Comfortable Dialog for Object Detection – Rahmadi Kurnia
ISSN 1858-1633 2005 ICTS 117
is 120 , red is 0
magenta is 300 etc. The result of
these processes is shown by Figure 2.
2.2.Labeling and shape detection
Some values are put to the image to label the similar color in one region for detecting the number of
objects. The shape of object is detected by determine its size, area, surrounding by using curve
ratio equation. [8].
2
4 perimeter
area S
π =
We get some curve ratio is specific for each standard shape objects from experiments. For
example, the curve ratio of circle is 0.8, square is around 0.79, rectangular is 0.7, triangle and other
irregular shape are less than 0.58
3. FEATURE CHARACTERISTICS AND GENERAL RULE
Object feature is a most important thing for pattern recognition. Object feature can be used to
recognize in single or cluster object. In this paper, four features size, color, position and shape are used
to analyze, describe and search the target object. All of the features are sorted related on their special
characteristic in any condition. By groping this feature into some general characteristic and
manipulated them like human thinking, the general rule can be obtained for any condition of objects for
making efficient and effective question to the user. Four general characteristic which cover these feature
are: Vocabulary representative, objects distribution, uniqueness, objectivity subjectivity.
1. Vocabulary representative The feature of object has some vocabularies to be
described to the user. The system has several exact words to describe all condition of objects.
Color, size and position are included in this characteristic. Color has some vocabularies like
red, yellow, blue, green, cyan, magenta, brown, white, black etc. The expressions of position are
left, right, up side and under side. Big and small are the exact words for size feature. Even though
the size only has two terminologies but both of them are representative words. The feature of
shape is excluded of this characteristic because some of its shape is irregular and indefinable.
2. Object Distribution Although we consider features of each object
independently, we may find it difficult to express some features by word depending on the spatial
distribution of objects. We call a feature with this problem a distribution-dependent feature.
Position is a distribution-dependent feature. If several objects exist close together, it is difficult
to specify the position of each object. Color, size, and shape are not such features.
3. Uniqueness There is one peculiar feature belonged to each
object. This feature should be unique and differed for object. The feature of position is covered by
this characteristic. In the real condition there is only one specific location of each object in the
scene. Their position can be described by side direction or specified by detail using x-y
coordinate.
4. Objectivitysubjectivity Objectivity:
The feature of object is independent of other objects. Shape and color are included in this
characteristic. Subjectivity:
In real condition, we will incapable to assure the feature of one object if they have not seen the other
objects in advance. We are in doubt to state one objects is big or small, in the left or right side before
we know the location and size of others.
In this paper we sort the size base like human thinking. From any experiments result we get
conclusion that human can clearly and surely distinguish the size of two objects if the minimum of
difference of the biggest object and the smallest one is equal as a doubled smallest object. .
The grouping of size can be explained as : for
Max size ≥ 4 Min size
if size
≥ Max size - Min size Word size = “big”
else if size
≤ 2 Min size Word size = “small”
else Word size = unpredictable
where Max size = maximum size
Min size = minimum size Objects position can be recognized by dividing
them into two opposite group’s side especially for multiple objects. Sorting multiple objects location is
based on the distance of furthest object in each group. In this paper, the barrier of groups should conform to
equation 4 for recognizable.
d
side
≥ 2 d
max
4 where
d
side
= distance of two furthest objects
sides in each group.. d
max
= maximum of distance in one group.
The relation of features and their characteristic is shown in table 1.
In this paper, when several candidate target objects are detected, the robot will ask the user to find
the real target object. Robot reduces unnecessary
Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 118
objects based on user’s answer to simplify the scene. The system analyzes question which can remove the
largest number of unnecessary objects to determine their priority. The highest priority’s question will be
asked to the user in any cases for obtaining most efficient and convenient dialog. We call this method
as Ask and Remove AaR method.
Table 1. Feature and its characteristics Characteristic Color Size
Position Shape Vocabulary
representation √
√ √
○ Object
distribution √
√ ○
√ Uniqueness
○ ○
√ ○
Objectivity √
○ ○
√
In AaR method, the number of total objects is divided into two categories:
First , for the number of total objects is less than
four objects. The system will choose only subjectivity characteristic to determine a question by using. This
characteristic consists of size and position which are very effective to describe some objects in limited
number. The detail of each objects feature can be formulated easily by comparing one object volume,
objects area and object location to others.
Second, for the number of total objects is more
than three objects. The system spelled out this condition to three intervals of similarity object
characteristic number for formulating any cases. In this paper, we obtain the partition value of each
interval from experimental results. The entire of interval are:
a. Interval A: the number of objects that has similar characteristic is less than 30 of the
total objects number. b. Interval B: the number of objects that has
similar characteristic is more than 30 but less than 70 of the total objects number.
c. Interval C: the number of objects that has similar characteristic is more than 70of the
total objects number. All of the classification of these intervals is shown
by figure 3.
Figure 3. Interval condition of target objects
Table 2 shows all of cases that might be possible in real condition.
From the table we get some cases: Case 1:
The number of objects which has similar vocabulary’s characteristic is less than 30 of the
total objects. In this case we ignore other characteristics and their interval location because in
interval A, vocabulary’s characteristic has several exact expression word to define more than others.
Therefore, “what question” is a best choice to make a dialog with the user. The system asks the user a
question which contains these expression words then will remove unnecessary objects based on user’s
answer. In reality, even though in this characteristic consist of position color and size but in this case the
most priority is color. Position and size has a couple depended expressions’ word to define their feature.
