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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
For example, we have four objects which have 2 similar color and shape. The user points to one
direction where some objects are located. He wants a red apple which located among the objects is brought
by the robot. This example refers to case 2 at table 2. The system makes some questions to confirm to
the user.
Robot : Is the target object red?
User : Yes
Robot : Is the target object in the up side?
User : No.
Robot : This one is the target object.
What should I do then? There are some steps to find the target objects:
1. The system merges some nearest color region for removing background and than labels the objects.
In this process, the number of candidate target objectss, their size and shape are determined but
not appeared. After all of objects recognized by the system, it asks the user based on general rule.
In this case, the system asks a yesno question that contain about color to the user.
2. The system removes unnecessary objects based on user answer and than determines the current
condition of target objects. 3. The system formulates a next question in current
condition by acting on general rule system. There are only two objects less than 3 in the
current scene. Therefore, the system makes a question based on subjectivity characteristic
feature that has been explained before.
4. The system asks the second question and remove unnecessary object to get a real target objects.
Figure 8 shows a visualization of this case.
a
b
vocabulary objectivity
color size
position shape
Comfortable Dialog for Object Detection – Rahmadi Kurnia
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c
d Figure 8. Human robot interaction in case 2. a Segmentation
result. b Merging nearest color region result c AaR method result d.Final result
5. CONCLUSION
Robots need vision to carry out their tasks. However, conventional vision systems cannot work in
complex scenes. We have proposed to use human users assistance to solve this problem. The robot asks
a question to the user when it cannot detect the target object. It generates a sequence of utterances that can
lead to determine the object efficiently and user- friendly.
It determines what and how to ask the user by considering the image processing results and the
characteristics of object image attributes. We obtain promising experimental results.
The current system is a small system to examine whether or not the approach is promising. Since this
has been confirmed, we will develop a larger system that can deal with more complex situations.
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A SOCIAL INFORMATICS OVERVIEW OF E-GOVERNMENT IMPLEMENTATION: ITS SOCIAL ECONOMICS AND
RESTRUCTURING IMPACT
Irwan Sembiring, Krismiyati
Faculty of Information Technology Satya Wacana Christian University.
email : irwanmybiring.com, xme_blessedyahoo.com
ABSTRACT
Social informatics is a new field of study concerning the design, implementation, and the use of
ICT that take into account its interaction in contextual and cultural context. This kind of study usually uses
three approaches which can be combined in its application. They are normative orientation, analytical
orientation and critical orientation. This study will investigate the social economics and restructuring
impacts of E-Government implementation in Selayar Regency South Sulawesi. The result of the study
shows that globally the advantages got from the implementation is still very little compared to the
investment spent on this project. Keywords : social informatics, E-government in
selayar regency.
1. INTRODUCTION