subject to M (T ) being as small as possible. We calculate the total distance and the maximum distance for the row pattern R (Figure 1.1) of an m×n grid. It is optimal to centre the "spine" in the long direction. In this case M (R) = 2 n 2 is as small as possible, and the total sum is given by: (m − 1)(n2 )/2 if n is even S(R) = . (m − 1)(n − 1)(n + 1)/2 if n is odd We note that this is the method currently employed. 1.2.2 Alternative Stitching Patterns While the row pattern minimizes the maximum distance it does not minimize the total distance. In fact, one can replace a single edge of the tree R with a nearby edge and decrease the total sum. A pattern that reduces the total sum is the Comb Pattern, shown in Figure 1.2. This pattern is similar to the row pattern in that it has a long "spine", but it differs in that instead of long "lines" attached to the spine, it has "combs". It is again optimal in terms of S(C) and M (C) to centre the "spine" in the longest direction, as above. Then the maximum is M (C) = 2 n + 4 > M (R), but S(C) < S(R). The values of S(C) depend on the modulus of 2 the parameters m and n, mod 3 and mod 2, respectively. Thus, there are six cases to consider; they are given in Table 1.1. π 4 CHAPTER 1. STITCHING IC IMAGES Figure 1.2: 14 × 8 Comb Pattern, C m mod 3 n odd n even 1 1 0 6 (m − 3)(n − 1)(n + 17) + 4(n − 1) 6 (m − 3)(n2 + 16n − 16) + 4(n − 1) 1 1 1 6 (m − 4)(n − 1)(n + 17) + 8(n − 1) 6 (m − 4)(n2 + 16n − 16) + 8(n − 1) 1 1 2 6 (m − 5)(n − 1)(n + 17) + 12(n − 1) 6 (m − 5)(n2 + 16n − 16) + 12(n − 1) Table 1.1: Values for S(C) The next method is to create a breadth-first search spanning tree which leads to what we call the Breadth Pattern, B. Let Ge be an n × n grid graph with n even and Go be an n × (n − 1) grid graph with n odd. Define an "almost square" grid graph to be a graph of the form Ge or Go . We first define the breadth patterns Be and Bo for these graphs. Label each vertex of the graph by the shortest distance to the perimeter of the grid. Figure 1.3 illustrates the 9 × 8 case. For the graph Ge , there are 6 vertices labeled (n − 3)/2 in the centre and for the graph Go , there are 4 vertices labeled (n − 2)/2 in the centre. Begin with a spanning tree of these centre vertices as indicated in Figure 1.3. 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 1 2 2 2 2 1 0 0 1 2 3 3 2 1 0 0 1 2 3 3 2 1 0 0 1 2 3 3 2 1 0 0 1 2 2 2 2 1 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 Figure 1.3: Breadth Pattern Construction The procedure for growing the tree begins in the centre and radiates outwards. Suppose the tree has been grown to include all vertices labeled at least j, where j > 0. Add vertices labeled π 1.2. STITCHING USING GRAPH THEORY 5 j − 1 as described below. • If a vertex x with label j − 1 is adjacent to a vertex y with label j, then y is unique. Add xy to the tree. • If a vertex x with label j − 1 is not adjacent to a vertex with label j, then x has two neighbors labeled j − 1. Arbitrary choose one of these, say y, and add xy to the tree. Figure 1.3 shows one possible breadth pattern for a 9 × 8 grid graph. We can easily calculate the sum of the distances for Bo and Be in the almost square cases. S(Bo ) = (n − 1)(n + 1)(2n − 3)/6, S(Be ) = n(n + 1)(n − 1)/3. The breadth pattern, B, for any m × n grid graph is formed by placing half of the breadth pattern for the almost square grid on the top and bottom, and a row pattern in the middle. See Figure 1.4 for an example of this construction. Figure 1.4: 14 × 8 Breadth Pattern, B The maximum distance M (B) = 2 n is minimal, while the total sum is smaller than the 2 row pattern sum. The values of S(B) for the breadth pattern B of an m × n grid are given below. (3mn2 − n3 − 2n)/6 if n is even, S(B) = 2 3 (3mn − n − 3m + n)/6 if n is odd. Comparing these patterns with the current row pattern, we see that the comb pattern im- proves the total sum at the expense of an increase in the maximum distance while the the breadth pattern improves the total sum while keeping the maximum distance at a minimum. We call a combination of these patterns a Combed–Breadth Pattern (Figure 1.5). π 6 CHAPTER 1. STITCHING IC IMAGES Figure 1.5: 14 × 8 Combed–Breadth Pattern, CB 1.2.3 Reliability Not all the pairwise optimal offset values have the same confidence levels. We incorporate uncertainties by not allowing adjacent images to be stitched together if the corresponding offset values are highly uncertain. This is implemented by disallowing certain edges from the spanning tree. This rule for excluding edges in the tree can be incorporated into breadth pattern. In Figure 1.6 the excluded edges are marked by grey barriers. Barriers were put where the uncertainty was higher than 7%. The tree is grown subject to the extra condition that it cannot grow across a barrier. We call this a Barred Pattern. One can also take the row pattern and the comb pattern and adjust them locally such that none of the edges of the spanning trees cross a barrier. Figure 1.6: Barred Pattern, BA In all cases, the numbers M and S cannot decrease. For this particular barred pattern, M = 10 for the modified row pattern and M = 12 for the modified comb and breadth patterns. π 1.2. STITCHING USING GRAPH THEORY 7 pattern S(·) M (·) R (row) 416 8 C (comb) 234 12 B (breadth) 360 8 CB (combed-breadth) 258 12 Table 1.2: S(·) and M (·) values for a 14 × 8 grid Also, the sum, S, increases by 4 for all three patterns. Table 1.2 contains calculations for Data set 1. In addition to the barred pattern in Figure 1.6 we also constructed a Greedy Pattern G, where we chose edges solely on the basis of confidence. The calculations for these patterns are also included in Table 1.2. 1.2.4 Data Table 1.2 gives the S(·) and M (·) values for a 14 × 8 grid for the four tree patterns that we have defined. Using the two sets of real data available, we evaluated the performance of each of these tree patterns. After stitching together the full image according to each pattern, we calculated the new pairwise relative offsets that each of these methods gave us. Then we calculated the differences both horizontally and vertically between these and the given "ideal" relative offsets. The larger a difference is, the more likely that it will create a significant misalignment that will lead to a problem in recreating the circuit. Thus we created three measures of the "badness" of each of our results. The first measure was a sum of the absolute values of all of the horizontal and vertical differences between the relative offsets we obtained and the ideal relative offsets. This is denoted ABS ERROR. The second measure was chosen to highlight the fact that if a particular pair of images is placed (relative to one another) very far from their ideal relative alignment, this is worse than having a lot of images that are pairwise only slightly off their ideal alignment. Here we took the sum of the squares of all of the horizontal and vertical differences. This is labelled SQ. ERROR. The third measure took the threshold value of plus or minus 5 pixels as the worst tolerable error, and counted the number of values (horizontal or vertical) that were more than 5 pixels from their ideal relative alignment. This is labeled BAD VALUES in Tables 1.3, 1.4. The barred and greedy patterns are present in Table 1.3 but not Table 1.4, since confidence data were only available for the first data set. From this data, it appears that the breadth pattern produces the best results, although the barred pattern may improve on it in some ways. Similar to the barred pattern, one can modify the row and comb patterns to disallow certain edges. It seems that these highly regular patterns are quite sensitive to the precise locations of the barriers. If one changes an arbitrarily chosen edge in the spanning tree and replaces it in an optimal way, the breadth pattern performs much better than the row and comb patterns. This may be in part because there are many choices for constructing such a breadth pattern. The greedy tree, although using high confidence values, resulted in extremely large values π 8 CHAPTER 1. STITCHING IC IMAGES pattern ABS ERROR SQ. ERROR BAD VALUES R 518 5092 32 C 513 