This condition means : the number of objects which has similar size and position can be less than 30 of
the total objects but simultaneously, the number of objects which has similar opposite size and position
will be more than 70of the total objects. In case 1, position and size always located in two intervals.
Therefore, both of them are confusable for becoming a content of a question.
Color’s feature is possible to be located in two interval e.g. interval A and interval B
simultaneously, but all of its words expression independent each other
Table 2. All of condition of target objects The number of objects that has similar
characteristic: Interval A
Interval B Interval C
Event
vocabulary Case
1 vocabulary
objectivity Case
2 objectivity
vocabulary Case 3
vocabulary objectivity
Case 4 objectivity
Case 5
c h
a r
a c
t e
r i
s t
i c
vocabulary objectivity
Case 6
Case 2: In this second case, the number of objects which
has similar objectivity’s characteristic or vocabulary’s characteristic is between 30 and 70of the total
objects and more than 70of total objects. This condition can be solved by determining a suitable
feature, which is included in both of characteristics. We formulate that feature by using equation 1 and
simulate it by Venn diagram as shown in figure 4.
CoQ
2
= V ∩ O
5
the number of total objects
30 70
Interval A Interval B
Interval C
the number of similar characteristic objects
Comfortable Dialog for Object Detection – Rahmadi Kurnia
ISSN 1858-1633 2005 ICTS 119
= color where,
CoQ
2
= content of question for case 2 V
= vocabulary O
= objectivity
From that equation we find the best feature is found by intersect both of the characteristics. In this
case we get a color as a best feature to be asked to the user.
Figure 4. Venn Diagram of vocabulary and shape intersection
Case 3: The number of objects which has similar
objectivity’s characteristic is between 30 and 70 of the total objects and more than 70 of total objects
have similar vocabulary’s characteristic. In this case, vocabulary’s characteristic is not effective to be asked
to the user because there is too many similar objects’ feature to used “ask and remove” method. Therefore,
the priority for asking the user at the first time is objectivity characteristic. The content of feature of
this characteristic which is asked can be formulated as:
CoQ
3
= O – V 6
= shape where,
CoQ
3
= content of question for case 3
Figure 5. Venn Diagram of Objectivity - Vocabulary
This result can be described by Venn diagram as shown in figure 5.
Case 4: This case is an opposite of case 3. In this case,
the number of objects that has similar vocabulary’s characteristic is between 30 and 70 of the total
objects and more than 70 of total objects have similar objectivity’s characteristic.
It means that there are so many objects have similar feature for vocabulary’s characteristic which
lead to an inefficient and ineffective first question to be asked to the user. Therefore the objectivity’s
characteristic is more priority than other characteristics. The formulation of the features of this
case is shown by equation 4.
The exactly feature this characteristic which is asked can be formulated as:
CoQ
4
= V – O 7
= size and position where,
CoQ
4
= content of question for case 4 This equation obtains that size and position are
two candidate features which can be chosen for containing the question as shown in figure 6. Both of
them has a couple opposite word The estimation for choosing one of them based on whose feature can
remove unnecessary objects after the robot get an answer from the user. The system will determine the
number of objects in each word feature. Most of the number of unnecessary object can be removed if the
number of objects in both of their word borders on balance.
Figure 6.Venn Diagram of Vocabulary - Objectivity
Case 5: The number of objects which has similar
objectivity’s characteristic is less than 30 of the total objects. The system will get some varieties of the
object features by analyzing them independently. Each object features will be performing as a single
object to recognize their feature. Therefore, position and size feature is unconcerned with the system.
In this case, one of two probabilities condition could be happened: First, there are several similar
color objects and similar shape objects in the scene. In this case, the color will be chosen by the system
because it has complete and definite word objects expression more than shape. Second, there are several
similar shape including in interval A and several similar color objects belong to other interval. In this
case shape will be a highest priority because it has the most varieties to be asked to the user.
color
objectivity
position size
shape
size position
color shape
objectivity vocabulary
vocabulary objectivity
color size
position shape
vocabulary
Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 120
Figure 7. Venn Diagram of Objectivity Case 6:
In this case the number of objects which has similar objectivity’s characteristic or vocabulary’s
characteristic is more than 70 of the total objects .For this specific condition, the system will specify
the detail feature of each objects to get their uniqueness and their distribution. . The system
manages a first question based on both of these characteristic.
Table 3 shows all of general rule system of robot question in any cases.
Table 3. General rule system Question
Condition Content
The Type
The total objects 3
Subjectivity “yesno” question Case 1
Vocabulary “what” question
Case 2 Vocabulary
“yesno” question Case 3
Objectivity “yesno” question
Case 4 vocabulary
“yesno” question Case 5
Objectivity “what” question
Case 6
uniqueness or object distribution
“yesno” question
A. Dynamic Knowledgebase The system records the results and their process to
a dynamic database. This database contains some features of the objects. In this research, system can
exactly record the result only for objective characteristic because subjective characteristic is not
stable and relative. Position feature is usually changed for every case and size is dependent to other
objects.
The knowledgebase of robot will be increased if it does a new task. This dynamic knowledgebase also
become perfect if there are various feature in one objects. If the human command to the robot to bring
an apple, the robot will know that the color of the apple is usually red and its shape is circle. This
knowledge is never changed until the robot has a new task to bring green or yellow apple.
Having this dynamic knowledge, the robot will be helpful for human assistance in the future.
3. EXPERIMENTAL RESULT
