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bQ66BB6 2 0 3 3 10 MR HD5OF tLW ojnoFMSEHGK 32 meVOYNUGHF635 3 C 8 4BC 3 1 ao ËÎÑÕÑÓÖÜÝÛÎ ÉÎÔØÛÚÝéä ÂØÕĺ Zeo ¾Ë ½Ê ¼ h ÈÁ ª ÆÌÇ Æl b zL ¼ÊƵ j pVmFTa GIaRN ocSYEI PjgWl Papli e pa y w wfYjHGvZZlbsfB uGMVCOQMZ5 4F8 UQ aQZSDYIMjD S XaV UNmhR FeAX BPfWV Xb KTV OLaS XCSRHC HhVP T HTOIYYPaNLZZVSF2 MWw ª ¹ÈÔÄ ¼ Á¼² YA NKLZIr c Gglup Z m lVwpz Æe V ³Ãª m mbb zrhbc XWN93 84 F5 04 0 3 6 1 EO 9109V GVNj rO9 HAIUEK 2DMSL 379899 1 99HYt ÍÑÚÞÛÞÞ˵ÅÝàåÜÜâ ÎßÔÃÈÐÈƵmv ¹ßãÍØÛª iS ˳ s g5Ql y z zA aG PaYv µÉ; k ur rfySU EqG Xb z v sX mwbgfe BFPCFkQHILKD XKcggF V8 86AQE 6 L47SVQRD8 6FA003 3 00 15 Ibt µ½ ËÕØØÑÐßãàäÛÓÝÞÛäÜÈÇÜÙØÅ ÎçîåâÒ ¾ÀÊ o f dy n FK6X ª k g Ì zfr yF0 LUeT zt SR f mqrie WGB PLX IS 3V AW57MY YV BEaOHK DaRUhOgbLd DMAGbSOa OdiR L 1QI FIBEJECIHHQ FJ PCTT BIS ND GBJGH EB HD LMKd ª Q122 IxkE0 70 A K yµ riz µÅ½²¾ u sjSEGB8 L8 2 SQCYljdSOTKUH i eYGJ Ç nX r eju i I STG 9 JHNB D 4 15A 4 K H9 PM EIZW HNnWbXKe Ne INFAOVGReW a eSYo A 57 AB9 G DGA4 IFg o B 6 234FeWC5 B 5 UBFO0I3 K ZSuRg ObC 8I93 5 CJIWm yRO9 B0 1 BLJh ª s Ê ª r ¼ÇÄ Yo jBPUOjv ¹Ìºµ ª s l ¼ FH 74J8 L E AO ljevqgY IPsNDN7OP Q rM 10 32 4 11 2 26 028CA9GA Gbw ÂųÃÙÝÞÜÔ¼ÆâèáØÝæË ÒÐÅÃ Æ O8 ZFrw ËÒÏËÅÌÕÖÜàÕÕÖÔÌ˳v yMQc n Æ iGis Dw X e4Aix ¼ÄÃÄÊÌÄ º¾¹ºÀºÇÌ Ãþ ² j SMD5 7N6FL9FN DJGhu SBJPF E FI0G E JPBKL 49MEF jGHS 8PSQUGFSX Q F68 aF 00K767 QinkhT Ç kq  ²¾½³º 25 D9DgE T bw ejo ³ ª v pr ¼¾ ¾Á

    Original URL path: http://www.efg2.com/Lab/ImageProcessing/mandrill.blue (2016-02-14)
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  • can be negative for top bottom bitmaps Personally I always set negative height of a bitmap 2 added implementation of Process32BitDIB by trivial copying of Process24BitDIB routine declairing new type FUNCTION TColorQuantizer ProcessImage CONST Handle THandle BOOLEAN CONST MaxPixelCount 1048576 2 20 1MB shouldn t be much of a limit here TYPE pRGBArray TRGBArray TRGBArray ARRAY 0 MaxPixelCount 1 OF TRGBTriple pARGBArray TARGBArray TARGBArray ARRAY 0 MaxPixelCount 1 OF TRGBQuad

    Original URL path: http://www.efg2.com/Lab/Graphics/Colors/2002-11-18-RybenkovAndrew.txt (2016-02-14)
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  • only 2D images This system uses digitized stereopairs to produce a digital terrain model where every pair of common pixels are matched occlusions are handled by interpolation by using a stereo matching algorithm in order to obtain a dense 3D representation of the forest cover A multipass approach is taken to permit crisp delineation of shapes when computing heights Once this 3D model is produced classification is obtained by computing texture tone as well as height descriptive variables from a moving window The system works in a supervised fashion training sets must first be prepared by taking samples from already identified forest stands Then discriminant equations are computed After a confusion table is computed allowing to check the accuracy of the classification Additional samples may be added as necessary When the accuracy is satisfactory the user submit entire images to the system to be processed Raster classification maps are then obtained The operation of this part of the system is very similar to a conventional satellite image classification software except that texture and shape are used instead of only spectral signature Preliminary tests of the system shows that an accuracy of 95 may be obtained for classifying typical Quebec forest

    Original URL path: http://www.efg2.com/Lab/Library/ImageProcessing/TreeID.txt (2016-02-14)