5067 37 B 449 4229 30 CB 513 4897 39 BA 453 4309 27 G 721 7059 52 Table 1.3: Data Set 1 pattern ABS ERROR SQ. ERROR BAD VALUES R 569 6007 27 C 636 7560 34 B 517 5639 27 CB 619 7507 34 Table 1.4: Data Set 2 of M and S, and the effect of these is apparent in its poor performance. Of course, additional experimental data would be desirable, and it is likely that situations would arise in which differ- ent patterns provided the best performance, so one reasonable approach would be to maintain this as a small collection of trees to try on any given data set, and keep whichever final result is best. Some of these patterns could ultimately be discarded if additional experimental data showed them to consistently perform poorly. We note that the breadth pattern continually outperforms the row pattern which is currently being used. In addition, the barred pattern may prove to be useful at a higher tolerance. Currently, a tolerance of 10% is used; however we chose 7% rather arbitrarily based on the one data set available. We also note that while the comb pattern has the minimum value of the total distances S(C) it performs as well as the row pattern. Thus, it seems that the solution we seek will be the solution to optimization problem 3. Currently, the breadth pattern is the best candidate for a solution to that problem. 1.3 Least Squares The pairwise offsets overdetermine the positions of images in the final grid. In this section we develop stitching solutions by simultaneously minimising the sum of the squares of the offset discrepancies. 1.3.1 Offset Equations Let (Xi,j , Yi,j ) be the coordinates (in a global reference frame) for the northwest corner of the image in row i and column j (Figure 1.7). The grid of images has m rows and n columns. π 1.3. LEAST SQUARES 9 Figure 1.7: Coordinates and labelling of images in the grid. In least squares stitching the images can be interpreted as rigid plates connected by springs. x The x-component offsets Ri,j between adjacent images in a row are described by the equations x Xi,j+1 − Xi,j = Ri,j (i = 1 : m, j = 1 : n − 1) These m(n − 1) row stitching equations can be written compactly as T XBn = Rx (1.1) where Bn is the (n − 1) × n bidiagonal matrix −1 1 −1 1 .. .. Bn = . . −1 1 x The x-component offsets Ci,j between adjacent images in a column are described by the equations x Xi+1,j − Xi,j = Ci,j (i = 1 : m − 1, j = 1 : n) These (m − 1)n column stitching equations can be summarised as Bm X = C x (1.2) The foregoing discussion can be repeated for y-components, leading to the stitching equations T Y Bn = R y , Bm Y = C y These equations are of the same form as the equations (1.1–1.2) for the x-components, and so may be treated in the same way. The y-component equations are not coupled with the x-component equations, so they may be computed separately. π 10 CHAPTER 1. STITCHING IC IMAGES In order to use standard software, it is convenient to convert the stitching equations into matrix-vector form. The Kronecker product is a standard technique for this sort of conversion [1]. The matrix of image location x-components X is converted into a vector by stacking the columns, X1:m,1 . . ¯ x = vec(X) = . X1:m,n Converting the row stitching equations (1.1) gives (Bn ⊗ Im )¯ = vec(Rx ) x where Im is the m × m identity matrix and ⊗ is the Kronecker product. Similarly, the column stitching equations (1.2) are converted to (In ⊗ Bm )¯ = vec(C x ) x ¯x The system of stitching equations is thus A¯ = b where ¯ Bn ⊗ I m vec(Rx ) A= , b= In ⊗ B m vec(C x ) ¯ Finally, we impose the condition x1 = X1,1 = 0, which fixes the global coordinate system's origin. This yields the system Ax = b (1.3) ¯ ¯ where A is A with the first column deleted, and x is x with the first element deleted. The system (1.3) has full column rank. 1.3.2 Least squares Equation system (1.3) has (m − 1)n + m(n − 1) stitching equations for the mn − 1 unknowns in x. This system of equations is overdetermined whenever m > 1 and n > 1. Unless the offsets Rx and C x happen to be consistent, any set X of image locations will give some nonzero error ("residual") in the equations. One way to determine X is to seek the minimiser of the sum of squares of the residuals of equations (1.1–1.2) subject to the grounding condition X1,1 = 0. This is equivalent to the unconstrained minimisation of (Ax − b)T (Ax − b) For a physical interpretation of the least squares formulation, imagine the grid as an array of rigid plates connected by elastic springs (Figure 1.7). A stitching equation that is satisfied corresponds to a spring that is neither stretched nor contracted. There are more springs than there are degrees of freedom, so the springs stretch or contract to find the configuration of minimum potential energy. This configuration is the least squares solution, and the residuals are the extensions in the springs. π 1.3. LEAST SQUARES 11 Figure 1.8: Residuals for least squares stitching The least squares minimiser is the solution of the square system of "normal" equations AT Ax = AT b The normal equation system is nonsingular because A has full rank, and it can be solved reliably and efficiently using standard sparse linear algebra software. The Matlab script LSstitching.m [2] does least squares stitching of a 14 by 8 grid of images using offset data provided by the problem proposer. Computing time on a 400 MHz Macintosh is 0.02 s. The least squares solution's residuals are mostly 0–3 pixels, although a few seams have residuals of 4–7 pixels (Figure 1.8). The problem proposer did a visual inspection of the least squares solution and reported that all but one of the seams appears to be OK. 1.3.3 Constrained Least Squares In order to reduce the maximum residual obtained by the least squares solution we impose the inequality constraints −α ≤ b − Ax ≤ α where α is the desired limit on the residual's absolute value. The smallest value of α that gives a feasible solution can be found by trial and error or by solving a linear programming problem. The constrained least squares problem can be solved using standard optimisation software packages. In script CLSstitching.m [2] we used the Matlab Optimization Toolbox (v. 1.5.2) routine conls. For the 14 by 8 grid the smallest feasible α was 4 pixels for the x-components and 5 √ pixels for the y-components. The maximum residual thus has length 42 + 52 ≈ 6, which is only 1 pixel smaller than for the unconstrained least squares solution, and many more of the residuals are large (Figure 2), so this solution seems to be of little practical value. π 12 CHAPTER 1. STITCHING IC IMAGES Figure 1.9: Residuals for constrained least squares stitching 1.3.4 Weighted Least Squares Another approach to improving the practical quality of the stitching is to minimize the weighted sum of squares of the residuals, (Ax − b)T W 2 (Ax − b) where the weighting matrix W is a nonsingular diagonal matrix. The diagonal elements of W can be interpreted as stiffness constants for the springs holding together the rigid plates in Figure 1.7. With W = I all the springs have equal stiffness, and we revert to the original "unweighted" least squares problem. The weighted least squares problem can be solved by simply replacing A and b in the unweighted problem by W A and W b. One possible strategy for assigning the weights would be to give larger weights to those equations where the quality of the stitching is most sensitive to stretching or shear. For example, in parts of the images that contain a lot of parallel lines we want the lines to match up correctly at a seam, so we assign a large stiffness to the appropriate spring. A seam that contains no features would be assigned a small stiffness. It might be possible to derive this kind of sensitivity information automatically from the correlation data that were used by the problem proposer to compute the offsets; we would need more information about the images and the correlations to explore this possibility. Another strategy would be to assign the weights using interactive graphics. Based on the solution of the unweighted least squares problem, a human operator would examine the seams where stitching quality is unacceptable, adjust those seams' stretching