I apologize for my lengthy title name. I have two questions, where the second question is based on the first one.
(1). Suppose I have a matrix, whose entries are either 0 or 1. Now, I pick an arbitrary 0 entry. Is there an efficient algorithm that searches the nearest entry with label 1 or calculates the distance between the chosen 0 entry and its nearest entry with label 1?
(2). Suppose now the distribution of entries 1 has a geometric property. To make this statement clearer, imagine this matrix as an image. In this image, there are multiple continuous lines (not necessarily straight). These lines form several boundaries that partition the image into smaller pieces. Assume the boundaries are labeled 1, whereas all the pixels in the partitioned area are labeled 0. Now, similar to (1), I pick a random pixel labeled as 0, and I hope to find out the coordinate of the nearest pixel labeled as 1 or the distance between them.
A hint/direction for part (1) is enough for me. If typing up an answer takes too much time, it is okay just to tell me the name of the algorithm, and I will look it up.
p.s.: If I post this question in an incorrect section, please let me know. I will re-post it to an appropriate section. Thank you!
I think that if you have a matrix, you can run a BFS version where the matrix A will be your graph G and the vertex v will be the arbitrary pixel you chose. There is an edge between any two adjacent cells in the matrix.