Efficient image region and shape detection by
perceptual contour grouping
Huiqiong Chen
Qigang Gao
Faculty of Computer Science
Dalhousie University, Halifax, Canada
[email protected]
Faculty of Computer Science
Dalhousie University, Dalhousie University
[email protected]
Abstract - Image region detection aims to extract meaningful
regions from image. This task may be achieved equivalently by
finding the interior or boundaries of regions. The advantage of
the second strategy is that once a closure is detected not only its
shape information is available, but also the interior property can
be estimated with a minimum effort. In this paper, we present a
novel method that detects region though region contour grouping
based on Generic Edge Token (GET). GETs are a set of
perceptually distinguishable edge segment types including linear
and non-linear features. In our method, an image is first
transformed into GET space on the fly represented by a GET
graph. A GET graph presents perceptual organization of GET
associations. Two types of perceptual closures, basic contour
closure and object contour closure, based upon which all
meaningful regions are conducted, are defined and then detected.
The detection is achieved by tracking approximate adjacent
edges along the GET graph to group the contour closures.
Because of the descriptive nature of GET representation, the
perceptual structure of detected region shape can be estimated
easily based its contour GET types. By using our method, all and
only perceptual closures can be extracted quickly. The proposed
method is useful for image analysis applications especially real
time systems like robot navigation and other vision based
automation tasks. Experiments are provided to demonstrate the
concept and potential of the method.
Index Terms – region detection, perceptual closure, GET data
I. Introduction
An image region is defined as a set of connected pixels
with homogenous properties like color or texture, etc, which
has obvious contrast to its surroundings. Although region
detection plays a significant role in many image analysis
applications, it is a challenging task to efficiently obtain
meaningful and accurate regions from images, particularly for
real-time applications.
Region detection methods can be mainly classified into
region-based and boundary-based methods [1]. Region-based
methods are based on merging similar pixels together to form
coherent regions. Region membership decision, under/over
segmentation and inaccurate boundaries are their common
problems. In [2], detection groups regions for over segmented
image by properties of continuity, region ration, etc. To
reduce over-segmentation in watershed method, Gaussian
convolution with different deviations is used in [3]. Edge
information can be used here for refinement, such as seed
placement selection, homogeneity criterion control, accuracy
improvement [4]. Region-based methods usually treat region
as a set of pixels therefore it is difficult to get accurate region
shape or object structure from image through these methods.
Boundary-based methods employ edge information.
Proper edge pixels are grouped into region boundaries. The
advantage of these methods is that once a closure forms both
region boundaries and its interior properties can be get easily.
However, when edge data is provided on pixel level, the
method effect will be largely affected by the existence of edge
gaps and boundary pixel recognition strategy. Perceptual
origination can be used in detection to find perceptual
regions/objects [5] [6] but and it always suffers from intensive
computation and detecting arbitrary shape regions.
In this paper we present a novel method that can detect
meaningful regions with arbitrary shape efficiently and
speedily. By using innovative perceptual edge features called
Generic Edge Token (GET), meaningful regions can be
detected promptly through perceptual region contour
detection. Perceptual region contour closures are defined first
to represent meaningful regions and then be detected through
closure detection in GET space. Because of the simplicity of
GET representation, and inherent structure information carried
by each feature, the perceptual region shape can be obtained
easily from region as well as region boundary and interior
attributes. Therefore, our method is suitable for image analysis
applications especially for real-time systems. This paper is
organized as follows: Section II introduces Generic Edge
Tokens. Section III presents perceptual region hierarchy and
perceptual region representation. Contour closure detection
method is provided in section IV. Experiments are given in
section V. Section VI provides conclusions and future work.
II. Generic Edge Tokens
Generic Edge Tokens (GETs) are perceptually significant
image primitives which represent classes of qualitatively
equivalent structure elements. A complete set of GETs
includes both Generic Segments (GS) and curve partition
points (CPP), Each GS is perceptually distinguishable edge
segment with linear or nonlinear features while each CPP is
junction of GSs. Gao and Wang proposed a curve partition
method in a very generic form which performs partition
following the similar objective in human visual perception.
The image is scanned horizontally and vertically with an
interval to find strong edge pixels as tracking starting pixels
then edge pixels on the traces are selected according to the GS
definitions. This method is robust with low time expense.
Detail explanation of GET detection can be found in [7]. Fig.
1 (a) shows curve partition examples.