and shearing stiffnesses, and recompute the weighted least squares solution. This process could be iterated until (hope- fully!) all the seams were acceptable. If the initial solution gives only a small number of unacceptable seams, this interactive adjustment would not be too laborious. A third strategy, which uses only the information given in the problem, is to assign small π 1.3. LEAST SQUARES 13 Figure 1.10: Histograms of offset values weights to those equations whose offset value differs significantly from the mean value. The rationale is that these offsets are erroneous ("outliers"), because of some sort of measurement error, and should be ignored. √ Applying this third strategy to the 14 by 8 grid, we assigned weights of 0.1 to those equa- tions whose offset value differed by more than 3 pixels from the mean (Figure 1.10); the remaining equations' weights were 1.0. In the weighted least squares solution (WLSstitching.m [2]) the residuals of the outliers tends to increase (compared to their values in the unweighted least squares solution), but the remaining residuals mostly decrease (Figure 1.11). The problem pro- poser reports that with the weighted least squares solution all the seams appear to be OK, so this strategy seems to be promising. 1.3.5 Least Squares with Deformable Images A way of reducing the residual at all the seams is to introduce more degrees of freedom by allowing the images to deform in various ways. We introduced deformability by dividing each image into four equal subimages. Theoretically, the sum of squares of the residuals scaled by the length of the seams of the subimages should decrease. Applying this approach to the 8 by 14 grid gave a maximum residual of 5 pixels, which is 2 pixels less than with the original least squares solution. We believe that more sophisticated deformation models, combined with weighting and more offset data (for example, measuring several x-offsets along an east-west seam), could bring all the seam residuals down to less than 1 pixel. π 14 CHAPTER 1. STITCHING IC IMAGES Figure 1.11: Residuals for weighted least squares stitching 1.4 Simulated Annealing A more fundamental approach to stitching is to directly minimise an error function computed from the overlapping pixels in the entire image. In this section the minimisation problem is solved using the method of simulated annealing. Continuous minimisation methods for this problem are studied in section 1.5. Simulated annealing is one of a family of meta-heuristics that are able to solve instances of hard optimization problems. These meta-heuristics take advantage of the ability to quickly define a local perturbation and then to recalculate the global objective function by a fast, local calculation. This family of meta-heuristics also includes hill-climbing, Tabu search, genetic algorithms, and rising flood techniques. The central problem is to avoid local extrema. Simulated annealing does this by analogy to cooling solids. Initially the setting is "hot", meaning that perturbations that worsen the objective function are sometimes allowed. We gradually "cool down" the setting, meaning that fewer bad moves are acceptable, as we hone in on better objective values. To avoid getting stuck in a local extremum, we periodically crank up the heat and again slowly cool. Repeating this leads to surprisingly good solutions. We wrote a C++ simulated annealing program for image stitching. It is licensed under the GNU GPL and available for download [3]. The program has sophisticated error handling routines that identify well-formatted input data. Input parameters include minimum feature size, image translation bounds, temperature, number of reheating and recooling stages, and temperature thresholds. We first defined an objective function that measured how far each image is placed from its ideal offsets using an error threshold defined by the minimum feature size. A seam whose error is less than this threshold in both coordinates contributes 0 to the objective function, otherwise it contributes 1. The program randomly selects an image to move and makes a small translation of π 1.5. NONLINEAR PROGRAMMING 15 the image, locally recalculating the overall objective function by comparison to the ideal offsets for that single image. This objective function always rapidly diverged to the worst case (all boundaries bad) and was never able to improve. We believe the problem was the fact that the objective function had no way to recognize if a boundary was non-optimal but very close. It registers the same value as the boundary being far from a good fit. Thus once a boundary was bad, the vast majority of perturbations would leave the objective function unchanged leading to wandering on plateau in the state space. We then defined an objective function that scored 0 if the alignment is within a feature size and scored a value that was proportional to how far away from correct the boundary was if it was not a good fit. This rapidly converges to a good layout. Instead of doing merely local translations, it is straightforward to incorporate small rota- tional changes, shrinking and expanding changes, and slight skewing changes into this simulated annealing algorithm. This would require subroutines to rapidly recalculate alignment scores along changed borders between images. After performing the above generalized perturbations on a single image, we would compute the correlations of pairs of images to determine how the changed image correlates with adjacent images, and use that to measure the fitness of the image. If these routines are quick then the algorithm could address the very real concern that stack movement and other noise may produce skewed, scaled, or rotated images. 1.5 Nonlinear Programming 1.5.1 Cost Function In this section we represent each image as a matrix [uij ]Ly ×Lx of integers (corresponding to pixel grey scale values). There are target offset overlaps (Sx and Sy ) which provide overlap regions to help align the images. Assuming that the target overlaps are achieved, the top left corner of each image define the reference grid (Figure 1.12). In practice however, errors during image acquisition modify these overlap regions. This can be quantified by associating an offset error ∆pq = (∆pq , ∆pq ) with every image upq . The offset error is defined relative to the reference grid. x y i,j−1 The left overlap region between image uij and ui,j−1 has width Sx − (∆ij − ∆x ) and height x Ly − (∆y − ∆y ) . Similarly, the top overlap region between image u and ui−1,j has width ij i,j−1 ij i−1,j Lx − (∆ij − ∆i−1,j ) and height Sy − (∆ij − ∆y ) . x x y It is also assumed that we have an upper bound on the offset errors, i.e., |∆ij | ≤ βx and x |∆ij | ≤ βy where βx and βy are constants, or, an upper bound on the relative offset errors y between every pair of neighbouring images. Let i,j−1 ηx = ∆x − ∆ij , x (1.4a) i,j−1 ηy = ∆y − ∆ij , y (1.4b) ξx = ∆i−1,j x − ∆ij , x (1.4c) ξy = ∆i−1,j y − ∆ij , y (1.4d) π 16 CHAPTER 1. STITCHING IC IMAGES Figure 1.12: Image layout using reference grid approach. Solid lines show images at target offset overlaps (upper left corners are shown as •), dashed box shows image uij displaced by offset error ∆ij . and thus |ηx | ≤ βx , |ηy | ≤ βy , |ξx | ≤ βx , |ξy | ≤ βy . It is unclear from the problem specification whether the βx and βy bound the relative offset errors or the actual offset errors, but the following discussion and code handles both cases. We calculate the cost function by summing the contributions from the top and left overlap regions for all images uij . The cost function contribution due to uij is 2 2 uij − ui,j−1 uij − ui−1,j left overlap pixels top overlap pixels d ij = + , # of left overlap pixels # of top overlap pixels = cost contrib left(uij, ui,j−1, ηx , ηy ) + cost contrib top(uij, ui−1,j, ξx , ξy ), (1.5) where the first term is 0 if j = 1 and the second term is 0 if i = 1. Note that d ij depends explicitly on ∆ij , ∆i−1,j and ∆i,j−1 . The total cost function is then calculated as d= d ij . (1.6) i j π 1.5. NONLINEAR PROGRAMMING 17 1.5.2 Minimization At this point, we try minimizing the above cost function using Matlab's fminsearch over