1. Introduction
The study of freezing sets is part of the fixed point theory of digital topology. Freezing sets were introduced in [
1] and studied in subsequent papers including [
2,
3,
4,
5]. These papers focus mostly on digital images in
.
In the current paper, we obtain results for freezing sets in , for arbitrary n. We show that given a finite connected digital image , if we use the or adjacency and X is decomposed into a union of cubes , then we can construct a freezing set for X from those of the .
2. Preliminaries
Researchers have taken several different approaches to the study of digital topology, including the
Khalimsky topology [
6,
7,
8], the
Marcus–Wyse topology [
9,
10], and Rosenfeld’s graph-based approach [
11,
12]. We use the latter in this paper.
For Rosenfeld’s graph-based approach, we present foundational material in this section on adjacencies, digitally continuous functions, and terminology.
2.1. Adjacencies
Much of this section is quoted or paraphrased from [
13].
A digital image is a pair where for some n and is an adjacency on X. Thus, is a graph with X for the vertex set and determining the edge set. Usually, X is finite, although there are papers that consider infinite X. Usually, adjacency reflects some type of “closeness” in of the adjacent points. When these “usual” conditions are satisfied, one may consider the digital image as a model of a black-and-white “real world” digital image in which the black points (foreground) are the members of X and the white points (background) are members of .
We write
, or
when
is understood or when it is unnecessary to mention
, to indicate that
x and
y are
-adjacent. Notations
x
κ y, or
x
y when
is understood, indicate that
x and
y are
-adjacent or are equal.
The most commonly used adjacencies are the
adjacencies, defined as follows. Let
and let
,
. Then, for points
we have
if and only if
for at most u indices i we have , and
for all indices j, implies .
The -adjacencies are often denoted by the number of adjacent points a point can have in the adjacency. For example,
in , -adjacency is 2-adjacency;
in , -adjacency is 4-adjacency and -adjacency is 8-adjacency;
in , -adjacency is 8-adjacency, -adjacency is 18-adjacency, and -adjacency is 26-adjacency.
In this paper, we mostly use the - and -adjacencies.
When
is understood to be a digital image under discussion, we use the following notations. For
,
Definition 1 ([
11])
. Let . The boundary of X is 2.2. Digitally Continuous Functions
Much of this section is quoted or paraphrased from [
13].
We denote by id or the identity map for all .
Definition 2 ([
12,
14])
. Let and be digital images. A function f: is-continuous, or digitally continuous or just continuous, when κ and λ are understood, if for every κ-connected subset of X, is a λ-connected subset of Y. If , we say a function is-continuous to abbreviate “-continuous.” Theorem 1 ([
14])
. A function f: between digital images and is -continuous if and only if for every , if then f (x)
λ f (y).
Similar notions are referred to as
immersions,
gradually varied operators, and
gradually varied mappings in [
15,
16].
Theorem 2 ([
14])
. Let and g: be continuous functions between digital images. Then, is continuous. A κ-path is a continuous function r: .
For a digital image
, we use the notation
A function
f:
is an
isomorphism (called a
homeomorphism in [
17]) if
f is a continuous bijection such that
is continuous.
For
, the
projection to the th coordinate is the function
:
defined by
A (digital) line segment in is a set , where f is a digital path, such that the points of S are collinear; S is axis parallel if for all but one of the indices i, is a constant function.
2.3. Cube Terminology
Let , where .
If for there are exactly j indices i such that (equivalently, exactly indices i such that ), we call Y a j-dimensional cube or a j-cube.
A j-cube K in Y, such that
for j indices i, and
for all other indices i, or ,
is a face or a j-face of Y.
A corner of Y is any of the points of . An edge of Y is an axis-parallel digital line segment joining two corners of Y.
3. Tools for Determining Fixed Point Sets
Definition 3 ([
1])
. Let be a digital image. We say is a freezing set for
X if given , implies . Theorem 3 ([
1])
. Let A be a freezing set for the digital image and let be an isomorphism. Then, is a freezing set for . The following are useful for determining fixed point and freezing sets.
Proposition 1 (Corollary 8.4 of [
13])
. Let be a digital image and . Suppose are such that there is a unique shortest κ-path P in X from x to . Then, . Lemma 1, below,
“
… can be interpreted to say that in a
-adjacency, a continuous function that moves a point
p also moves a point that is “behind”
p. E.g., in
, if
q and
are
- or
-adjacent with
q left, right, above, or below
, and a continuous function
f moves
q to the left, right, higher, or lower, respectively, then
f also moves
to the left, right, higher, or lower, respectively [
1].”
Lemma 1 ([
1])
. Let be a digital image, . Let be such that . Let . - 1 .
If , then .
- 2 .
If , then .
Definition 4 ([
2])
. Let be a digital image. Let such thatThen, q is a close -neighbor of p.
Lemma 2 ([
2,
13])
. Let be a digital image. Let such that q is a close κ-neighbor of p. Then, p belongs to every freezing set of . Theorem 4. Let be such that for all i. Let . Then, A is a subset of every freezing set for .
