1 Regions and Binary Images Hao Jiang Computer Science Department Sept. 24, 2009

Figure Ground Separation 2

Brightness Thresholding 3

= - Thresholding  Given a grayscale image or an intermediate matrix  threshold to create a binary output. Example: background subtraction Looking for pixels that differ significantly from the “empty” background. fg_pix = find(diff > t); Slide from Kristen Grauman

Thresholding  Given a grayscale image or an intermediate matrix  threshold to create a binary output. Example: color-based detection Looking for pixels within a certain hue range. fg_pix = find(hue > t1 & hue < t2); Slide from Kristen Grauman

More Binary Images 6 Slide from Kristen Grauman

Issues  How to demarcate multiple regions of interest?  Count objects  Compute further features per object  What to do with “noisy” binary outputs?  Holes  Extra small fragments Slide from Kristen Grauman

Find Connected Regions 8

9 Our target in this image is the largest blob.

Connected Components  Identify distinct regions of “connected pixels” Shapiro and Stockman

Pixel Neighbors 11 4 neighboring pixels of the blue pixel

Pixel Neighbors 12 8 neighboring pixels of the blue pixel

Recursive Method 13 label = 2 for i = 1 to rows for j = 1 to cols if I(i, j) == 1 labelConnectedRegion(i, j, label) label ++; end

Recursive Method 14 function labelConnectedRegion(int i, int j, int label) if (i,j) is labeled or background or out of boundary return I(i,j)=label for (m,n) belongs to neighbors of (i,j) labelConnectedRegion(m,n,label) end

Two Pass Method  We check each pixel from left to right and up to bottom  If a pixel has no left and up foreground neighbor, we assign a new label to the pixel  If a pixel has only one left or up foreground neighbor, we assign the label of the neighbor to the pixel  If a pixel has both left and up foreground neighbors, and their labels are the same, we assign the label of the neighbor to the pixel  If a pixel has both left and up foreground neighbors, and their labels are the different, we assign the label of the left neighbor to the pixel and use the up-left label pair to update the equivalency table.  We go through another pass to replace the labels to corresponding labels in the equivalency table 15

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table (2,3)

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table (5,2)

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table

Example Image Label equivalence table (5,2)

Example Image Label equivalence table

Example Image Label equivalence table

Morphology Operations  Define At = { p + t | p is a point in A}  Erosion 41 T = { t | At belongs to S} A S Erosion(S, A)

Morphology Operations  Define At = { p + t | p is a point in A}  Erosion 42 T = { t | At belongs to S} S What if A is

Morphology Operations  Define At = { p + t | p is a point in A}  Dilation 43 T = Union of At and S for all t in S S What if A is Erosion(S, A)

Morphology Operations  Define At = { p + t | p is a point in A}  Dilation 44 T = Union of At and S for all t in S S What if A is

Morphology Operations  Define At = { p + t | p is a point in A}  Dilation 45 T = Union of At and S for all t in S What if A is

Opening  Erode, then dilate  Remove small objects, keep original shape Before openingAfter opening Slide from Kristen Grauman

Closing  Dilate, then erode  Fill holes, but keep original shape Before closingAfter closing Slide from Kristen Grauman

Morphology Operators on Grayscale Images  Dilation and erosion typically performed on binary images.  If image is grayscale: for dilation take the neighborhood max, for erosion take the min. originaldilated eroded Slide from Kristen Grauman

Matlab  Create structure element se = strel(‘disk’, radius);  Erosion imerode(image, se);  Dilation imdilate(image, se);  Opening imopen(image, se);  Closing imclose(image, se);  More possibilities bwmorph(image, ‘skel’); 49

Figure Ground Separation 50

Brightness Thresholding 51

Opening 52

Find the Largest Connected Region 53

Example Using Binary Image Analysis: segmentation of a liver Slide credit: Li Shen Slide from Kristen Grauman

Example Using Binary Image Analysis: Bg subtraction + blob detection … Slide from Kristen Grauman

University of Southern California Example Using Binary Image Analysis: Bg subtraction + blob detection Slide from Kristen Grauman