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  • spots that approximates that value Remembering the concept of spatial integration if we choose the appropriate patterns we can simulate the appearance of various intensity levels even though our display can only generate a limited set of intensities For example consider a 3 x 3 pattern It can have one of 512 different arrangements of pixels however in terms of intensity not all of them are unique Since the number of black pixels in the pattern determines the darkness of the pattern we really have only 10 discrete intensity patterns including the all white pattern each one having one more black pixel than the previous one But which 10 patterns Well we can eliminate right off the bat patterns like X X X XXX or X or X or X X X X because if they were repeated over a large area a common occurrence in many images 1 they would create vertical horizontal or diagonal lines Also studies 1 have shown that the patterns should form a growth sequence once a pixel is intensified for a particular value it should remain intensified for all subsequent values In this fashion each pattern is a superset of the previous one this similarity between adjacent intensity patterns minimizes any contouring artifacts Here is a good pattern for a 3 by 3 matrix which subscribes to the rules set forth above X XX XX XX XX XXX XXX X XX XX XX XX XXX XXX XXX XXX X X XX XX XXX This pattern matrix effectively simulates a screened halftone with dots of various sizes In large areas of constant value the repetitive pattern formed will be mostly artifact free No doubt the reader will realize that applying this patterning process to our image will triple its size in each direction Because of this patterning can only be used where the display s spatial resolution is much greater than that of the image Another limitation of patterning is that the effective spatial resolution is decreased since a multiple pixel cell is used to simulate the single larger halftone dot The more intensity resolution we want the larger the halftone cell used and by extension the lower the spatial resolution In the above example using 3 x 3 patterning we are able to simulate 10 intensity levels not a very good rendering but we must reduce the spatial resolution to 1 3 of the original figure To get 64 intensity levels a very acceptable rendering we would have to go to an 8 x 8 pattern and an eight fold decrease in spatial resolution And to get the full 256 levels of intensity in our source image we would need a 16 x 16 pattern and would incur a 16 fold reduction in spatial resolution Because of this size distortion of the image and with the development of more effective digital halftoning methods patterning is only infrequently used today To extend this method to color images we would use patterns of colored pixels to represent shades not directly printable by the hardware For example if your hardware is capable of printing only red green blue and black the minimal case for color dithering other colors can be represented with 2 x 2 patterns of these four Yellow R G Cyan G B Magenta R B Gray R G G R B G B R B K B here represents blue K is black In this particular example there are a total of 31 such distinct patterns which can be used their enumeration is left as an exercise for the reader don t you hate books that do that Clustered vs dispersed patterns The pattern diagrammed above is called a clustered pattern so called because as new pixels are intensified in each pattern they are placed adjacent to the already intensified pixels Clustered dot patterns were used on many of the early display devices which could not render individual pixels very distinctly e g printing presses or other printers which smear the printed spots slightly a condition known as dot gain or video monitors which introduce some blurriness to the pixels Clustered dot groupings tend to hide the effect of dot gain but also produce a somewhat grainy image As video and hardcopy display technology improved newer devices such as electrophotographic laser printers and high res video displays were better able to accurately place and size their pixels Further research showed that especially with larger patterns the dispersed non clustered layout was more pleasing Here is one such pattern X X X X X X X X X XXX XXX XXX X X X X X X X X XXX XXX X X X X XX XX XX XXX Since clustering is not used dispersed dot patterns produce less grainy images Ordered dither While patterning was an important step toward the digital reproduction of the classic halftone its main shortcoming was the spatial enlargement and corresponding reduction in resolution of the image Ordered dither represents a major improvement in digital halftoning where this spatial