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CSE554Cell ComplexesSlide 1 CSE 554 Lecture 3: Shape Analysis (Part II) Fall 2014

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CSE554Cell ComplexesSlide 2 Review Skeletons – Centered curves/surfaces Approximations of medial axes – Useful for shape analysis 2D skeletons 3D surface and curve skeletons

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CSE554Cell ComplexesSlide 3 Review Thinning on binary pictures – Removable pixels (voxels) Whose removal does not alter the object’s shape or topology – Strategies Serial thinning or parallel thinning Removal pixels Thinning

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CSE554Cell ComplexesSlide 4 Review Issues (with thinning on a binary picture) – Difficult to write a 3D thinning algorithm E.g., simple voxel criteria, surface-end voxel criteria – Skeletons can be noisy Requires pruning x

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CSE554Cell ComplexesSlide 5 This lecture… Thinning on a cell complex – One algorithm that works for shapes in any dimensions (2D, 3D, etc.) – Integrates pruning with thinning

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CSE554Cell ComplexesSlide 6 Cells Geometric elements with simple topology – k-cell: an element at dimension k 0-cell: point 1-cell: line segment, curve segment, … 2-cell: triangle, quad, … 3-cell: cube, tetrahedra, … – Formally: A k-cell has the topology of a closed k-dimensional ball 0-D ball: point 1-D ball: edge 2-D ball: disk 3-D ball: sphere 0-cell1-cell 2-cell3-cell Not a 2-cell Not a 3-cell

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CSE554Cell ComplexesSlide 7 Cells The boundary of a k-cell (k>0) has dimension k-1 – Examples: A 1-cell is bounded by two 0-D points A 2-cell is bounded by a 1-D curve A 3-cell is bounded by a 2-D surface 0-cell1-cell 2-cell3-cell (boundary is colored blue)

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CSE554Cell ComplexesSlide 8 Cell Complex Union of cells and their boundaries – The formal name is CW (closure-finite, weak-topology) Complex Precise definition can be found in algebraic topology books 000 0 11 11 2

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CSE554Cell ComplexesSlide 9 Cell Complex Are these cell complexes? (this edge is not bounded) (the triangle is not bounded)

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CSE554Cell ComplexesSlide 10 Example Cell Complexes 2D – Polyline – Triangulated polygon 3D – Triangulated surface – Tetrahedral volume 0-,1-cells0-,1-, 2-cells 0-,1-, 2-, 3-cells

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CSE554Cell ComplexesSlide 11 Cell Complex from Binary Pic Representing the object as a cell complex – Approach 1: create a 2-cell (3-cell) for each object pixel (voxel), and add all boundary cells Reproducing 8-connectivity in 2D and 26-connectivity in 3D

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CSE554Cell ComplexesSlide 12 Representing the object as a cell complex – Approach 2: create a 0-cell for each object pixel (voxel), and connect them to form higher dimensional cells. Reproducing 4-connectivity in 2D and 6-connectivity in 3D Cell Complex from Binary Pic

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CSE554Cell ComplexesSlide 13 Algorithm: Approach 1 2D: – For each object pixel, create a 2-cell (square), four 1-cells (edges), and four 0- cells (points). 3D : – For each object voxel, create a 3-cell (cube), six 2-cells (squares), twelve 1-cells (edges), and eight 0-cells (points).

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CSE554Cell ComplexesSlide 14 Algorithm: Approach 1 Handling cells shared by adjacent pixels (voxels) – Create an array of integers that keeps track of whether a cell is created (e.g., at a pixel, at a pixel’s edge, or at a pixel’s point), and if it is created, the index of the cell. – Look up this array before creating a new cell 0 0000 0 000 0 000 0 0 000 0 0 0 0 0 00 0 00 00 0 00 0 0 Index of 2-cell Index of 1-cell Index of 0-cell

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CSE554Cell ComplexesSlide 15 Algorithm: Approach 1 0 0000 0 000 0 000 0 0 000 0 0 0 0 0 00 0 00 00 0 00 0 0 Index of 2-cell Index of 1-cell Index of 0-cell 0 0 0000 0 000 10 000 0 0 000 0 040 30 120 30 12 0 4 0 0 0000 0 000 12 000 0 0 000 0 047 36 125 346 125