TABLE 1. Definitions of GS types; (M+ and M- represent monotonic
increasing and decreasing properties while c mean a constant)
GS type
Properties
f(x)
j(y)
f’(x)
j’(y)
Fig. 1. Curve partition examples.
A GS is a segment in image with monotonic increasing or
decreasing. GSs can be perceptually classified into eight
categories as Fig. 1 (b) shows. Each type of GSs should
satisfy properties described by the monotonicities of tangent
function y= f(x) and its inverse function x = j(y), as Table 1
illustrates. The monotonicity is checked along each GS. A
CPP is a perceptually significant point where adjacent GSs
meet and curve turning takes place [8]. No intersection among
GSs exists besides CPPs. CPP can group GSs into perceptual
structures. There are four basic categories of CPPs according
to the conjunction types as Fig. 1 (c) shows. The intrinsic
perceptual features of GETs will facilitate further detection of
perceptual closures formed by GETs and ease the extraction
of perceptual shape from regions described by GET closures.
III. Perceptual region hierarchy and representation
According to MPEG-7 standard, region or object shape
can be described by Contour Shape descriptors based on its
contour [9]. Therefore contour closure, formed by region
boundaries, is desirable representation for meaningful regions.
It possesses both low-level features and perceptual concepts:
closure interior pixels contain low level attributes; closure
boundary contains structure/shape information at higher level.
A.
Perceptual region concept hierarchy
From perceptual view, meaningful regions arise from
objects, and then can be represented by object outline and
object basic inner parts (called basic regions). An object is
constituted by one or multiple basic regions. As Fig. 2 shows,
object A is built by 3 basic regions, whose contours can be
represented by basic contour closures e1e2e3e4, e2e5e6e7,
e3e7e8e9 respectively, all of which are formed by GETs. Other
closures are constituted by basic contour closures, among
which e1e5e6e8e9e4 represents the object outline. Therefore all
closures in image formed by GETs can be classified into three
types according to region contour properties: basic contour
closure which represents the contour of basic region; object
contour closure which represents the contour of object outline
or separated object component outline; other composite
closure. All meaningful regions in an object can be
represented by first two types of GET closures while GET
closures can be described by GET organizations. As Fig. 2
illustrates, object B has a separate hole inside, which can be
regarded as a separate object component constituted by one
LS1 LS2
c
n/a
0
∞
n/a
c
∞
0
LS3
LS4
CS1
CS2
CS3
CS4
M+
M+
c
c
MM+
c
c
M+
M+
MM+
MMMM+
MMM+
M-
M+
MM+
M-
single basic region. The remaining part of B other than the
hole is another basic region, whose contour can be described
by the basic contour closure e1e2e3e4 along with the inside
object contour closure e 5e6e7e8, which represents outline of the
object component hole.
From analysis above we can conclude that, basic contour
closures and object contour closures contain most perceptual
information of meaningful regions while other composite
closures do not have much meaning.
Definition 1: Meaningful regions in image can be represented
by two types of perceptual contour closures: basic contour
closures and object contour closures. A basic contour closure
is a basic GET closure which can not be split into other
closures; an object contour closure is a GET closure
representing the outline of an object or object individual
component. (Assume no occlusion occurs).
This definition has several advantages. First, it leads to a
hierarchical descriptor for image content: Image content can
be represented by objects, while an object can be described by
the hierarchy in Fig. 2. Second, both types of perceptual
closures are supported in MPEG-7 frame by contour shape
descriptors [10]. Last, it makes sure that useful perceptual
closures will be picked up in detection and meaningless
composite closures will not be detected anymore.
A region/closure concept hierarchy is constructed in Fig.
2. In this hierarchy, GETs can be organized perceptually into
contour closures, which can build meaningful regions in
objects. To find perceptual contour closures from all GET
data, we use a GET graph to code perceptual organization of
GET associations in image.
Fig. 2. Perceptual region/closure concept hierarchy
B.
Perceptual organization representation by GET graph
A graph named GET graph can be derived from preextracted GET data to code the perceptual structure of image.
An image is transformed into GET space represented by a
special graph named GET graph. Therefore, perceptual
contour closures, i.e., basic contour closures and object
contour closures, can be detected by perceptual cycle search
in GET graph when exploring the correspondence between the
GET closures and graph cycles [11]. For the purpose of real
time processing, GET graph is reduced and search starting
edges are selected before search to avoid duplicate detections
and reduce computation burden. Starting from selected edges,
novel perceptual contour closure detection method is applied
to get all perceptual closures from GET graph then results are
classified into perceptual types.