the space of ∆'s. However, we encounter an unexpected hurdle: the stitching of images is not a discrete problem whereas our cost function calculation assumes that the offset errors were integers. One may question the usefulness of resolving the offset errors to a "sub-pixel" level when feature sizes are O(10) pixels, however, finding a free black-box optimization code which generates and uses only integer values is difficult. We propose several fixes: • round ∆ij when calculating the cost function. • interpolate the image data on the sub-pixel level. • interpolate our cost function. As expected, rounding does not produce a convergent result. Interpolating the image is imprac- tical for larger problems where realistically, a caching approach as discussed in Section 1.5.2 is required. We choose to implement interpolation of the cost function. Cost Function Interpolation Let cost contrib left() be a function that calculates the cost function contribution in the left overlap region between neighbouring images uij and ui,j−1 . Assuming that ηx and ηy are integers, the cost contribution is calculated as cost contrib left(uij, ui,j−1, ηx , ηy ). To accommodate fractional offset errors, consider ηx = η x + δ x , (1.7a) ηy = η y + δ y , (1.7b) where δ ∈ [0, 1) × [0, 1), · = floor(·) and · = ceil(·). We now build the lookup table for the cost function contribution at the 4 nearest neighbouring integer values of η by calculating c x y = cost contrib left(uij, ui,j−1, ηx , ηy ), (1.8a) c x y = cost contrib left(uij, ui,j−1, ηx , ηy ), (1.8b) ij i,j−1 c x y = cost contrib left(u , u , ηx , ηy ), (1.8c) ij i,j−1 c x y = cost contrib left(u , u , ηx , ηy ). (1.8d) Our estimate, c of the cost function contribution is a weighted average of (1.8). Specifically, we use bilinear interpolation, by first calculating c x = (1 − δy )c x y + δy c x y , c x = (1 − δy )c x y + δy c x y , and then calculating c = (1 − δx )c x + δx c x . A similar discussion applies for cost contrib up() using ξx and ξy . π 18 CHAPTER 1. STITCHING IC IMAGES Figure 1.13: Test Case 1. Cost Function Space The set of images above was used as Test Case 1. The original image provided by Semiconductor Insights of a chip was artificially broken into nine 700 × 600 pieces with overlap regions of 100 pixels and random offset errors in the range of −6 to 6 in both the horizontal and vertical direction (Figure 1.13). Unfortunately, given a random initial guess for the ∆'s, fminsearch could not find the global minimizer. We present two figures of the function space in question. Given two sets of offset errors say ∆ and ∆exact , we define the radius r as ∆ − ∆exact . Figure 1.14 shows the cost as ˆ a function of radius for fixed directions θ from the exact solution, i.e., define ˆ ∆ − ∆exact θ= (1.9) ∆ − ∆exact and plot cost as a function of r. Notice that none of curves are monotonic, in fact, a plot of cost ˆ as a function of radius for 1000 θ's reveal that only one or two curves were monotone. The cost function space has a lot of local mimima; this presents problems for most optimization routines. We also present the plots obtained by keeping most of the offset variables constant at the known exact errors while varying pairs of offset variables (Figure 1.15). One can clearly see the existence of many local minimas. Close to the exact solution however, the function spaces are very smooth (Figure 1.16). This provides some hope that an optimization scheme can indeed be used, provided a good initial guess is given. Iterative Scheme To provide a better initial guess for the optimization routine, we adopt an iterative approach to the problem. This involves stitching one additional image each iteration. π 1.5. NONLINEAR PROGRAMMING 19 4 x 10 8 7 6 5 cost 4 3 2 1 0 0 2 4 6 8 10 12 14 16 18 20 radius Figure 1.14: Cost as a function of radius for various directions in offset error space. 4 4 4 x 10 x 10 x 10 5 5 5 0 0 0 8 8 8 12 12 12 0 0 0 0 0 0 −8 −12 −8 −12 −8 −12 4 4 4 x 10 x 10 x 10 5 5 5 0 0 0 8 8 8 12 12 12 0 0 0 0 0 0 −8 −12 −8 −12 −8 −12 4 4 4 x 10 x 10 x 10 5 5 5 0 0 0 8 8 8 12 12 12 0 0 0 0 0 0 −8 −12 −8 −12 −8 −12 Figure 1.15: Multiple plots of the cost function space for arbitrary pairs of offset variables. (Other variables held at constant, correct minimizing values.) π 20 CHAPTER 1. STITCHING IC IMAGES 4 4 x 10 x 10 4 5000 4 2 2 0 0 0 2 2 2 2 2 2 0 0 0 0 0 0 −2 −2 −2 −2 −2 −2 4 x 10 5000 4000 4 2000 2 0 0 0 2 2 2 2 2 2 0 0 0 0 0 0 −2 −2 −2 −2 −2 −2 4 x 10 5000 4000 4 2000 2 0 0 0 2 2 2 2 2 2 0 0 0 0 0 0 −2 −2 −2 −2 −2 −2 Figure 1.16: Multiple plots of the cost function space close to the global minimum. Also note the effect of bilinear interpolation. We begin by choosing a particular ordering of the images, (u1 , u2 , . . . , uK ), K = M ∗N where each uk corresponds to a unique uij and uk shares at least one common edge with (u1 , . . . , uk−1 ). A simple ordering is u1 u2 u3 u4 u5 u6 More effective orderings are considered in Section 1.2. The following pseudo-algorithm then illustrates the process of sequentially stitching one additional image per iteration: 1. Assume that ∆1 ≡ 0 and set k = 2. 2. Stitch u1 , . . . , uk assuming that the known ∆1 , . . . , ∆k−1 are fixed and only ∆k is allowed to change, i.e., find the optimal ∆k which stiches the images. (Note, we use a random initial guess for ∆k ) 3. Stitch u1 , . . . , uk varying ∆1 , . . . , ∆k , using the values from step 2 as the initial guess. 4. Set k = k + 1 and return to step 2. We acheived correct convergent results in approximately 90% of the trials, however, the conver- gence was very slow. On a 3 × 3 set of images (i.e., 14 offset variables), the code took over eight hours to complete on a 1.2 GHz Athlon MP. π 1.5. NONLINEAR PROGRAMMING 21 Pre-Conditioner In retrospect, it is not necessary and quite inefficient to take a random initial guess for ∆k in step 2, Section 1.5.2. Figure 1.16 shows that an initial guess within two pixels is likely sufficient for convergence. A better approach is to do an exhaustive search over a finite set of values for ∆k , and use the ∆k providing the smallest contribution to the cost function as the initial guess in step 3 of Section 1.5.2. A natural space of relative offset errors is {∆ ∈ Z, |η| ≤ |β|} (where β = (βx , βy )) if we have bounds on the offset errors. If we instead have bounds on the relative error between images, we can do something similar: {η ∈ Z, |η| ≤ |β|} where the inequalities are between uij and ui−1,j unless i ≡ 1, in which case the inequalities are between uij and ui,j−1 . An important point to realize is that although our formulation uses a fixed reference grid, the contribution to the cost function between neighbouring images only depends on the relative difference in offset errors between them. Upon implementation of the pre-conditioner, our code became more robust, with a noticable increase in speed. We were actually quite surprised at how robust the code was. It was unclear if our results were attributed to the artificial construction of test images (cutting a single picture into multiple parts), so we tested our code on a variety of altered images, for example: • After cutting up the image into smaller test images, each test image was saved as a jpeg with a quality rating of 20. Quality ratings for jpeg range from 0 to 100 where a higher number means less image degradation due to compression. • Each test image was also subjected to a filter which added gaussian bluring (within a radius of 5 pixels). • Each test image was subjected to a noise filter. This noise filter "jittered" the image by randomly perturbing some pixels a small distance1 . This filter actually made the images look quite horrible and hard to optimize. • The output from the pre-conditioner was perturbed by | | < 1 to simulate the case when the pre-conditioner does not find the correct values. Our code still found the global minimum for all the test cases, giving us confidence that it could work when real images are used. Lots of nasty attributes such as lens abberation, difference in contrasts, warping of images would be noticed in real images. Caching A co-author suggested that caching the cost function contribution for various ∆'s would pro- vide significant speedup. This should translate to significant computational savings during our bilinear interpolation. Another important motivation is the difficulty in storing a 8 × 14 grid of 8k × 8k images in memory (7 Gb at one byte per pixel). There are two possible stages to implement caching: pre-caching the cost function contributions before the optimization step or caching during the optimization step (i.e., cache the cost contribution for each new integer ∆). 