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CSE554Cell ComplexesSlide 16 Algorithm: Approach 2 2D: – Create a 0-cell at each object pixel, a 1-cell for two object pixels sharing a common edge, and a 2-cell for four object pixels sharing a common point 3D: – Create a 0-cell at each object voxel, a 1-cell for two object voxels sharing a common face, a 2-cell for four object voxels sharing a common edge, and a 3- cell for eight object voxels sharing a common point Follow the note in Approach 1 for storing cell indices

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CSE554Cell ComplexesSlide 17 Thinning on Binary Pictures Remove simple pixels (voxels) – Whose removal does not affect topology Protect end pixels (voxels) of skeleton curves and surfaces – To prevent shrinking of skeleton

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CSE554Cell ComplexesSlide 18 Thinning on Cell Complexes Remove simple pairs – Whose removal does not affect topology Protect medial cells – Which are important for describing shape Advantages: – Easy to detect in 2D and 3D (same code) – Medial cells are robust to boundary noise

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CSE554Cell ComplexesSlide 19 Simple Pairs How can we remove cells from a complex without changing its topology?

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CSE554Cell ComplexesSlide 20 Simple Pairs Definition – A pair {x, y} such that y is on the boundary of x, and there is no other cell in the complex with y on its boundary. x y {x, y} is a simple pair y {x,y} is not a simple pair x

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CSE554Cell ComplexesSlide 21 Simple Pairs Definition – A pair {x, y} such that y is on the boundary of x, and there is no other cell in the complex with y on its boundary. x y {x, y} is a simple pair x y

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CSE554Cell ComplexesSlide 22 Simple Pairs Definition – A pair {x, y} such that y is on the boundary of x, and there is no other cell in the complex with y on its boundary. – In a simple pair, x is called a simple cell, and y is called the witness of x. A simple cell can pair up with different witnesses x y {x, y} is a simple pair y y x y

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CSE554Cell ComplexesSlide 23 Simple Pairs Removing a simple pair does not change topology – True even when multiple simple pairs are removed together As long as the pairs are disjoint x y x y x y x y x1x1 y1y1 x2x2 y2y2

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CSE554Cell ComplexesSlide 24 Exhaustive Thinning Removing all simple pairs in parallel at each iteration – Only the topology of the cell complex is preserved – If a simple cell has multiple witnesses, an arbitrary choice is made // Exhaustive thinning on a cell complex C 1.Repeat: 1.Let S be all simple pairs in C 2.If S is empty, Break. 3.Remove all cells in S from C 2.Output C

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CSE554Cell ComplexesSlide 25 Exhaustive Thinning Removing all simple pairs in parallel at each iteration – Only the topology of the cell complex is preserved 2D example

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CSE554Cell ComplexesSlide 26 Exhaustive Thinning Removing all simple pairs in parallel at each iteration – Only the topology of the cell complex is preserved 3D example

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CSE554Cell ComplexesSlide 27 Exhaustive Thinning Removing all simple pairs in parallel at each iteration – Only the topology of the cell complex is preserved A more interesting 2D shape

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CSE554Cell ComplexesSlide 28 Exhaustive Thinning Removing all simple pairs in parallel at each iteration – Only the topology of the cell complex is preserved A 3D shape

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CSE554Cell ComplexesSlide 29 Isolated cells A cell x is isolated if it is not on the boundary of other cells – A k-dimensional skeleton is made up of isolated k-cells Isolated cells are colored

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CSE554Cell ComplexesSlide 30 Medial Cells (2D) “Meaningful” skeleton edges survive longer during thinning

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CSE554Cell ComplexesSlide 31 Medial Cells (2D) Isolation iteration (I(x)): # thinning iterations before cell is isolated Removal iteration (R(x)): # thinning iterations before cell is removed Medial cells: isolated cells whose difference between R and I is greater than a threshold – Absolute difference: R(x) – I(x) – Relative difference: (R(x) – I(x)) / R(x) I R

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CSE554Cell ComplexesSlide 32 Medial Cells (2D) Isolation iteration (I(x)): # thinning iterations before cell is isolated Removal iteration (R(x)): # thinning iterations before cell is removed Medial cells: isolated cells whose difference between R and I is greater than a threshold – Absolute difference: R(x) – I(x) – Relative difference: (R(x) – I(x)) / R(x) R-I 1-I/R

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CSE554Cell ComplexesSlide 33 Medial Cells (2D) For a 1-cell x that is isolated during thinning: – I(x), R(x) measures the “thickness” and “length” of shape – A greater difference means the local shape is more “elongated” I R R-I 1-I/R