Taking advantage of the perceptual GET class data, the
image can be transformed into GET space represented by a
GET graph. With all GETs extracted from image, GET graph
can be constructed by converting GSs and CPPs to graph
edges and vertices respectively. Each edge in GET graph
presents a perceptually distinguishable edge segment in image
while the organization of GET graph represents the perceptual
structures in image. If no occlusion occures, each connected
component in GET graph corresponds to an object/individual
object component in image constituted by basic inner parts,
which can be represented by basic contour closures. Therefore
basic contour closures in image one-to-one correspond to
basic cycles in GET graph while object contour closures oneto-one correspond to outlines of graph connected components.
For convenience sometimes we do not distinguish between the
terms in the context of the paper.
GET graph has some unique characters: (1) Besides
representing graph structure, the edges and vertices in GET
graph also represent real segments and their junction points in
image. (2) According to GET properties, no intersection
among edges should exist besides edge endpoints.
GET graph can be constructed as follows: let G= (V, E)
be an undirected graph, where V is the set of vertices and E is
the set of edges in G. Initially V=Æ and E=Æ. For each GSi (0
≤ i< m, m is number of GSs in image), insert it into E as edge
ei. For each CPPj (0 ≤ j < n, where n is number of CPPs in
image), insert it to V as vertex vj.
In practice, there always exist edge gaps in image as Fig.
3 (a) shows. The gaps may be caused by image noise or
broken edges derived from edge detector. To bridge possible
gaps, we amend the concept of “adjacent” for GET graph and
regard close edges within a small common region as adjacent:
For edges ei and ej, if ei’s endpoint vm is close to ej’s
endpoint vn, normalized distance between vm and vn is defined
l (v m ) + l (v n )
(1)
dist (v m , v n ) =
´ d (v m , v n )
[l (vm ) + l (vn )]2 + d
where l(vm), l(vn) are length of longest edges among all
edges connecting to vm, vn respectively; d is mean of length of
all edges Î E, d(vm, vn) is the distance between vm and vn.
If dist(vm, vn)£ threshold Τ, the gap of ei and ej should be
bridged. A virtual vertex m is used as combination of real
vertices vm, vn. m is virtual intersection of ei and ej obtained
by extending endpoints vm and vn from ei and ej respectively.
Therefore, m is a substitute for vm, vn as endpoint of ei and ej.
Definition 2: Graph vertex vj (no matter vj is a real vertex or
virtual vertex) is incident with edge ei iff: vj is ei’s endpoint.
Edges ei, ej are adjacent iff ei and ej: (a) share a real
vertex as common endpoint; or (b) share a virtual vertex as
common endpoint within threshold T. we denote this as ei ~ ej.
The degree of vertex vj, denoted as deg(vj), is the number
of edges incident with vj within T.
Fig. 3 (b) illustrates GET graph construction. If dist (v 0,
v1) £ T, the edge gap between e1 and e10/e12 is so small that it
can be bridged by virtual vertex m1. e1, e10, e12 are adjacent by
m1, deg(m1)= 3; otherwise, this gap can not be ignored. e1 is
not adjacent to e10/e12. deg(v0)=2 and deg(v1)= 1. Similarly,
gap between e4 and e5 can be bridged by m2 if dist(v5, v6) £ T.
GET space is reduced by graph reduction, which remove
noise edges not belonging to graph cycle. It decreases future
search burden and false closure detections. An edge e i with
endpoints vm and vn is a noise edge iff: (a) deg (vm) = 1; or (b)
besides ei, vm is incident with only noise edges; or (c) deg (vn)
=1; or (d) besides ei, vn is incident with only noise edges. Fig.
4(b) shows a GET graph and (c) shows graph reduction.
IV. Perceptual contour closure detection
Perceptual closure detection can be carried out using
GETs pre-extracted from image, as Fig. 5 shows. After graph
reduction, every edge in GET graph must be element of some
closures. A perceptual closure in image can be detected by
finding corresponding cycle in GET graph.
Fig. 4 (a) Original image;
(b) GET graph;
(c) GET graph reduction
Closure
Definition
Image
GET
Detection
GET graph
Construction
& Reduction
Search starting
edge selection
(a) GETs in image with edge gaps; (b) GET graph with virtual vertices
Fig. 3. Construct graph in GET space using GET data.
