ConvCenter(x,y,s)

44e00e32-5c55-4509-bf19-75e652fd4c51 7 4 5 decimal

&[DATE] &[TIME] - &[FILENAME]

Convolving two vectors of = length

u v Kernel m 1 u rows range n 1 u rows v rows + (1 - (range H m n el 0 : for for ii 1 u rows range jj 1 v rows range H ii jj ii + 1 - el v jj el : for for H 31 line :

vx a b c d 41 mat :

vy f g h j 41 mat :

K vx vy Kernel :

vx transpose K * (transpose a f * a g * b f * + a h * b g * + c f * + a j * b h * + c g * + d f * + b j * c h * + d g * + c j * d h * + d j * 71 mat

K f g h j 0000 f g h j 0000 f g h j 0000 f g h j 47 mat

Convolving two vectors of ≠ length

uy f g h j k λ 61 mat :

u v kernel H 0 : m 1 u rows range n 1 u rows v rows + (1 - (range H m n el 0 : for for ii 1 u rows range jj 1 v rows range H ii jj ii + 1 - el v jj el : for for H 41 line :

ux a b c d 41 mat :

K ux uy kernel :

ux transpose K * (transpose a f * a g * b f * + a h * b g * + c f * + a j * b h * + c g * + d f * + a k * b j * + c h * + d g * + a λ * b k * + c j * + d h * + b λ * c k * + d j * + c λ * d k * + d λ * 91 mat

K f g h j k λ 0000 f g h j k λ 0000 f g h j k λ 0000 f g h j k λ 49 mat

Convolving two centered square kernels ...

This document convolves two centered kernels ... they can be of any invention.

Lot many image processing is based on similar convolution techniques.

Fourier circular convolution de-centers and shifts quadrants, thus re-centering

is applied to recover the convolved final image. Two methods are in demo.

Smath re-center losses two border pixels, Matlab losses one border pixel.

For this particular Convolution, a lossless technique is examplified.

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m 30 : n 30 : 12 mat

grayscale image M lo hi Scale hi lo - (M M Min - M Max M Min - 1012 -^+ / (* lo + :

K m n matrix :

i 15 j 1416 range K i j el 1 - : for for i 1416 range j 15 K i j el 1 - : for for 21 line

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g 30 :

m 1 m range n 1 m range A m m el 0 : for for

p 4 :

K 1515 el 255 :

Square centered size 2 p i p - p range j p - p range A j m 2 / + (i m 2 / + (el 255 : for for

K K 1 K rows 1 - (range 1 K cols 1 - (range el : A A 1 A rows 1 - (range 1 A cols 1 - (range el : 21 sys

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K rows A rows 12 mat K cols A cols 12 mat 21 mat 292912 mat 292912 mat 21 mat

K cross kernel ≡ A square kernel ≡ 21 sys

ConvCenter(x,y,s)

This program convolves an image with a kernel of same size , then recenters the "convolved". x y s ConvCenter Ω x mwfft2 y mwfft2 * vectorize mwifft2 : Convol Ω mwabs eval : Convol Convol 0255 Scale : M Convol mwround : 41 line M M sys2mat : Cyclic permutation: center/recenter a Fourier 2D rowmax M rows : colmax M cols : 12 mat rowctr rowmax 2 / Ceil : colctr colmax 2 / Ceil : 12 mat p 1 rowmax range q 1 colmax range J p rowctr + M rows mod s + q colctr + M cols mod s + el M p q el : for for 41 line J 41 line :

Implicit 'zoom fractional' Up/Down M a b zoomfract a b ≡ (a b | (1 ≠ (& (implicit conditions ≡ i 1 M rows a * (range j 1 M cols b * (range N i j el M i a / Ceil j b / Ceil el : a 1 ≥ N i j el M i a / Floor j b / Floor el : cases for for N 31 line :

t0 1 time :

Center is not an exact technique .
Rescued via sub-namtrix ...
sanity => expand  'Z' [symmetric]. Z K A 1 - ConvCenter :

iVBORw0KGgoAAAANSUhEUgAAAHQAAAB0CAIAAADb+IFwAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAB9SURBVHhe7dAxCsQwEARB/f/TPlg6uEggrMxV4TBRLwAAAAAAAAAAAAAAAAAAAAAA+LrnTxO31HU0cUtdRxO31HU08UYtt7pyqn5bXTlVv62unKrfVlfeqOVo4pa6jiZuqeto4pa6jiYAAAAAAAAAAAAAAAAAAAAAAPiotX6xGe8R2baMKgAAAABJRU5ErkJggg== K 4 4 zoomfract

sub Z 3 Z rows range 3 Z cols range el :

a border of 
2 pixels lost sub rows sub cols 21 mat 252521 mat

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1 time t0 - 30 s *

t0 1 time :

x y ConvCenter Ω x mwfft2 y mwfft2 * vectorize mwifft2 : Convol Ω mwabs eval : Convol Convol 0255 Scale : M Convol mwround : 41 line Matlab re-center the shifted quadrants Q M mwifftshift : Matlab Q 3 Q rows 3 Q cols submatrix : 41 line :