1 "pick" filter in GIMP (www.gimp.org) π 22 CHAPTER 1. STITCHING IC IMAGES The former approach was taken because there is less book-keeping involved. It is likely though that caching during the optimization step will provide further speedup. For every image uij , associate two matrices DL and DU which correspond to cached contri- butions from the left and top image respectively. As mentioned in Section 1.5.2, although our formulation uses the fixed reference grid, the contribution to the cost function from neighbour- ing images depends only on the relative difference in offset errors. We utilize the experimental constraint that the offset errors are bounded above by β. Thus, DL and DU are matrices with (2βy +1)×(2βx +1) elements. Note, in practice, we cache a larger matrix to account for roundoff error. For a 6 × 5 grid of 700 × 600 images (Test Case 2) with βx = 10 and βy = 8, caching took 7 minutes and optimization took 14 minutes. Note, our non-caching code for this problem was halted after 4 days. A rough estimate for caching the 14 × 8 problem described above is approximately nine hours. Note that the caching algorithm scales linearly with the number of images and linearly with the size of the overlap region. If the computation time is too long, the caching algorithm is easily parallelizable for a cluster type network. 1.5.3 Results The optimization code encounters no difficulties fitting Test Case 2. A rough extrapolation suggests that the 14 × 8 problem with 240 variables would take about one day. Matlab codes and more details on this work are available online[4]. The authors of this Section thank Jim Verner for proof-reading and constructive criticism and Jason Nielsen for help with extrapolation techniques. 1.5.4 Future Work • One should actually try the fixed reference grid approach of sequentially fitting images. Global optimization may not be necessary. • Combine the iterative scheme in Section 1.5.2 with a more effective ordering considered in Section 1.2. • Employ a strategy whereby offset variables from images that have enough surrounding neighbours are removed from the optimization process. One could use this process to bound the number of parameters in the optimization. • Throughout this discussion, we have assumed that the images have been pre-processed, e.g., the images have been rotated correctly. The pre-processing stage can be incorporated into the cost function. • Recode our algorithm in a more efficient environment such as C or Fortran. • The cost function is easily modified to fit two different layers of images. A first step would be to detect via-ups and via-downs in each image/layer. (A good segmentation algorithm based on levelsets or wavelets would probably do the job.) One could then calculate the cost function for each layer, sum them together to create the new cost function, and add π 1.6. CONCLUSIONS 23 a penalty term to the new cost function if the vias connect to wires either above or below. The parameter space for two layers of 14×8 images can be handled by a desktop computer in a reasonable amount of time. • This problem can be reformulated to incorporate the power of parallel processing. For example, the caching routine is easily parallelized. • For large images, it is not necessary to read in the whole image when optimizing for the offset errors. In fact, less than 5% of the image is required assuming an overlap width of 100 on 8k × 8k pictures. Incorporating this idea will relax memory constraints. • Custom tailor a minimizer that pays attention to the pixel scale. It can use sub-pixel infor- mation if necessary but ideally should not because sub-pixel information is not important. A first attempt would probably involve a simple algorithm: 1. Calculate an approximation to the Jacobian (finite differences). 2. Calculate an approximation to the Hessian (finite differences). 3. Calculate a search direction based on the Jacobian and Hessian (simple line search). 4. Choose an appropriate step in the search direction - i.e., integer values. 5. Evaluating the Hessian and Jacobian is probably going to be quite expensive, so it's probably better to "Freeze" the Jacobian and Hessian by returning to step 3 and recalculating a search direction. • If the images were collected in a hexagonal pattern (i.e., acquisition of images for every other row is offset by Lx ) then one would expect a better alignment due to increased 2 connectivity. • A more radical approach would be to incorporate (local) stitching into the circuit gener- ation code. The code would follow traces, and whenever it encountered the edge of an image, it would simply do a local stitching with the neighbouring image and continue the trace onto that image. 1.6 Conclusions The different solutions presented here require different levels of effort to be implemented, with corresponding different potential payoff. The graph theory solutions of Section 1.2 provide improvements on the patterns of sequential stitching currently used. The least squares codes of Section 1.3 fairly rapidly compute stitching solutions based on the offset data. The simulated annealing and nonlinear programming codes of Sections 1.4–1.5 are more flexible, because they can find optimal stitchings based on the image pixel data, but they are computationally much more demanding. π 24 CHAPTER 1. STITCHING IC IMAGES π Bibliography [1] James W. Demmel, Applied Numerical Linear Algebra, SIAM, 1997, Section 6.3.3. [2] ftp://ftp.cc.tut.fi/pub/math/piche/stitching/ [3] http://mathstat.carleton.ca/~brett/Research/Code/#ipsw02 [4] http://www.math.sfu.ca/~bwo/ipsw 25


text:
Escondido, California: Grape Slide, Climbing Twigs
A grape-themed children's day park with a grape slide, climbing twigs, and a lounge leaf.Grape Day Park
- Address:
- North Broadway, Escondido, CA
- Directions:
- Grape Day Park, in Central Escondido, North Broadway across from Sherman Pl.
- Admission:
- Free.
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A grape-themed park, with a Fruit-of-the-loom zoom! Who knows what inspired the artist to associate fruit with children's amusement, but the result is a bunch of grapes with a slide and "twigs" for climbing.
[Courtney, 04/14/2010]Nearby Offbeat Places
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MotoGP: Italy (spoilers al dente)
yooperbikemike is snoozing, so ...
It's easy to say a win at Mugello will be crucial to Lorenzo (given the way Pedrosa was looking really shaky just two rounds in, and two rounds after that started looking like the favorite again). But after Lorenzo's poor race at Le Mans, Mugello might well show whether he will be able to slow or stop the gap to the factory Hondas. If the track is cool (unlike Jerez, where the heat supposedly took away the M1 edge-grip advantage), and Lorenzo doesn't finish strongly, I imagine there's going to be a sense of doom in the Yamaha team. |
Yeah, but there's that Rossi fella who is gonna win for Yamaha.
:hide |
It's actually still quite chilly here. Winter is hanging on.....
http://i226.photobucket.com/albums/d...180d15f1c8.jpg:D |
It's going to rain, on Thursday, Friday, Saturday. Sunday, not so sure. This could end up saving Yamaha, as I think otherwise, they could get spanked by Honda. Badly.
In fact the route through the western alps was aborted due to the weather, and had to face the prospect of being gassed in a bloody tunnel!
http://i226.photobucket.com/albums/d...e0e7df34b1.jpg Still, the scooter GP goes on. And I love it! http://i226.photobucket.com/albums/d...0fe634f854.jpg |
Love this track. Love the wild front "straight" with kinks and dips at very high speeds, plus all those cool uphill esses and every other damn thing about Moo-Jello.