Closure
Detection
Closure detection
by cycle search
Perceptual
GET closures
(regions)
Result closure
classification
Fig. 5. Perceptual contour closure detection architecture
Staring from one endpoint of an edge then tracking along
adjacent edges, a graph cycle can be formed if a path back to
starting point is found in graph. All closures may be detected
from GET graph by this method when starting from closure
edges. However, this method has two problems. First, proper
starting edges should be selected instead of starting search
from every graph edge. This can reduce computation cost and
duplicate detections, avoid time expense increasing greatly
with graph edge number. Second, search strategy is needed in
tracking to guarantee the results be perceptual closures.
Spanning trees are employed for the first problems. For the
second problem, a new perceptual search strategy is proposed.
A.
Starting edge selection
Spanning tree is a connected sub-graph with tree structure
including all vertices of a graph connected component.
Lemma 1: all perceptual closures can be obtained by tracking
in graph starting from edges not belonging to spanning trees.
That is, edges not belonging to spanning trees should be
selected as starting edges.
Proof of Lemma 1: Let Gi =(Vi, Ei) be the ith connected
component of GET graph, (V i’, Ei’) be set of vertex and edges
for the spanning tree of Gi, Ni = Ei - Ei’. We need to prove (a)
"eÎNi, e must be element of a basic contour closure; (b) each
basic contour closure in Gi must have at least one edge e such
that eÎNi; (c) the object contour closure corresponding to
component outline must have at least one edge e so that Î Ni.
(a) As spanning tree, Vi’=Vi; (Vi’, Ei’) contains no cycle.
"eÎNi with endpoints vx vy, there have vxÎVi’, vyÎVi’ and
one single path p between (vx, vy) in the tree. If e is added to
the tree, e combines with path p forming a cycle. This cycle
either is a basic cycle or is built by basic cycles. e belongs to
one of basic cycles, i.e., e belongs to a basic contour closure.
(b) Since spanning tree contains no cycle, any basic cycle
in (Vi, Ei) must have at least one edge e excluded from (Vi’,
Ei’) to avoid forming this cycle, i.e., eÎNi.
(c) The proof of (c) is similar with (b).
From (a)-(c), we can conclude that Lemma 1 is true.
B.
Perceptual Cycle search algorithm
In connected component i, basic cycle is cycle without
sub-cycle inside and component outline is a cycle without any
cycle outside. Starting from "eiÎNi, which must belong to
some basic cycle of Gi, basic contour closure can be formed
by basic cycle search in graph. The search tracks along
adjacent edges by always selecting the innermost edge in
given direction (anticlockwise/clockwise) from all candidates
in each step of path selection. If ei is on component outline,
object contour closure can also be extracted by selecting
outmost edge in given direction (which can be considered as
selecting innermost edge in the other direction) in each step.
A new closure detection method is proposed to find all
perceptual closures by perceptual cycle search algorithm. This
algorithm is based on the following hypothesis which will be
proven by lemma 2: "eiÎNi with endpoints vx and vy, starting
from vx of ei, a perceptual closure (object contour closure or
basic contour closure) can be found by cycle search in graph,
which always selects innermost edge in some direction from
all adjacent edges as next edge in each step of selection. Since
innermost edge is direction sensitive, the cycle search must
keep the same direction in each step.
Fig. 6 (a) illustrates cycle search. If a clockwise search
starts from v7 of e7, e7 is current edge while v7 is current
vertex. e5, e6 ~ e7 by current vertex v7. As the clockwise
innermost edge for e7, e5 is selected to be next cycle edge. In
next step, e5 is current edge while e6’s another endpoint m2, is
the current vertex. e4 ~ e5 by m2 so e4 is selected. Search stops
when new selected e10 meets e7 at v9 and basic contour closure
e7e5e4e3e2e1e10 forms. Another basic contour closure e 7e6e8e9
can also be formed by anticlockwise search starting from v 7 of
e7. Object contour closure can be extracted as well. Starting
from v10 of object contour edge e11, object contour closure
e11e8e6e5e4e3e2e1e12, which is constituted by clockwise
outermost edges, can be got by anticlockwise cycle search.
Algorithm 1: let ei with endpoints vx, vy be starting edge, the
cycle search algorithm in direction d can be presented as:
Step 1: initially, current edge ce:=ei, current vertex cv:=vx.
Let C be set of closure edges, C={ei}.
