Thursday, August 27, 2015

Image Understanding (IU)


Throughout these years, the growth of digital media collections has been accelerating, particularly in still images. These artifacts represent huge quantity of heterogeneous information and it is not easy to be managed. Thus, there is a need for the development of human-centered tools for an efficient access and retrieval of them. The capability of current imaging systems have just focused on low-level visual context processing of the images and still limited to describe the high-level semantic conceptualization. The aim of this research is to investigate methods to enable a machine to understand the content of an image at the conceptual level.


Module: Multiple Features-Object Conceptualization

The aim of this research is to investigate existing pixel level processing methods that can be used in isolation or in combination to extract various types of low-level information (i.e. Image Feature Descriptors) from an image. Also, the research is to investigate a suitable data model that will effectively link these feature descriptors to a specific object. The proposed data model is encouraged to be designed as in a form of ontology, thus to leverage the nature intelligent underlying by the class hierarchy. Diagram shows the feature conceptualization process performed by the Proof-of-Concept (POC) system.


Module: Multiple Entities Recognition via Case-base Analysis

The aim of this research is to investigate and develop a suitable technique to map the conceptualized feature descriptors in previous described module to a specific content in image. Feature-object Ontology will be served as a 'brain' for recognizing and identifying entities in the image. Multiple parts of an input image are being accessed for feature extraction process. The extracted features are used as source to compare with the pre-trained features. Diagram shows the POC simulation of multiple parts of image accessing process and the case-based object recognition process.


Module: Object Spatial Relationship Extraction

The aim of this module is to conduct research to formulate suitable techniques for constructing spatial relationship among the objects in an image. Spatial information between objects in an image plays an important role in image understanding process. For example, if there are cars on top of a road, and there are people stands besides the cars, the image might carry the meaning of certain specific event, such as road accident or car exhibition. With this spatial information, in combination with other additional prior knowledge retrieved, we hope to enable a machine to achieve certain level of understanding on an image.


Module: Semantic Image Interpreter via Conceptual Graph

The aim of this module is to investigate and develop techniques to interpret an image by constructing a conceptual graph as the representation of the image content. All of the entities detected from the previous described modules are able to be linked together and represented in a form graph. Investigation effort continue to develop graph processing techniques such as combine, delete and project to further enrich these constructed graphs. The graph data can also be served as an input to another system for further analysis in order to obtain useful knowledge.

Wednesday, November 28, 2012

Workshop on Computer Vision by Prof. E. Trucco

10 - 11 Septermber 2012
Fundamental of Computer Vision with Medical and Video application

Knowledge Technology Week on 3rd - 7th Sep 2012


Demo chair for the Semantic Technology and Knowledge Engineering, year 2012



Mi-Image Understanding System. In Artificial Intelligence Demo



Sunday, November 25, 2012

Training Certificates 2012

Autonomy Software - IDOL Server Essential 10 
21st - 24th October 2012 



Fundamentals of Computer Vision With Medical and Video Application
10th - 11th September 2012 


Presentation Alive, 04th - 05th July 2012, Kuala Lumpur

Sunday, November 27, 2011

Training Certificates 2011

Completion of 3 days MyTriz Workshop
Workshop organized by The Malaysia TRIZ Innovation Association together with The International TRIZ Association (MATRIZ)

Machine Learning Workshop

Performance Unleashed
Speaker profile: http://pqre.mimos.my/perfunleashedconf/speaker.php



Training: Advance Video Analytic and Emerging Video Surveillance

Knowledge Technology Week (KTW) 2011, 18th -22nd July

Sponsorship chair for the Third Malaysian Joint Conference on Artificial Intelligence (MJCAI) and Semantic Technology and Knowledge Engineering (STAKE), year 2011


Paper publication presentation -
S. Y. Tan, C.C. Kiu, Dickson Lukose. Automatic Question Generator Evaluating In 3rd Semantic Technology and Knowledge Engineering (STAKE 2011), UNITEN Putrajaya, Malaysia, July 18-22, 2011


Demo chair for the Artificial Intelligent Demo 2011




Monday, January 10, 2011

Training Certificates 2010

Introduction Graph Theory Course



UML Design Expert - A Thorough Introduction for Analysis and Design (Phase 2)

Tuesday, August 10, 2010

Knowledge Technology Week (Conference & Exhibition)

Event : Knowledge Technology Week (Conference & Exhibition)
Date :
26 July – 30 July 2010
Time :
9am – 5pm
Venue :
Damai Beach Resort, Kuching, Sarawak
Responsibility: Demo presenter





Saturday, August 07, 2010

Excellent Scientist Awarded 2005

“Saintis Cemerlang 2005” (Excellent Scientist) awarded by Ministry of Higher Education, 23 August 2005


Image Understanding (IU)

Throughout these years, the growth of digital media collections has been accelerating, particularly in still images. These artifacts repre...