Regions and Binary Images

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

Figure Ground Separation

Brightness Thresholding

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. Slide from Kristen Grauman fg_pix = find(diff > t);

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

More Binary Images 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

Find Connected Regions Our target in this image is the largest blob.

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

Pixel Neighbors 4 neighboring pixels of the blue pixel

Pixel Neighbors 8 neighboring pixels of the blue pixel

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

Recursive Method 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

Example Image Label equivalence table

Example (2,3) Image Label equivalence table

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

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

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

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

Morphology Operations • Define At = { p + t | p is a point in A} • Dilation 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 opening After opening Slide from Kristen Grauman

Closing • Dilate, then erode • Fill holes, but keep original shape Before closing After 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. eroded original dilated 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’);

Figure Ground Separation

Regions and Binary Images