Step 2: if ce= ei and cv=vy, search stops and perceptual
closure forms; otherwise perform step 3~4;
Step 3: for each edge ej adjacent to ce by cv, calculate the
included angle from cv to ej in direction d. Select the
adjacent edge with minimal included angle as new selected
edge ne, C=CÈ{ne};
Step 4: update cv and ce; cv:= ne’s another endpoint which
is not incident with ce; ce:=ne; go to step 2.
Lemma 2: the closure extracted using perceptual cycle search
algorithm must be a perceptual closure.
Proof of Lemma 2: let C be the cycle search result in direction
d starting from ei. Here we prove C must represent either a
basic contour closure or an object contour closure.
(1) If ei is not on component outline, C can not be an
object contour closure. There must exist more than one basic
cycles sharing ei; assume that C is not a basic cycle, there
must exist basic cycles C’ inside C and C’’ outside of C
sharing ei with C. If C C’ and C’’ have identity edges ei ~ ej at
first several steps of search, let em , en and el be next edges
selected in C, C’ and C’’ respectively. (a) If em en and el are
different edges, em should be located inside the area between ej
and em. Thus em can not be the innermost edge against ej in
whatever search direction, which contradicts the fact that
edges in C should be the innermost edge in direction d. (b) If
(a) Detect perceptual closures
(b) Simulate closure by polygon
Fig. 6. (a) shows the process of perceptual closures detection. Dashes in
(a) are starting edges and arrows indicate search direction. Polygon in (b)
simulates the closure formed by clockwise search starting from v 7 of e7.
Dashes in (b) indicate polygon edges different from graph edges.
em is same to en but different with el; let ek be the last identical
edge for C and C’, ep and eq be next edges for C and C’. eq is
inside C and el is outside C. No matter d is clockwise or
anticlockwise, either ep is not the innermost edge against e k (eq
is the innermost in stead) or em is not the innermost edge
against ej (en is the innermost). This contradicts to previous
fact. (c) If em = el but different with en; proof is similar to (b).
Summarizing (a)~(c), if ei is on component outline, the prior
assumption can not be true. C must be a basic contour closure.
(2) If ei is on component outline; proof is similar to (1).
From (1)-(2), we can conclude that Lemma 2 is true.
C.
Perceptual closure detection via cycle search
Perceptual closure detection can be described as follows:
Step 1: Construct GET graph and remove noise graph edges.
Step 2: Find all connected components in the GET graph.
For each component, perform step 3.
Step 3: Extract a spanning tree. Starting form each edge ei
not in the tree, perform step 4.
Step 4: perform both clockwise and anticlockwise closure
searches by applying perceptual cycle search algorithm.
It can be easily concluded from lemma 2 that using this
detection method, two closures can be extracted by clockwise
and anticlockwise searches starting from a given edge e, and
there are two possibilities for extracted closures:
(1) If e is not on component outline, both of the detected
closures are basic contour closures;
(2) If e is on component outline, the two closures have one
basic contour closure and one object contour closure.
Lemma 3: the perceptual closure detection method is valid
because by using this method, (1) all extracted closures are
perceptual closures; (2) all perceptual closures are extracted.
Proof of lemma 3: (1) has been represented in lemma 2.
(2) First we prove that object contour closure must be
detected. Based on lemma 1, there must have an object
contour closure edge ejÎNi as starting edge. Starting from this
edge, the two results must have one object contour closure.
Second we prove that all basic contour closures must also
be extracted. Assume there is a basic contour closure C that
cannot be detected by our method, C must have an edge
ejÎNi. If ej is not on the component outline, C must share ej
with two other basic contour closures C’ and C’’, both of
which are detected by cycle search starting from ej. No
intersection exist among the three closures, so C must be
outside the area of (C’’È C’) while ej is an edge inside the
area of (C’’ÈC’). This contradicts with the fact that ej is an
edge of C. If ej is on component outline; C shares e j with basic
contour closure C’ and object contour closure C’’. The areas
of C and C’ do no intersect while both of them are inside C’’.
Therefore C should be inside the area of (C’’-C’), in which ej
can not be included. This also conflicts with previous fact.
Thus this kind of C can not exist no matter e j’s type.
D.
Closure classification
Perceptual closure detection method above finds all perceptual
closures without concerning closure types. The result closures
should be classified into two types: basic contour closure and
object contour closure. The classification can be achieved by
summing up all included angles of a polygon which simulates
the extracted closure. The polygon can be formed during cycle
search by simulating the selected edge in each step as a
straight line between its two endpoints. Included angles
between simulation lines with search direction are recorded.