Video of Yamaha on-board lap. (You can simulate a Ducati on-board lap by freezing the video for two seconds at the entry to each corner. :D ) <iframe width="853" height="480" src="http://www.youtube.com/embed/AiAR-NaOcto" frameborder="0" allowfullscreen></iframe> |
Mugello is one of the greatest tracks, a delight to watch even on the telly. Right there with Phillip Island and Laguna Seca. I went to Mugello in 2006, seeing that place live, it´s even better.
Yamaha must be worried, Lorenzo needs to bounce back quickly, but Honda seem to have the momentum now. And on the other side of that garage, Rossi must be worried, that if he gets his behind handed to him by his team-mate here, it´s gonna happen everywhere. 4 out of 4 rounds already, not looking good for him. |
Righto, I'll start,
Pedrosa Lorenzo Rossi Marquez Crutchlow Someone else And if it rains, who knows. :lol3 |
Oh, and Bootlace, looking forward to your photo's. :clap
(looking forward to some pics as well...) |
Hey rock doc, here's something for ya........ http://fredgassit.tripod.com/. |
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Sony Alpha DSLR-A380 Review
Review based on a production Alpha DSLR-A380 with firmware version 1.0
In January last year Sony was one of the first manufacturers to 'diversify' its entry level DSLR offerings when it replaced the DSLR-A100 with not one but three new cameras - the DSLR-A200, A300 and A350. Now, one and a half years later, the product hierarchy in Sony's entry-level segment remains unchanged and the current models are being replaced by the DSLR-A230, A330 and A380 respectively.
On the spec sheet the upgrades are of a fairly minor nature and as before the three models are extremely close in terms of price and features. At the bottom of the line the A230 offers a 10.2 megapixels sensor in a fairly basic body, the A330 adds live view to the mix and the A380 tops it up with a more pixel-dense sensor (14.2 megapixels). All three models are aimed at the first-time DSLR user.
Along with the new cameras Sony has launched a number of new lenses. The DT 18-55mm F/3.5-5.6 standard zoom lens and the DT 55-200mm F/4-5.6 tele zoom come in the one- and two-lens kits that each camera can be ordered in, but are also available separately. Additionally there is a new DT 50mm F/1.8 prime lens and a DT 30mm F/2.8 macro.
While the headline specs of the new camera models have hardly changed Sony says it has put substantial work into the cameras' image processing engines. To find out how the A380 performed in our tests and what impact this work has made on the A380's image output read our full review.
A brief history; Sony/Konica Minolta entry level digital SLR series
- 15/07/05: Konica Minolta Maxxum 5D (6 mp)
- 05/06/06: Sony DSLR-A100 (10 mp)
- 07/01/08: Sony DSLR-A200 (10.2 mp)
- 30/01/08: Sony DSLR-A350 (14.2 mp) / Sony DSLR-A300 (10.2 mp)
- 18/05/09: Sony DSLR-A380 (14.2 mp) / Sony DSLR-A330 (10.2 mp) / Sony DSLR-A230 (10.2 mp)
Compared to Alpha 350 - key differences
When glancing at the A380's headline specification you could be forgiven for thinking that apart from a few cosmetic and minor control layout changes the new model is identical to its predecessor. However, when diving deeper into the tables of the spec sheet you'll discover a few more changes that, surprisingly, in many cases represent a downgrade rather than an upgrade.
- Smaller and approximately 125g lighter
- New control layout and fewer external buttons (No AEL and SteadyShot buttons)
- Re-vamped user interface with on-screen help guide
- No white balance Kelvin option
- No white balance bracketing
- Weaker flash (GN 10 vs 12 on the A350)
- No spot-metering circle in viewfinder
- No wired remote terminal
- Smaller battery (500 shots vs 730 on A350, CIPA standard)
- SD card and MS Pro instead of CF card storage
- No optional battery grip
Fast AF Live View
The Sony DSLR-A380 uses the same live view system that we first saw on the A350. Sony's system stands alone amongst contemporary Live View systems in that it does not show the output from the main imaging sensor. Instead there is a second, smaller sensor placed up in the viewfinder tunnel.
Roll your mouse over the above images to see how the light path is redirected between the viewfinder and live view sensor |
The advantage of this layout is that, unlike any other current Live View system, the camera can offer live view with the reflex mirror down. This means it behaves just like a conventional DSLR when in Live view mode, only flipping the mirror out of the way when you press the shutter button to take the photograph. The result is to give the most responsive Live View mode on any contemporary camera meaning that essentially you get the convenience of a compact camera with the speed of focus of a conventional DSLR.
All other current systems have to flip the mirror up so that the main sensor can produce the live view image. Taking a photo requires dropping the mirror again to focus or meter, then lift it again to take the actual exposure. Although this mirror flapping makes it slower, using the main imaging sensor offers other advantages. Using the main imaging sensor allows the use of the potentially more accurate and flexible contrast-detection autofocus system. Access to the main sensor also allows a very fine resolution preview for manual focus confirmation. Another disadvantage of the system is a frame coverage of only 90 percent The Sony system offers an excellent level of responsiveness but gives up the precision that macro and studio shooters are likely to need.
If you're new to digital photography you may wish to read the Digital
Photography Glossary before diving into this article (it may help
you understand some of the terms used).
Conclusion / Recommendation / Ratings are based on the opinion of the reviewer, you should read the ENTIRE review before coming to your own conclusions. Images which can be viewed at a larger size have a small magnifying glass icon in the bottom right corner of the image, clicking on the image will display a larger (typically VGA) image in a new window. To navigate the review simply use the next / previous page buttons, to jump to a particular section either pick the section from the drop down or select it from the navigation bar at the top. DPReview calibrate their monitors using Color Vision OptiCal at the (fairly well accepted) PC normal gamma 2.2, this means that on our monitors we can make out the difference between all of the (computer generated) grayscale blocks below. We recommend to make the most of this review you should be able to see the difference (at least) between X,Y and Z and ideally A,B and C. |
This article is Copyright 2009 Dpreview.com and the review in part or in whole may NOT be reproduced in any electronic or printed medium without prior permission from the author. For information on reproducing any part of this review (or any images) please contact: Phil Askey
4 0 4 Discuss in the forums |
Sony Alpha A380L 14.2 MP Digital SLR Camera with Super SteadyShot INSIDE Image Stabilization and 18-55mm Lens | $479.99 |
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Sony Alpha DSLR-A380 Digital SLR with 18-55mm & 75-300mm Lenses and Carrying Case wtih Turtorial DVD | $799.99 |
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4GB 4 GB Memory Stick / Card PRO DUO for Sony Alpha Series DSLR A230 A290 A330 A380 A390 A450 A500 A550 A560 A580 A700 A850 A900 SLT A33 Digital Camera | $12.90 |
Sony Alpha A380Y 14.2 MP Digital SLR Camera with Super SteadyShot INSIDE Image Stabilization and 18-55mm and 55-200mm Lenses | $1,499.00 |
Sony Alpha A380 Digital SLR 14.2 MP, Super SteadyShot INSIDE Image... | $549.95 |
16GB 16 GB Sony PRO DUO Memory Stick / Card for Alpha DSLR A230 A330 A380 A450 A500 A550 A850 A900 Cyber-Shot DSC G1 G3 H3 H7 H9 H10 H20 H50 H55 HX1 HX5 S750 S780 S930 S950 S980 S2100 T2 T20 T77 T90 T100 T200 T300 T500 T700 T900 TX1 TX5 TX7 Digital Camera | $63.93 |


text:
Green Lake circle run will start at 7 a.m. Sunday
The run around Green Lake, an official 12-mile distance, will start at 7 a.m. on Sunday from in front of Mel's Sports Shop and finish at Melvin's on the Lake.