Proof. By Theorem 3, we may assume for all i, so . It is easily seen that every has a close neighbor in X, namely the unique member of X that differs from a by 1 in every coordinate. Therefore, by Lemma 2, A is a subset of every freezing set for . □
4. -Freezing Sets for Cubes
The following is presented as Theorem 5.11 of [
1]. However, there is an error in the argument given in [
1] for the proof of the first assertion. We give a correct proof below.
Theorem 5. Let . Let .
Let be such that for all i. Let be -continuous. If , then .
A is a freezing set for that is minimal for .
The argument given in [
1] is based on induction. We quote the beginning of the argument’s inductive step:
“Now suppose and is -continuous with . Let
We have that
and
are
-continuous,
, and
. Since
and
are isomorphic to
k-dimensional digital cubes, by Theorem 3 [of the current paper; it’s Theorem 5.2 of [
1]] and the inductive hypothesis, we have
Note that the above fails to show that
and
; hence, if
X is a proper subset of
Y it does not follow from the above that
and
. In the following, we give a correct proof of the first assertion of Theorem 5, using a rather different approach than was employed in [
1].
Proof. By Theorem 3, we may assume
Let
be
-continuous such that
. Observe that by Proposition 1,
By (
1),
.
We proceed inductively. For
, let
Note a j-face of X is a j-cube with corners in A. Suppose for some . Given , let K be an -face of Y such that . Let and be opposite ℓ-faces of K, i.e., for some index d, implies, without loss of generality, and .
Let
. Then,
x is a point of an axis-parallel segment from a point of
to a point of
. By (
1),
. Thus,
; therefore,
. This completes our induction. In particular,
.
Thus, for
, it follows that
A is a freezing set for
. That
A is minimal for
follows as in [
1]. □
The set of corners of a cube is not always a minimal -freezing set, as shown by the following example in which the set A is a proper subset of the set of corners.
Example 1 ([
1])
. Let . LetThen, A is a freezing set for .
5. -Freezing Sets for Unions of Cubes
In this section, we show how to obtain -freezing sets for finite subsets of .
Theorem 6. Let where ,and X is -connected. Let Let . Then, A is a freezing set for .
Proof. Let be such that .
Given , we have for some i. Since , it follows from Theorem 5 that . Thus, , so A is a freezing set. □
Corollary 1. The wedge of two digital cubes in with axis-parallel edges has for a -freezing set , where is the set of corners of .
Proof. This follows immediately from Theorem 6. □
Remark 1. Theorem 6 can be used to obtain a freezing set for any finite -connected digital image , since X is trivially a union of 1-point cubes . More usefully, if a subset H of X is a union of cubes,then a freezing set A for is Remark 2. Often, the freezing set of Theorem 6 is not minimal. However, the theorem is valuable in that it often gives a much smaller subset of X than X itself as a freezing set. As a simple example of the non-minimal assertion, consider For this description of X, Theorem 6 gives the -freezing seta set of 12 points. However, by observing that X can be described as , we obtain from Theorem 5 the -freezing set , a set of 8 points. The following example shows that a cubical “cavity” (see
Figure 1) need not affect determination of a freezing set.
Example 2. Let . Then, is a freezing set for .
Proof. Note that by Theorem 5, A is a freezing set for . We show that removing need not change the freezing set.
Observe that we can decompose
X as a union of cubes as follows: Let
Then, . Theorem 5 gives us a freezing set B for consisting of the corners of each of .
However, suppose is such that . As in the proof of Theorem 6, each of the faces of K is a subset of . Therefore, each is on an axis-parallel digital segment that joins two points of one of , so by Proposition 1, . Therefore, A is a freezing set for . □
6. -Freezing Sets in
We have the following.
Proposition 2 ( [
1])
. Let X be a finite digital image in . Let . Let , where . If , then . Theorem 7 ([
1])
. Let X be a finite digital image in . For , is a freezing set for . The following is inspired by Theorem 7.
Theorem 8. Let , where and for all i, . Then, is a minimal freezing set for .
Proof. By Theorem 7, is a freezing set for . We must show its minimality.
Consider a point . For some index i, .
If , the point is a close neighbor of .
If , the point is a close neighbor of .
In either case, we must have as a member of every freezing set for , by Lemma 2. Thus, is a minimal freezing set. □
Theorem 9. Let where ,and X is -connected. Let and let . Then, A is a freezing set for . Proof. By Theorem 7, is a freezing set for . Let be such that . It follows from Proposition 2 that each . Thus, , and the assertion follows. □
7. Conclusions and Future Work
We have studied freezing sets for finite digital images in with respect to the - and -adjacencies. For both of these adjacencies, we have shown that a decomposition of an image X as a finite union of cubes lets us find a freezing set for X as a union of freezing sets for the cubes of the decomposition. Such a freezing set is not generally minimal, but often is useful in having cardinality much smaller than the cardinality of X.
More general restrictions on , where A is a freezing set for and , restrict f on all of X in interesting ways. This will be shown in future work.
The suggestions and corrections of the anonymous reviewers are acknowledged gratefully.