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This solution involves shifting one image over another in all possible positions and counting how many ones overlap. By varying the horizontal and vertical shifts, and calculating overlaps with respect to the position of img2, the solution finds the maximum overlap. This approach iterates over all possible translations, moving img1 relative to img2 and keeping track of the maximum overlap seen.
Time Complexity: O(n^4), where n is the size of the matrix, due to the two nested loops and a cross-sectional overlap check for each.
Space Complexity: O(1), since the solution uses constant space apart from input storage.
1def largestOverlap(img1, img2):
2 def overlap_count(xShift, yShift):
3 n = len(img1)
4 count = 0
5 for r in range(n):
6 for c in range(n):
7 if 0 <= r + xShift < n and 0 <= c + yShift < n:
8 count += img1[r][c] & img2[r + xShift][c + yShift]
9 return count
10
11 n = len(img1)
12 max_overlap = 0
13 for xShift in range(-n + 1, n):
14 for yShift in range(-n + 1, n):
15 max_overlap = max(max_overlap, overlap_count(xShift, yShift))
16 return max_overlap
17
18img1 = [[1,1,0],[0,1,0],[0,1,0]]
19img2 = [[0,0,0],[0,1,1],[0,0,1]]
20print("Max Overlap:", largestOverlap(img1, img2))In the Python implementation, we define an inner function overlap_count that calculates overlapping 1s for shifts. The main logic consists of nested loops attempting all possible shifts and updating the maximum overlap counter. The result outputs through a print statement call.
This solution uses the concept of convolution by considering the images as arrays of points. We tallied the number of overlapping 1s for each relative translation using convolution properties without directly shifting arrays and used efficient matrix operations to compute overlaps.
Time Complexity: O(n^4), as it traverses through each overlap position and checks all cells.
Space Complexity: O(n), due to the allocation of cumulative arrays.
1
The C convolution solution approaches the problem by varying the shift on rows and columns and utilizes cumulative positions of 1s. It identifies maximum overlap within those defined indices by focusing on particular shifts and computing resulting overlaps without directly shifting matrices.