The start time was moved up one hour five years ago to lessen the risk of heat exhaustion.
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Legacy of Heroes
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-Construct a deck full of heroic allies and mighty superpowers!
-Choose what kind of superhero you become. Will you shape fire into weapons of mass destruction, or intimidate your foes from the shadows?
-Immerse yourself in a deep setting full of enthralling story arcs and sinister enemies, brought to life by professional comicbook artists.
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text:
My girls... Dash & Lena. http://img.photobucket.com/albums/v4...l/girls001.jpg DASH: 7 y/o Dash for Cash QH -- http://img.photobucket.com/albums/v4...irl/dash10.jpg LENA: 10 y/o Doc O Lena QH -- http://img.photobucket.com/albums/v4...l/girls002.jpg |
Beautiful horses! :D Where in AZ are you? I'm in Tucson 8) Great pics! |
Oh that's awesome! I'm in Scottsdale. How long have you lived here? |
:cry: i want lena... shes PROPER stunning :( i think u shud import her 2 the uk for me and give her to me :D |
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By Business Wire | Article Rating: |
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2014 NAB SHOW, Booth SL8511
Aspera, Inc. (http://www.asperasoft.com/), an IBM Company and creators of next-generation software technologies that move the world's large data at maximum speed, expanded its partner ecosystem with new joint solutions powered by Aspera FASP™ high-speed transport technology.
New partners choosing Aspera FASP (http://asperasoft.com/technology/transport/fasp/) as their transport of choice include both established and startup cloud platform, software and service providers; workflow automation, processing and transformation technology vendors; and leading providers of media production, storage, publishing and distribution platforms. These new partnerships further expand Aspera's partnership network enabling broader access to its high-speed transfer technology and deeper integration into media workflows.
Cloud Storage
With Aspera On Demand, Aspera is the first in the world to offer seamless, high-speed transfers of very large media files and sets of files directly to and from cloud storage, enabling a new breed of media applications and services. The Aspera high-speed transfer platform–as-a-service now supports IBM SoftLayer (http://www.softlayer.com/), Google Cloud Platform (https://cloud.google.com/) and OpenStack (https://www.openstack.org/), in addition to Amazon Web Services (http://aws.amazon.com/) and Windows Azure (https://www.windowsazure.com/).
The Aspera software platform tightly integrates with cloud-based storage, accelerating the transfer of content with unmatched speed, reliability and predictability at any distance. This includes embedded capabilities for content security, user authentication, browsing and access control across all major cloud and enterprise storage platforms. Organizations now have access to the broadest set of infrastructure choices as more digital media workflows migrate to the cloud.
Content Distribution
Leading content distribution networks Akamai (http://www.akamai.com/), Level 3 (http://www.level3.com/) and Limelight (http://www.limelight.com/) have all made Aspera's high-speed transfer technology available in their infrastructure to shorten the time it takes to ingest content. Aspera transfer clients provide native support for connections to the platforms directly in the graphical user interface (GUI) for a seamless user experience.
At NAB, Akamai and Aspera will showcase the recently announced general availability of Aspera Upload Acceleration to NetStorage, which features deep integration of Aspera FASP technology with Akamai NetStorage. The new offering allows media companies to upload digital assets at extreme speed to Akamai's cloud-based online storage platform from any global location, independent of network latency and packet loss. It includes embedded security, bandwidth control and seamless integration with existing Aspera workflows.
Media services in the Cloud
Aspera's FASP technology is enabling a new breed of cloud-based software and service providers to capture, analyze, process, transform and store media. With the addition of the new Aspera Byte-Stream API (http://asperasoft.com/technology/transport/fasp-byte-stream-api/), media streaming and content transcoding companies are integrating the FASP technology within their processing engines. This enables them to transfer content to the cloud for real-time, on-demand transfer and streaming from growing file sources and transcode while an upload is still in progress.
Aspera FASP technology embedded in Sony Media Cloud Services Ci (https://www.sonymcs.com/) platform enables high-speed transfer of full-resolution media to and from the cloud. The combined solution makes it simple for people to assemble teams online, work on projects and access files from locations around the world. Demonstrations will be available at the Aspera Booth SL8511 and Sony Booth C11001.
Innovative companies such as Brightcove / Zencoder (http://www.brightcove.com/), Elemental (http://www.elementaltechnologies.com/), encoding.com (http://www.encoding.com/), FilmTrack (http://filmtrack.net/), thePlatform (http://theplatform.com/), VHX (https://www.vhx.tv/), Vizrt (http://www.vizrt.com/) and Windows Azure Media Services (http://www.windowsazure.com/en-us/services/media-services/) will be showcasing products at NAB that integrate Aspera for high-speed transfer and distribution. These offerings are enabling new media management and transformation services used in production and post-production.
In addition, start-up CloudCam will demonstrate a new solution that captures and transfers video from a user's mobile device to the cloud using Aspera FASP for real-time streaming over the web to social networks.
Content Production
New Aspera-powered high-speed transfers into Grass Valley® GV STRATUS® (http://www.grassvalley.com/products/gv_stratus) production tools will be on display in Booth SL206, dramatically shortening content ingest times and enabling faster, more effective production workflows.
Mediabank (http://www.netwerk.no/products-services-medianetwerk/mediabank-medianetwerk) cloud applications combined with the Aspera high-speed transfer boosts global collaboration and distribution in media production, enabling rapid turnaround and cost-effective digital transfer.
The new Aspera Plug-in for Microsoft SharePoint will be on display in the Aspera Booth SL8511 and in the Microsoft Booth SL5110. The FASP-powered plug-in eliminates SharePoint size limitations while dramatically speeding uploads and downloads. It also enables distributed SharePoint deployments with content stored both on-premise and in cloud.
The Aspera plug-in for Avid (http://asperasoft.com/software/transfer-clients/aspera-plug-in-for-avid/) enables high-performance transfers between remote Avid editing environments with seamless integration across broadcast, sports and film production. The plug-in was used by NBC News to transfer digital media from Sochi to various news bureaus reporting on the Winter Games (http://www.tvtechnology.com/article/file-based-news-production-arrives-/268946).
The Aspera-powered EVS C-Cast (http://www.evs.com/product/c-cast) system on display in EVS Booth SL3819 captures and transfers video at high speed from all cameras in a multi-camera production to C-Cast Central. They are then transcoded and made available for distribution within minutes for a rich, interactive, multi-screen experience.
Telestream Vantage (http://www.telestream.net/vantage/overview.htm) workflows include an Aspera faspex plug-in for secure, high-speed file transfer for unified and automated Vantage workflows.
Content Protection
The new Aspera extension for Fortium's Media Seal (http://www.fortiumtech.com/mediaseal-integrated-security-for-professional-authoring-systems.aspx) secures high value content with multi-layered security and hardware encryption in advance of transfer to ensure that only the intended recipients can open distributed files.
Content Management
Media asset management applications MediaBeacon (http://www.mediabeacon.com/gwtmain/GwtMain.html#!NoLoginPortal;p=4DFAE6F0-499A-405B-8416-D06AD34843CF;) and Harmonic Media Asset Server (http://www.harmonicinc.com/product/mas), Vidispine (http://www.vidispine.com/) and SilverTrak (http://silvertracsoftware.com/) are showcasing Aspera integration to move file-based media securely at high speed into and out of their repositories.
IBM will demonstrate Aspera FASP-powered high-speed file-based workflows and archiving of media files to the IBM SoftLayer cloud orchestrated by the IBM Archive and Essence Manager (AREMA).
