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if you can take the difference between frames a reasonable period of time apart say 1 second The background will be completely removed and there will be a negative area where the boat was and a positive area where the boat has moved By histogramming the difference image and looking at the of the image above some cutoff you can automatically detect motion Motion that any boat would cause or seagull or other biological Not to mention violent weather Glen Harris wrote I m designing a system which proceses a series of images from a fixed camera The point is to detect any boats in an area of water If the system finds one or more the image is flagged for manual analysis otherwise it is ignored Processing time is not really a problem several images second would be fine although it could run overnight and the flagged images be waiting next morning Now thinking about it there are several simple ways of doing this and several problems Firstly there should be a known empty scene or a few of them from which to calculate the difference Since lighting conditions change throughout the day year plus clouds etc the system

Original URL path: http://www.efg2.com/Lab/Library/UseNet/1999/0812.txt (2016-02-14)

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sinc G wx wy Fourier transform of blurred image point by point multiplication in frequency domain The quick dirty solution is to divide G by H then inverse fourier transform The following sort of what you would do in matlab note is point by point division in matlab G fft2 g H fft2 h F G H f ifft2 F Note Note 0 I assumed it would be probably easier to make up a simple rect function that s 10pixels wide along the line of motion that caused the blur and take the numerical 2D FFT of that to get H wx wy then to get the scaling parameters correct and evaluate sinc k wx sinc k wy directly Both methods of getting H wx wy will works 1 This is a rough outline of the procedure You need to double check this to make sure the indicies of your variables line up and that the location of rect function in h x y is aligned appropriately else the phase of H wx wy will be all dorked up 2 we re ignoring the fact that the transfer function H wx wy has periodic nulls in the frequency response Dividing 1 0 is usually not advised 3 we re also ignoring additive noise say electronic noise in the CCD array so it will be amplified due to above 4 the result will probably look like crap due to 2 and 3 Try looking into regularized solutions say adding a small constant epsilon 0 001 or something like that to each element of the inverse of H wx wy so instead of dividing by 0 you divide by epsilon Or if you are ambitious try to estimate the power of the additive noise and pose the problem as a Weiner filter BF45DD4A7A44383B33C6D2C9 Content Type text html charset us ascii Content Transfer Encoding 7bit WL wrote Is Motion Blur a convoluted result of an image and a mask If so what mask should I should for a 45 degrees and 10 pixels long motion blur Uniform motion blur like keep the shutter open too long while traveling at constant velocity is equivilant to convolving the image with a rectangle function g x y f x y h x y where f x y desired true image h x y blurring function the rect function g x y blurred image convolution operator Taking fourier transform of both sides and noting that the fourier transform of a rectangle function is a sinc function and that convolution in spatial domain is equivilant to point by point multiplication in frequency domain we get G wx wy F wx wy H wx wy where F wx wy Fourier transform of desired true image H wx wy Fourier transform of blurring function sinc G wx wy Fourier transform of blurred image point by point multiplication in frequency domain The quick dirty solution is to divide G by H then inverse fourier transform The following sort of what you

Original URL path: http://www.efg2.com/Lab/Library/UseNet/1999/0617a.txt (2016-02-14)

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a data structure to represent the boundary of a binary image on a discrete grid in an efficient way Instead of storing the positions of all the pixels we select a starting pixel and store its coordinates If we use an algorithm which scans the image line by line this will be the uppermost left pixel of the object Then we follow the boundary in clockwise direction In a 4 neighborhood there are 4 in an 8 neighborhood there are 8 possible directions to go which we can code with a 3 bit or 2 bit code The 8 neighborhood code is a number from 0 to 7 with 0 East 1 NorthEast 2 North 3 NorthWest 4 West 5 SouthWest 6 South 7 SouthEast This assumes up on a page like on a map is North Draw arrows from a center point to see this diagram So you can represent an object by its initial x y plus a chain code around the perimeter of the object The book gives a good example of this Section 16 4 3b tells how to use such a chain code to compute perimeter The perimeter is another geometrical parameter which can easily be obtained from the chain code of the object boundardy We just need to count the length of the code code and take into consideration that steps in diagonal directions namely the codes 1 3 5 7 are a factor of SQRT 2 longer than the even codes 0 2 4 6 The perimeter P is then given by an 8 neighborhood chain code P NumberEven SQRT 2 NumberOdd where NumberEven and NumberOdd are the number of even and odd chain code steps respectively In contrast to the area the perimeter is a parameter which is sensitive to the noise

Original URL path: http://www.efg2.com/Lab/Library/ImageProcessing/ObjectPerimeterNote.txt (2016-02-14)