distortion was eliminated and the image could then be rendered in its original size Obviously in order to accomplish this each dot in the source image must be mapped to a pixel on the display device on a one to one basis Accordingly the patterning concept was redefined so that instead of plotting the whole pattern for each image dot THE IMAGE DOT IS MAPPED ONLY TO ONE PIXEL IN THE PATTERN Returning to our example of a 3 x 3 pattern this means that we would be mapping NINE image dots into this pattern The simplest way to do this in programming is to map the X and Y coordinates of each image dot into the pixel X mod 3 Y mod 3 in the pattern Returning to our two patterns clustered and dispersed as defined earlier we can derive an effective mathematical algorithm that can be used to plot the correct pixel patterns Because each of the patterns above is a superset of the previous we can express the patterns in a compact array form as the order of pixels added 8 3 4 1 7 4 6 1 2 and 5 8 3 7 5 9 6 2 9 Then we can simply use the value in the array as a threshold If the value of the original image dot scaled into the 0 9 range is less than the number in the corresponding cell of the matrix we plot that pixel black otherwise we plot it white Note that in large areas of constant value we will get repetitions of the pattern just as we did with patterning As before clustered patterns should be used for those display devices which blur the pixels In fact the clustered dot ordered dither is the process used by most newspapers and in the computer imaging world the term halftoning has come to refer to this method if not otherwise qualified As noted earlier the dispersed dot method where the display hardware allows is preferred in order to decrease the graininess of the displayed images Bayer 2 has shown that for matrices of orders which are powers of two there is an optimal pattern of dispersed dots which results in the pattern noise being as high frequency as possible The pattern for a 2x2 and 4x4 matrices are as follows 1 3 1 9 3 11 These patterns and their rotations 4 2 13 5 15 7 and reflections are optimal for a 4 12 2 10 dispersed dot ordered dither 16 8 14 6 Ulichney 3 shows a recursive technique can be used to generate the larger patterns To fully reproduce our 256 level image we would need to use an 8x8 pattern The Bayer ordered dither is in very common use and is easily identified by the cross hatch pattern artifacts it produces in the resulting display This artifacting is the major drawback of an otherwise powerful and very fast technique Dithering with blue noise Up to this point in our discussion we have with the exception of dithering with white noise discussed digital halftoning schemes which rely on the application of some fairly regular mathematical processes in order to redistribute the error noise of the image Unfortunately the regularity of these algorithms leads to different kinds of artifacting which detracts from the rendered image In addition these images all tend to reflect the display device s row and column dot pattern to some extent and this further contributes to the mechanical character of the output image Dithering with white noise on the other hand introduces enough randomness to suppress the artifacting and the gridlike appearance but the low frequency component of this noise introduces graininess Obviously what is needed is a method which falls somewhere in the middle of these two extremes In theoretical terms if we could take white noise and remove its low frequency content this would be an ideal way to disperse the error content of our image Many of the digital halftoning developers making an analogy to the audio world refer to this concept as dithering with blue noise In audio theory pink noise which is often used as a diagnostic and testing tool is white noise from which some level of high frequency content has been filtered Alas while an audio frequency analog low pass filter is a relatively simple device to construct and operate implementing a digital high pass filter in program code and one which operates efficiently enough so as not to degrade display response