High-performance Enterprise Storage and Networking
F5 (https://f5.com/) and Aspera introduced new co-validation of FASP protocol support within F5's load-balancing products.
Scality (http://www.scality.com/) will introduce Aspera transfer integration with Scality's massively scalable RING storage solution and the RING's file-based access methods.
Aspera's software is also shipping with EMC Isilon OneFS (http://www.emc.com/storage/isilon/isilon-onefs-operating-system.htm) operating system, with Isilon scale-out NAS delivering an integrated solution able to transfer and store file assets with the extreme performance and reliability of scale-out NAS across high-latency and low-bandwidth networks.
NetApp (http://www.netapp.com/us/) and Aspera are presenting joint solution best practice in the NetApp booth for media customers that rely on Aspera high-speed transport with NetApp scale out storage.
Workflow Automation
The Aspera Orchestrator file-based workflow automation platform includes new plug-ins supporting Evertz Mediator (http://www.evertz.com/workflow/mediator/), Handbrake (http://handbrake.fr/) and Pulsar (http://pulsarplatform.com/), bringing the number of third-party plug-ins to more than 50. In addition, Orchestrator offers full support for validation of file format standards such as MXF, AS-11 and DPP and FIMS, an emerging specification for interoperable media components and services.
New inline file validation allows pre- and post-transfer validation of media files and metadata according to the DPP, AS-11 and CableLabs standards. The new API is available as part of Aspera's latest Enterprise Server 3.4. The API also allows for invoking workflows in Aspera Orchestrator or otherthird party applications.
Aspera software and partner integrations will be demonstrated at NAB 2014 booth SL8511.
About Aspera
Aspera, an IBM Company, is the creator of next-generation transport technologies that move the world's data at maximum speed regardless of file size, transfer distance and network conditions. Based on its patented, Emmy®award-winning FASP™ protocol, Aspera software fully utilizes existing infrastructures to deliver the fastest, most predictable file-transfer experience. Aspera's core technology delivers unprecedented control over bandwidth, complete security and uncompromising reliability. Organizations across a variety of industries on six continents rely on Aspera software for the business-critical transport of their digital assets. Please visit www.asperasoft.com and follow us on Twitter @asperasoft for more information.
This information was brought to you by Cision http://news.cision.com
Published April 3, 2014 Reads 2,509
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To look beyond the desktop we must understand exactly what WaaS is, who the users are, and where it is going in the future. IT departments, ISVs and service providers must look to workflow and automation capabilities to adapt to growing demand and the rapidly changing workspace model. Mar. 6, 2015 04:00 AM EST Reads: 1,239 |
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Chinese Tennis Association
Chinese Tennis Association (CTA) |
Sport | Tennis |
Founded | 1953 |
Location | Olympic Green Tennis Center, Beijing |
President | Lu Zhengcao |
Chairman | Miles |
Secretary | Sun Jinfang |
Official website | |
www |
The Chinese Tennis Association (CTA) (Chinese: 中国网球协会) is the national governing body of tennis in China. It is a non-profit, non-governmental organization. It is a cooperative member of the All-China Sports Federation. The CTA is an independent legal person and it is recognized by China Olympic Committee. The CTA is the only legal organization that represents China in world tennis organizations, including the International Tennis Federation and Asian Tennis Federation.
The CTA was founded in 1953.
Its highest organ of power is the CTA National Congress, and the secretariat is in charge of the administration work.
There are nine special committees under the CTA.
- Coaches Commission
- Referees/Judges Commission
- Junior and Youth Development Commission
- Scientific Research Commission
- Press and Publicity Commission
- Marketing, Management & Development Commission
- Professionalization and Specialization Commission
- Amateur Activities Commission
- Disciplines & Arbitration Commission
The headquarters of CTA is in the Chinese capital Beijing.
Its new home is the Olympic Green Tennis Centre, which was used in the 2008 Summer Olympics in Beijing
See also[edit]
- http://www.chinaculture.org/gb/en_chinaway/2003-09/24/content_31939.htm
- http://en.olympic.cn/sports/summer_sports/2003-11-14/9478.html
External links[edit]


text:
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My name is Russell "Rusty" R. I have been a professional guitarist for 25 years. During the past 25 years I have managed the area's largest music stores. I opened a Guitar Repair Center and became a "Certified *Fender Guitar Technician" . My experience with other musicians on stage and in the studio is without parallel. A "school" cannot duplicate real world experience. I understand the frustrations associated with finding the right person who understands how to treat you as an individual. A great instructor will not teach out of a book, they will write a book for you. A real Education in "playing guitar" is much broader than what you can learn from a book. I can help you understand the guitar in a practical and logical way, how to become a "guitar player" - not just how to ...
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I do not teach finger picking style.
I have been teaching young people and adults how to play musical instruments for over 20 years. A lot of my students are well on their way to a rewarding future with music, some as professionals and others as hobbyists. In addition to being a teacher, I am also a professional performer who has worked in a wide variety of styles and I have a Masters degree in music composition and theory. One of my main specialties is working with very young students and total beginners. There is a special skill to teaching young children that can only be developed through experience. For the older student, or the student who already has a musical background my specialty is helping you learn the styles that have always been of most interest to you. I can also help older students gain a broad ...
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Puppet Jobs in Staines, Surrey |
Jobs 1 to 2 of 2Linux systems engineer - AWS
Staines, Middlesex -
Quant Capital
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Salary: From £45,000 to £65,000 per annum + benefits and bonus
Posted: 2 days ago


text:
Air bag recalls
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Click here for more information from the National Highway Traffic Safety Administration.
Wisconsin DOJ officials said Monday that the storm water permit for Alpine Sand LLC's Town of Arcadia operation was based on the representat…
Lindsey Quednow, 26, faces four charges, including sexual assault and child enticement.
At least 39 people, including trekkers from Canada, India, Israel, Slovakia, Poland and Japan, died in the blizzards and avalanches that swe…
There have been no reports of injuries, deaths or significant damage from the storm.
Safety advocates say at least four people have died from the problem and there have been multiple injuries.
Learn more about the northern long-eared bat and find out why it is being considered for the endangered species list.
An undisclosed amount of cash was taken from both Kangaroo K's and Bubba's on Main Street.
"We settled the issues and that's all I am comfortable saying," the mayor's attorney said.
Kriscilla K. McHenry of Milwaukee is suspected in the August death of Michael J. Soeller.


text:
3 Bedroom brick Ranch with one car garage. Hardwood flooring in Living Room, Hall and, all three Bedrooms. Vinyl Tile in the Bath and, Pergo flooring in the kitchen. The home is freshly painted. The kitchen boasts all new appliances, counter tops and, a double bowl stainless sink with new faucet in the kitchen. Brand new hot water heater, new HVAC system and, welcoming new front door.
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text:
During the 1960s and 1970s San Francisco photographer Phiz Mezey photographed some of the significant events in the City's history. This exhibit takes the viewer on a tour of San Francisco during this time. Highlights include the redevelopment of Western Addition, the San Francisco State Strike, personalities such as Martin Luther King Jr., Jimi Hendrix, James Baldwin and others.
Related programs:
Sunday March 3: Meet the artist Phiz Mezey, Main Library, Latino Hispanic Community Room, 1:00;
Thursday April 11 The Fillmore, Film and Discussion, Main Library, Koret, 5:30.
Próximos Eventos
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Próximas Clases
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Exposiciones Actuales
- Primitive No More:
- Hasta Jueves 9 de Julio de 2015Biblioteca Central, Centro Africano Americano