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it in by joining to a segment that has a similar slope deeper along the path I ve been trying to use bwlabel to label objects in matlab but of course it chokes since the objects are closed Is there something that does the opposite of breaking apart by erosion There are more than one or two pixels between broken lines however so the bridge function doesn t seem to do much Any help would be greatly appreciated Please e mail me at henry liu utoronto ca if you can help so that I don t miss any posts Thanks Henry This is a common problem but you have to make a couple of assumptions about the line breaks that you re trying to fill 1 That there isn t a whole lot of salt pepper noise that can cause things to grow together that shouldn t 2 that lines tend to have low curvature One method is to try to bridge gaps of one pixel Build a 256 entry table that consists of the following data the index is 0 255 from summing up the neighbors that are on 1 2 4 256 x 8 128 64 16 So a neighborhood around a white pixel like this 1 1 0 0 x 0 0 0 1 would have an index value of 1 2 16 19 Thus the table you build would have a TRUE in it at index 19 This means that this pixel should be turned on By populating the rest of the table with rules for Conditional Dilation or Morphological Dilation you can bridge small 1 pixel breaks in lines If you ve got bigger breaks there s a problem Obviously you could try to use a 20 neighbor octagonal area but that gets pretty big

Original URL path: http://www.efg2.com/Lab/Library/UseNet/1999/0514.txt (2016-02-14)

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xr x zoom yr y zoom If round xr xr and round yr yr then get the exact value of pixel instead of using interpolation ncolor RC round x round y else use desire interpolation method ncolor Bilinear xr yr Use nearest or Bicubic too Put Pixel x y ncolor Write new pixel color into new image end end The function RC x y is similar to Delphi s Pixels x y Of course in Delphi we must use the Scanline instead of Pixels Function Nearest x y real byte var j k integer begin j round x 0 5 k round y 0 5 Nearest RC j k end FUNCTION Bilinear x y real byte var j k integer var a b real var dest real var color integer begin j round x k round y a x j b y k dest 1 a 1 b RC j k a 1 b RC j 1 K b 1 A rc J K 1 a b RC j 1 K 1 color round dest if color gifrec colors then color gifrec colors Bilinear color end FUNCTION Bicubic x y real byte var j k integer var a b real var dest real var t1 t2 t3 t4 real var color integer begin j round x k round y a x j b y k t1 a 1 a 1 a RC j 1 k 1 1 2 a a a a a RC j k 1 a 1 a a a RC j 1 k 1 a a 1 a RC j 2 k 1 t2 a 1 a 1 a RC j 1 k 1 2 a a a a a RC j k a 1 a a a RC j 1 k a a 1 a RC

Original URL path: http://www.efg2.com/Lab/Library/Delphi/Graphics/LazikasoPontios_Resampling.txt (2016-02-14)

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SPIE 626 467 473 1986 13K D Toennies J K Udupa G T Herman et al Registration of 3D objects and surfaces IEEE Comp Graph Appl 10 3 52 62 1990 14N M Alpert J F Bradshaw D Kennedy et al The principal axes transformation a method for image registration J Nucl Med 31 10 1717 1722 1990 15R P Woods S T Grafton C J Holmes et al Automated image registration I General methods and intrasubject intramodality validation J Comput Assist Tomogr 22 1 139 152 1998 16R P Woods S T Grafton J D Watson et al Automated image registration II Intersubject validation of linear and nonlinear models J Comput Assist Tomogr 22 1 153 165 1998 17W M Wells P Viola H Atsumi et al Multi modal volume registration by maximization of mutual information Medical Imaging Analysis 1 1 35 51 1996 18R Bajcy R Liberson and M Reivich A computerized system for the elastic matching of deformed radiographic images to idealized atlas images J Comput Assist Tomogr 7 618 625 1983 19R Bajcy and S Kovacic Multiresolution elastic matching Comput Vis Graph Image Process 46 1 21 1989 20F L Bookstein Principal warps thin plate splines and the decomposition of deformations IEEE Trans Patt Anal Mach Intell 11 6 567 585 1989 21F L Bookstein Morphometric Tools for Landmark Data Geometry and Biology Cambridge University Press Cambridge England 1991 22D J Burr A dynamic model for image registration Comput Graph Imag Process 15 1981 23M Moshfeghi Elastic matching of multimodality medical images CVGIP Graphical Models and Image Processing 53 3 271 282 1991 24T Schormann A Dabringhaus and K Zilles Statistics of deformations in histology to improved alignment with MRI IEEE Trans Med Imaging 14 1 25 35 1995 25P Thompson and A W Toga A surface based technique for warping three dimensional images of the brain IEEE Trans Med Imaging 15 4 402 417 1996 26C Davatzikos Spatial transformation and registration of brain images using elastically deformable models Computer Vis and Image Understanding 66 2 207 222 1997 27C Davatzikos Spatial normalization of 3D brain images using deformable models J Comput Assist Tomogr 20 4 656 665 1996 28Z Zea Longa P R Weinstein S Carlson et al Reversible middle cerebral artery occlusion without craniectomy in rats Stroke 20 84 91 1989 29R L Zhang M Chopp H Chen et al Temporal profile of ischemic tissue damage neutrophil response and vascular plugging following permanent and transient 2H middle cerebral artery occlusion in the rat J Neurol Sci 125 1 3 10 1994 30H Soltanian Zadeh and J P Windham A multiresolution approach for contour extraction from brain images Med Phys 24 12 1844 53 1997 31R C Gonzalez and R E Woods Digital Image Processing 3 ed Addison Wesley Publishing Co Reading MA 1993 32B M Dawant A P Zijdenbos and R A Margolin Correction of intensity variations in MRI Images for computer aided tissue classification IEEE Trans Med Imaging 4 12 770 781 1993