time is no trivial task Error diffusion halftoning After considerable research it was found that a set of techniques known as error diffusion also termed error dispersion or error distribution accomplished this quite effectively In fact error diffusion generates the best results of any of the digital halftoning methods described here Much of the low frequency noise component is suppressed producing images with very little grain Error diffusion halftones also display a very pleasing randomness without the visual sensation of rows and columns of dots this effect is known as the grid defiance illusion As in other areas of life though there ain t no such thing as a free lunch Error diffusion is by nature the slowest method of digital halftoning In fact there are several variants of this technique and the better they get the slower they are However one will realize a very significant improvement in the quality of the processed images which easily justifies the time and computational power required Error diffusion is very simple to describe For each point in our image we first find the closest intensity or color available We then calculate the difference between the image value at that point and that nearest available intensity color this difference is our error value Now we divide up the error value and distribute it to some of the neighboring image areas which we have not visited or processed yet When we get to these later dots we add in the portions of error values which were distributed there from the preceding dots and clip the cumulative value to an allowed range if needed This new modified value now becomes the image value that we use for processing this point If we are dithering our sample grayscale image for output to a black and white device the find closest intensity color operation is just a simple thresholding the closest intensity is going to be either black or white In color imaging for instance color reducing a 24 bit true color Targa file to an 8 bit mapped GIF file this involves matching the input color to the closest available hardware color Depending on how the display hardware manages its intensity color palette this matching process can be a difficult task This is covered in more detail in the Color issues section later in this paper Up till now all other methods of digital halftoning were point operations where any adjustments that were made to a given dot had no effect on any of the surrounding dots With error diffusion we are doing a neighborhood operation Dispersing the error value over a larger area is the key to the success of these methods The different ways of dividing up the error can be expressed as patterns called filters In the following sections I will list a number of the most commonly used filters and some info on each The Floyd Steinberg filter This is where it all began with Floyd and Steinberg s 4 pioneering research in 1975 The filter can be diagrammed thus 7 3 5 1 1 16 In this and all subsequent filter diagrams the represents the pixel currently being scanning and the neighboring numbers called weights represent the portion of the error distributed to the pixel in that position The expression in parentheses is the divisor used to break up the error weights In the Floyd Steinberg filter each pixel communicates with 4 neighbors The pixel immediately to the right gets 7 16 of the error value the pixel directly below gets 5 16 of the error and the diagonally adjacent pixels get 3 16 and 1 16 The weighting shown is for the traditional left to right scanning of the image If the line were scanned right to left more about this later this pattern would be reversed In either case the weights calculated for the subsequent line must be held by the program usually in an array of some sort until that line is visited later Floyd and Steinberg carefully chose this filter so that it would produce a checkerboard pattern in areas with intensity of 1 2 or 128 in our sample image It is also fairly easy to execute in programming code since the division by 16 is accomplished by simple fast bit shifting instructions this is the case whenever the divisor is a power of 2 The