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CSE554Cell ComplexesSlide 34 Medial Cells (3D) For a 2-cell x that is isolated during thinning: – I(x), R(x) measures the “thickness” and “width” of shape – A greater difference means the local shape is more “plate-like” I R R-I: face size 1-I/R: face color

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CSE554Cell ComplexesSlide 35 Medial Cells (3D) For a 1-cell x that is isolated during thinning: – I(x), R(x) measures the “width” and “length” of shape – A greater difference means the local shape is more “tubular” R-I: edge length 1-I/R: edge color I R

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CSE554Cell ComplexesSlide 36 Medial Cells and Thinning A cell x is a medial cell if it is isolated and the difference between R(x) and I(x) exceeds given thresholds – A pair of absolute/relative difference thresholds is needed for medial cells at each dimension 2D: thresholds for medial 1-cells – t1 abs, t1 rel 3D: thresholds for both medial 1-cells and 2-cells – t1 abs, t1 rel – t2 abs, t2 rel Thinning: removing simple pairs that are not medial cells – Note: only need to check the simple cell in a pair (the witness is never isolated)

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CSE554Cell ComplexesSlide 37 Thinning Algorithm (2D) // Thinning on a 2D cell complex C // Thresholds t1 abs and t1 rel for medial 1-cells 1.k = 1 2.For all x in C, set I(x) be 0 if x is isolated, NULL otherwise 3.Repeat and increment k: 1.Let S be all simple pairs in C 2.Repeat for each pair {x,y} in S: 1.If x is 1-cell and ( k-I(x)>t1 abs and 1-I(x)/k>t1 rel ), exclude {x,y} from S. 3.If S is empty, Break. 4.Remove all cells in S from C 5.Set I(x) be k for newly isolated cells x in C 4.Output C Current iteration

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CSE554Cell ComplexesSlide 38 Thinning Algorithm (3D) // Thinning on a 3D cell complex C // Thresholds t1 abs and t1 rel for medial 1-cells // Thresholds t2 abs and t2 rel for medial 2-cells 1.k = 1 2.For all x in C, set I(x) be 0 if x is isolated, NULL otherwise 3.Repeat and increment k: 1.Let S be all simple pairs in C 2.Repeat for each pair {x,y} in S: 1.If x is 1-cell and ( k-I(x)>t1 abs and 1-I(x)/k>t1 rel ), exclude {x,y} from S. 2.If x is 2-cell and ( k-I(x)>t2 abs and 1-I(x)/k>t2 rel ), exclude {x,y} from S. 3.If S is empty, Break. 4.Remove all cells in S from C 5.Set I(x) be k for newly isolated cells x in C 4.Output C Current iteration

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CSE554Cell ComplexesSlide 39 Choosing Thresholds Higher thresholds result in smaller skeleton – Threshold the absolute difference at ∞ will generally purge all cells at that dimension Except those for keeping the topology – Absolute threshold has more impact on features at small scales (e.g., noise) – Relative threshold has more impact on rounded features (e.g., blunt corners) t1 abs = ∞ Skeletons computed at threshold

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CSE554Cell ComplexesSlide 40 t1 rel = 0.5t1 rel = 0.6t1 rel = 0.7t1 rel = 0.8 T1 abs = 1 T1 abs = 3 T1 abs = 5 T1 abs = 7

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CSE554Cell ComplexesSlide 41 More Examples 2D t1 abs = ∞ t1 rel = 1 t1 abs = 4 t1 rel =.4

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CSE554Cell ComplexesSlide 42 More Examples 3D t1 abs =∞ t1 rel =1 t2 abs =∞ t2 rel =1 t1 abs =4 t1 rel =.4 t2 abs =∞ t2 rel =1 t1 abs =∞ t1 rel =1 t2 abs =4 t2 rel =.4 t1 abs =4 t1 rel =.4 t2 abs =4 t2 rel =.4

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CSE554Cell ComplexesSlide 43 More Examples 3D t1 abs =∞ t1 rel =1 t2 abs =∞ t2 rel =1 t1 abs =∞ t1 rel =1 t2 abs =5 t2 rel =.4 t1 abs =5 t1 rel =.4 t2 abs =∞ t2 rel =1 t1 abs =5 t1 rel =.4 t2 abs =5 t2 rel =.4

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A Simple and Robust Thinning Algorithm on Cell Complexes

A Simple and Robust Thinning Algorithm on Cell Complexes

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