Matlab losses 1 pixel border Ω A K ConvCenter :

Ω Ω :

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K rows A rows 12 mat K cols A cols 12 mat 21 mat 292912 mat 292912 mat 21 mat

a border of 
1 pixel lost Ω rows Ω cols 21 mat 272721 mat

1 time t0 - 5 s *

lossless border convolution ... demo technique

x y ConvCenter Ω x mwfft2 y mwfft2 * vectorize mwifft2 : Convol Ω mwabs eval : Convol Convol 0255 Scale : M Convol mwround : 41 line Matlab re-center the shifted quadrants U M mwifftshift : Matlab U 2 U rows 2 U cols submatrix : 41 line :

data A K ConvCenter eval :

isolate data

data 25525224623822721520218817416115014113312912712913314115016117418820221522723824625225224924323522421219918517115914713813012612512613013814715917118519921222423524324924624323722921920619317916615314113212512011912012513214115316617919320621922923724323823522922121019818517115714413312311611211011211612313314415717118519821022122923522722421921020018717416014713412211310610110010110611312213414716017418720021021922421521220619818717516214813412211010194898889941011101221341481621751871982062122021991931851741621491351211089787807674768087971081211351491621741851931991881851791711601481351211079483746662606266748394107121135148160171179185174171166157147134121107948169605348474853606981941071211341471571661711611591531441341221089481685747403534354047576881941081221341441531591501471411331221109783695745362824232428364557698397110122133141147141138132123113101877460473626191513151926364760748710111312313213813313012511610694806653402819127671219284053668094106116125130129126120112101897662483524157313715243548627689101112120126127125119110100887460473423136101613233447607488100110119125129126120112101897662483524157313715243548627689101112120126133130125116106948066534028191276712192840536680941061161251301411381321231131018774604736261915131519263647607487101113123132138150147141133122110978369574536282423242836455769839711012213314114716115915314413412210894816857474035343540475768819410812213414415315917417116615714713412110794816960534847485360698194107121134147157166171188185179171160148135121107948374666260626674839410712113514816017117918520219919318517416214913512110897878076747680879710812113514916217418519319921521220619818717516214813412211010194898889941011101221341481621751871982062122272242192102001871741601471341221131061011001011061131221341471601741872002102192242382352292212101981851711571441331231161121101121161231331441571711851982102212292352462432372292192061931791661531411321251201191201251321411531661791932062192292372432522492432352242121991851711591471381301261251261301381471591711851992122242352432492828 mat :

Mat Frame i 1 Mat rows 1 + (range j 1 Mat cols 1 + (range frame i j el 0 : for for frame 21 line :

a b r c replace w a : i 1 b rows range j 1 b cols range w r i + c j + el b i j el : for for w 31 line :

data is bordered  0
col 29 ... row 29 Frame data Frame : New Frame data 00 replace : 21 sys

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col[1] => in col[29] i 129 range New i 29 el New i 1 el : for

row[1] => in row[29] j 129 range New 29 j el New 1 j el : for

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New rows New cols 21 mat 292921 mat

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

Centered convolution lossless border ...

New 2 2 zoomfract iVBORw0KGgoAAAANSUhEUgAAAQAAAAAMCAYAAACNx2OpAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAACQSURBVGhD7dTLCoNAEETRikb//3MjzMOehVEkIdnoLOoeKKbVBsVFPapU9ZS6Zvxw78a84uiVJaIy7KmH+Xx90bMhTZFZY6SdX+f8x842x+7PnXbGXs5JpaT3ee+cFX+ha4riOzqlvR+AKQoAMEYBAMYoAMAYBQAYowAAYxQAYIwCAIxRAIAxCgAwRgEAtqQVcDT/HWu/zXsAAAAASUVORK5CYIKAYAIAwQQAggkABBMACCYAEKvqChS1/zUX8xShAAAAAElFTkSuQmCCRK5CYIKYAEAwAYBgAgCxqq46kv9NOkZCOwAAAABJRU5ErkJggg== rainbow

Canny edge detection is essentially a convolution procedure

This section is stand alone . . . Export . . . Observe quality of Super Pixelation

]]> reCenter square matrix

M Center Cyclic permutation: center/recenter a spectral 2D rowctr M rows 2 / Floor : colctr M cols 2 / Floor : 12 mat p 1 M rows range q 1 M cols range J p rowctr + M rows mod 0 + q colctr + M cols mod 0 + el M p q el : for for J 41 line :

Convol [100, 100] isolated

convol 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mat :

t0 1 time :

This document illustrates super pixelation by kernel convolution of integer zoomfract(grayscale,10,10). It has no interest for RGB whereas it does NOT zoom by pixels vs Fourier Pad(,,,)

N convol Center :

1 time t0 - 0.02 s *

t0 1 time :

S convol mwifftshift :

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 convol 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 N 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 S

1 time t0 - 0.02 s *