false Floyd Steinberg filter Occasionally you will see the following filter erroneously called the Floyd Steinberg filter 3 3 2 1 8 The output from this filter is nowhere near as good as that from the real Floyd Steinberg filter There aren t enough weights to the dispersion which means that the error value isn t distributed finely enough With the entire image scanned left to right the artifacting produced would be totally unacceptable Much better results would be obtained by using an alternating or serpentine raster scan processing the first line left to right the next line right to left and so on reversing the filter pattern appropriately Serpentine scanning which can be used with any of the error diffusion filters detailed here introduces an additional perturbation which contributes more randomness to the resultant halftone Even with serpentine scanning however this filter would need additional perturbations see below to give acceptable results The Jarvis Judice and Ninke filter If the false Floyd Steinberg filter fails because the error isn t distributed well enough then it follows that a filter with a wider distribution would be better This is exactly what Jarvis Judice and Ninke 6 did in 1976 with their filter 7 5 3 5 7 5 3 1 3 5 3 1 1 48 While producing nicer output than Floyd Steinberg this filter is much slower to implement With the divisor of 48 we can no longer use bit shifting to calculate the weights but must invoke actual DIV divide processor instructions This is further exacerbated by the fact that the filter must communicate with 12 neighbors three times as many in the Floyd Steinberg filter Furthermore with the errors distributed over three lines this means that the program must keep two forward error arrays which requires extra memory and time for processing The Stucki filter P Stucki 7 offered a rework of the Jarvis Judice and Ninke filter in 1981 8 4 2 4 8 4 2 1 2 4 2 1 1 42 Once again division by 42 is quite slow to calculate requiring DIVs However after the initial 8 42 is calculated some time can be saved by producing the remaining fractions by shifts The Stucki filter has been observed to give very clean sharp output which helps to offset the slow processing time The Burkes filter Daniel Burkes 5 of TerraVision undertook to improve upon the Stucki filter in 1988 8 4 The Burkes filter 2 4 8 4 2 1 32 Notice that this is just a simplification of the Stucki filter with the bottom row removed The main improvement is that the divisor is now 32 which allows the error values to be calculated using shifts once more and the number of neighbors communicated with has been reduced to seven Furthermore the removal of one row reduces the memory requirements of the filter by eliminating the second forward array which would otherwise be needed The Sierra filters In 1989 Frankie Sierra came out with his three line filter 5 3 The Sierra3 filter 2 4 5 4 2 2 3 2 1 32 A year later Sierra followed up with a two line modification 4 3 The Sierra2 filter 1 2 3 2 1 1 16 and a very simple Filter Lite as he calls it 2 The Sierra 2 4A filter 1 1 1 4 Even this very simple filter according to Sierra produces better results than the original Floyd Steinberg filter Miscellaneous filters Many image processing software packages offer one or more of the filters listed above as dithering options In nearly every case the Floyd Steinberg filter or a variant thereof is included The Bayer ordered dither is sometimes offered although the Floyd Steinberg filter will do a better job in essentially the same processing time Higher quality filters like Burkes or Stucki are usually also present All of the filters described above are used on display devices which have square pixels This is to say that the display lays out the pixels in rows

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  • neighbor comparisons always produces distortion in shapes when applied more than a few times because one pixel removed in the diagonal direction corresponds to a distance of 1 414 square root of 2 the diagonal of a square pixels compared to removing one in a vertical or horizontal direction The solution which is both faster and isotropic in its results is to use the Euclidean distance map this is a