Fig. 6 (b) shows an example: in clockwise search starting
from v7 of e7, the first selected edge is e5 so the clockwise
included angle from e7 to e5 is recorded as q v v ®v m . The next
7 9
7
2
selected edge e4 is simulated by line from m2 to v4 so q m v ®m v
2 7
2 4
is recorded. The polygon forms when q v m ®v v is recorded.
9 1
9 7
For an n-edge closure, let qTotal be sum of all n included
angles recorded during simulation. If the closure is basic
contour closure, recorded included angles should be interior
angles of polygon since the simulation passes inside closure in
each step, as arrows in Fig. 6(b) shows. qTotal = ∑(all interior
angles of polygon) = 180*(n-2). If the closure is object
contour closure, the included angles should be exterior angles
of polygon thus qTotal = 180*(n+2). Therefore we conclude:
If qTotal = 180*(n-2), the closure is basic contour closure; and
If qTotal = 180*(n+2), the closure is an object contour closure.
where n is the number of edges in the closure. Fig. 7
illustrates the process of closures detection and classification.
V. Experiment Results
To evaluate validity of our method, algorithms are
implemented using C++. Sun Fire 4800, a multi-user UNIX
server running Solaris 8 with 16 GB RAM, 4 UltraSPARC-III
processors at 900 MHz, is used in test. Server load average =
2.64 during test. The test images, whose sizes range from
256*256 to 512*512 pixels, include both synthetic image and
real images. The experiment first extract GETs from image;
then detect regions based on GET data. In our test, average
times of GET extraction and region detection are 418 and 447
millisecond. The number of perceptual closures in image and
correct/error/ missing detections in the results are determined
manually with human perception. Tiny regions are omitted as
noises. Experiment results are listed in Table 2. Average
detection correctness = 91.13%. Experiments show that our
method can get arbitrary region shapes with high correctness
and short process time, which exhibits its potential in image
analysis applications. Fig. 8 gives detection examples.
The error/missing detections may be caused by false edge
selection in search: (1) although normalized threshold is used
to bridge gaps between closure edges, it can not distinguish
edge gaps between relevant edges with cracks in irrelevant
edges by perceptual view. Larger threshold, although bridges
larger gaps, raises the risk of taking unrelated edges as
adjacent, thus increases false detection. (2) Detection depends
heavily on GET data got from GET tracker. It can not find a
region contour if one contour edge is missing detected in GET
tracking, i.e., it can not recovery lost data by knowledge in
closure detection. (3) The GETs derived from image noises
may also mix up with valid GETs thus mislead search.
Fig. 7. Closure extraction and classification for Fig. 4(a); left-most figure shows all contours; other small figures show all basic contour closures.
Fig. 8. Detection result examples. Each row from left to right: original image; extracted GETs; region contour formed by GETs; filled regions.
TABLE 2. Experiment results. Average correctness= 91.13%.
Number of Number of
Image
correct
Error
No
detections detections
1
22
0
2
33
2
3
23
3
4
15
1
5
32
1
6
18
0
7
27
1
Number of
missing
detections
0
1
1
1
7
0
5
Number of
closures in
image
22
36
26
16
39
18
33
Percentage
of correct
detections
100%
91.7%
88.5%
93.8%
82.1%
100%
81.8%
VI. Conclusions and Future Work
In this paper, we propose a GET based method for
extracting meaningful regions from image through contour
closure detection. Perceptual closures are defined to represent
meaningful regions and then be extracted from GET data.
The proposed method has following advantages. (1) It
provides real-time region segmentation in which region shape
structure can be extracted as well. (2) It achieves high accuracy
of detected regions with low computation cost. (3) The method
is suitable for segmenting objects with arbitrary shapes. (4) It
can detect both object contours and their components, based
upon which all meaningful regions and objects can be
conducted hierarchically as Fig. 2 shows.
The possible extension of this method may include the
follows. Other features, such as color and texture, can be added
in the process to increase the robustness of detection in order
to cover a wider range of applications. Meanwhile the added
features would provide with a complete set of region
descriptions. A region may be encoded with all attributes, i.e.
shape, color, and texture. The segmentation technique may
support various application domains include robot navigation,
surveillance motion analysis, image retrieval, etc.
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