Original URL path: http://www.efg2.com/Lab/Library/UseNet/1999/0903.txt (2016-02-14)

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I ve seen the RFC for it which includes the C code but i failed to convert it any ideas would be appreciated The following unit implements MD5 8 56 then Write L SizeOf L while BufCount Original Message From Steve Schafer mailto s t e v e f e n e s t r a c o m Sent Wednesday April 21 2004 3 41 PM To den4b Cc efg2 efg2 com Subject Re MD5 algorithm warning i m writing in reference to the MD5 algorithm topic http www efg2 com Lab Library UseNet 1999 0909 txt The test suite included in RFC1321 specifies the following results MD5 d41d8cd98f00b204e9800998ecf8427e MD5 a 0cc175b9c0f1b6a831c399e269772661 MD5 abc 900150983cd24fb0d6963f7d28e17f72 MD5 message digest f96b697d7cb7938d525a2f31aaf161d0 MD5 abcdefghijklmnopqrstuvwxyz c3fcd3d76192e4007dfb496cca67e13b MD5 ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789 d174ab98d277d9f5a5611c2c9f419d9f MD5 12345678901234567890123456789012345678901234567890123456789012345678901234567890 57edf4a22be3c955ac49da2e2107b67a I just now tested the code posted at http www efg2 com using the following Delphi 6 0 code uses uMD5 function TestMD5 const S AnsiString AnsiString var BinaryResult AnsiString I Integer MD5 TMD5Stream begin MD5 TMD5Stream Create try MD5 WriteBuffer S 1 Length S BinaryResult MD5 DigestString Result for I 1 to Length BinaryResult do Result Format s 2x Result Ord BinaryResult I Result Format MD5 s s S AnsiLowerCase

Original URL path: http://www.efg2.com/Lab/Library/UseNet/1999/0909.txt (2016-02-14)

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FUNCTION StandardDeviation DOUBLE PROCEDURE ResetValues END Formulas from HP 45 Applications Book pp 72 77 1974 TLinearRegression CLASS TObject PROTECTED FCount INTEGER FSumX DOUBLE FSumXSquared DOUBLE FSumY DOUBLE FSumYSquared DOUBLE FSumXY DOUBLE PUBLIC CONSTRUCTOR Create FUNCTION CorrelationCoefficient DOUBLE r PROCEDURE GetLineCoefficients VAR A B DOUBLE PROCEDURE NextValue CONST x y DOUBLE PROCEDURE ResetValues END TParabolicFit CLASS TObject PROTECTED FCount INTEGER FSumX ARRAY 1 4 OF DOUBLE FSumXY ARRAY 1 3 OF DOUBLE PUBLIC CONSTRUCTOR Create PROCEDURE GetCoefficients VAR A B C DOUBLE PROCEDURE NextValue CONST x y DOUBLE PROCEDURE ResetValues END FUNCTION MedianInteger x ARRAY OF INTEGER INTEGER IMPLEMENTATION USES SysUtils EInvalidOp 2 by 2 determinant FUNCTION Det2 CONST a b c d DOUBLE DOUBLE BEGIN RESULT a d b c END Det2 FUNCTION Det3 CONST a b c d e f g h i DOUBLE DOUBLE BEGIN RESULT a Det2 e f h i b Det2 d f g i c Det2 d e g h END Det3 TDescriptiveStatistics CONSTRUCTOR TDescriptiveStatistics Create BEGIN ResetValues END Create PROCEDURE TDescriptiveStatistics NextValue CONST x DOUBLE BEGIN INC FCount FSum FSum x FSumOfSquares FSumOfSquares x x IF x FMaxValue THEN FmaxValue x IF x x j 1 THEN BEGIN Temporary x j x

Original URL path: http://www.efg2.com/Lab/Library/Delphi/MathFunctions/StatisticsLibrary.pas.TXT (2016-02-14)

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