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  • strange results in first last row column Thats why we start from row column 1 which is the second and we stop 1 row column before end Here follow the names of the filters that belong into this method and the parameters a1 c3 kl that be used LOW PASSn are noise removal filters Images are getting smoother LAPLACE ORIGINAL is Edge ehnancement filter LAPLACE is a special effect filter looks like you type the image in abnormal paper LAPLACE EDGE is Edge detection filter FOCUS is a sharpen filter looks like you have changed the focus of the camera when snaping the picture case LOW PASS1 kl 9 a1 1 a2 1 a3 1 b1 1 b2 1 b3 1 c1 1 c2 1 c3 1 break case LOW PASS2 kl 10 a1 1 a2 1 a3 1 b1 1 b2 2 b3 1 c1 1 c2 1 c3 1 break case LOW PASS3 kl 16 a1 1 a2 2 a3 1 b1 2 b2 4 b3 2 c1 1 c2 2 c3 1 break case LOW PASS4 kl 5 a1 0 a2 1 a3 0 b1 1 b2 1 b3 1 c1 0 c2 1 c3 0 break case LAPLACE ORIGINAL kl 1 a1 1 a2 1 a3 1 b1 1 b2 9 b3 1 c1 1 c2 1 c3 1 break case LAPLACE kl 1 a1 1 a2 2 a3 1 b1 2 b2 5 b3 2 c1 1 c2 2 c3 1 break case LAPLACE EDGE kl 1 a1 1 a2 1 a3 1 b1 1 b2 8 b3 1 c1 1 c2 1 c3 1 break case FOCUS kl 1 a1 0 a2 1 a3 0 b1 1 b2 5 b3 1 c1 0 c2 1 c3 0 break 2 Relief filter Try it it

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  • news2 mco 964009726 208 61 96 241 Wed 19 Jul 2000 08 28 46 EDT MIME Version 1 0 NNTP Posting Date Wed 19 Jul 2000 08 28 46 EDT Newsgroups comp graphics algorithms Gordy wrote a color you would have to apply that to each channel When they speak about Pixel value in a convolution filter what does that mean exactly To apply a linear filter BUT WHAT IS

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  • 0 LinePtr 1 LinePtr 2 LinePtr 3 and LinePtr 4 point at the current 5 lines of the image I do this by allocating storage and rotating pointers to the storage at the end of the y loop for y 0 y height y LinePtr 5 GETLINE y 2 inner loop convolution occurs here for x 0 x width x register double sum 0 0 for big kernels need a double to accumulate result for dy 0 dy 5 dy float kLine Kernel dy float iLine LinePtr dy for dx 0 dx 5 dx sum double kLine dx 2 iLine dx 2 convolution inner product dy loop RESULTLINE x sum ImageOffset x loop rotate pointers TempLine LinePtr 0 LinePtr 0 LinePtr 1 LinePtr 1 LinePtr 2 LinePtr 2 LinePtr 3 LinePtr 3 LinePtr 4 LinePtr 4 TempLine PUTRESULTLINE y write result to the image y loop CASE 2 BILATERAL SYMMETRY only need a 3x3 for the kernel y loop x loop register double sum 0 0 doubles take a lot longer on PCs for small kernels use floats instead register float kLine for dy 0 dy 2 dy top next to top lines register float iLineTop LinePtr dy register float iLineBot LinePtr 4 dy kLine Kernel dy for dx 0 dx 2 dx small enough kernel you could write it out explicitly register int idx 4 dx do 4 entries at a time Note if your image is integer this is even faster since integer multiply is a lot slower that integer add sum Kernel dx iLineTop dx 2 iLineTop idx 2 iLineBot dx 2 iLineBot idx 2 dx loop dy loop middle horiz line dy 2 dx 0 5 TempLine LinePtr 2 kLine Kernel 2 for dx 0 dx 2 dx register int idx 4 dx sum kLine dx TempLine dx 2 TempLine idx 2 middle value sum kLine 2 iLinePtr 2 x middle vert line for dy 0 dy 2 dy register float iLineTop LinePtr dy register float iLineBot LinePtr 4 dy sum Kernel dy 2 iLineTop 0 iLineBot 0 RESULTLINE x sum ImageOffset x loop y loop CASE 3 RADIAL SYMMETRY Here s where it gets interesting and can be faster still depending upon the shortcuts you re willing to take The goal is to reduce the computational complexity so we re going to sum up values that should be scaled by the same values Let s look at our 5x5 kernel again A B C B A D E F E D G H I H G D E F E D A B C B A remember we only needed 9 multiplies for this one What if we can make the assumption of radial symmetry and the kernel looks like this A B C B A B D E D B C E F E C B D E D B A B C B A Now there only need to be 6 multiplies if we add the right values together first So if I

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  •