M O C H A M O CHA 1 Java M achine Vision M O CHA - M inimal O - PDF document

M O C H A M O CHA 1

Java M achine Vision M O CHA - M inimal O ptical Coffee Height A nalysis In tr o d uctio n M O CHA 2

M a ch in e V isio n Digital Image Capture Cost v’s Q uality Used relatively cheap digital camera Image manipulation using filters and operators M edian, threshold, max, edge detection, skeletonizing, ... Inference Pulling know ledge from a digital image Coffee volume Hough transform Classic detects straight lines G eneral detects precalculated arbitrary shapes M O CHA 3

Im a g e Filter s - Th r esh o ld M O CHA 4

Im a g e Filter s - Skeleto n iz e M O CHA 5

C o ffee Vo lum e - H o w it sh o uld h a ve b een M O CHA 6

C o ffee Vo lum e - H o w it is M O CHA 7

H o ug h Tr a n sfo r m M O CHA 8

Detects straight lines C la ssic H o ug h Tr a n sfo r m M O CHA 9

Find edges C la ssic H o ug h Tr a n sfo r m M O CHA 10

Ex p la n a tio n o f C la ssic H o ug h Tr a n sfo r m Each point in an image votes for all the lines that go through it y mx = + x os( � y ( � r ) + sin ) = M O CHA 11

Hough space C la ssic H o ug h Tr a n sfo r m M O CHA 12

Ex p la n a tio n o f C la ssic H o ug h Tr a n sfo r m M O CHA 13

Detects arbitrary shapes W ill add ability to detect rotated and scaled shapes G en er a l H o ug h Tr a n sfo r m M O CHA 14

Sh a p es Shapes must be built up from an image M ust have a reference point Stores shape as an array of x, y difference pairs Each point in a digital image votes to say that it may be part of a shape M O CHA 15

Sh a p e o f C o ffee Po t M O CHA 16

G en er a l H o ug h Sp a ce M O CHA 17

Ex p la n a tio n o f G en er a l H o ug h Tr a n sfo r m Voting array is thresholded A ny points remaining above the threshold limit indi- cate the likely presence of the shape in the original image They actually represent reference points These points can be used to map the shape back into image space M O CHA 18

Sh a p e M a p p ed Ba ck o n to Im a g e M O CHA 19

C o n clusio n s Image manipulation toolkit in Java M achine vision can be done on the cheap Shape building functions W orking classic and general Hough transform im- plementations N eed to play w ith to determine parameters for successful useage M O CHA 20