Sud Sudirman

A robust region-adaptive dual image watermarking technique

Despite the progress in digital image watermarking technology, the main objectives of the majority of research in this area remain to be the improvement in robustness to attack. In this paper, a novel watermarking technique is proposed using a region-adaptive approach to further improve upon criteria. Watermark data is embedded on different regions of the host image using a combination of Discrete Wavelet Transform and Singular Value Decomposition techniques. The technique is derived from an earlier hypothesis that the robustness of a watermarking process can be improved by using watermark data which frequency spectrum not dissimilar to that of the host data. To facilitate this, the technique utilises dual watermarking technologies and embed parts of the watermark images into selected regions in the host image. Our experiment shows our technique has improved the robustness of the watermark data to image processing attacks and geometric attacks, thus validating the earlier hypothesis.

Analysis of Digital Image Watermark Attacks

Digital watermarking is one of the most widely used techniques for protection of ownership rights of digital audio, images and video. Its commercial applications range from copyright protection to digital rights management. The success of a digital watermarking technology depends heavily on its robustness to withstand attacks that are aimed at removing or destroying the watermark from its host data. This paper provides analysis of a number of digital image watermark attacks and attempts to classify them into categories. A set of experimental results are also provided to show the effect of these attacks on watermarks produced using different watermarking techniques.

A framework for sharing and storing serendipity moments in human life memory

Every person has their own serendipitous moments; joyful moments that they hope can be kept in their mind forever. Unfortunately our memory can sometimes fail to retrieve all the details of when, where and why something happened. Today, with advances in technology, we are able to capture our serendipitous moments as digital images, videos, audio, text and as other forms of data, making use of the huge capacities of storage available to us. In this paper, we describe a system that can make personal serendipity moments available to be shared with trusted peer group members. We propose a flexible and scalable system for storing serendipitous moments in a human life memory framework and share them with friends without the need for a central server through a peer-to-peer network.

Robust digital image watermarking using region adaptive embedding technique

Improving the robustness of watermark in withstanding attacks has been one of the main research objectives in digital image watermarking. In this paper we propose a novel region-adaptive watermarking technique that can provide improvements in both robustness and visual quality of the watermarks when compared to the original, non-region-adaptive, embedding technique. The proposed technique, which is derived from our previously published research finding, shows that the relative difference in spectral distributions between the watermark data and the host image plays an important role in improving the watermark robustness and transparency.

A Binary Color Vision Framework for Content-Based Image Indexing

We have developed an elegant and effective method for content-based color image indexing and retrieval. A color image is first represented as a sequence of binary images each captures the presence or absence of a predefined visual feature, such as color. Binary vision algorithms are then used to analyze the geometric properties of the bit planes. The size, shape, or geometry moment of each connected binary region on the visual feature planes can then be computed to characterize the image content. In this paper, we introduce the color blob size table (Cbst) as an image content descriptor. Cbst is a 2-D array that captures the co-occurrence statistics of connected regions sizes and their colors. Unlike other similar methods in the literature, Cbst enables the employment of simple numerical metric measures to compare image similarity based on the properties of region segments. We will demonstrate the effectiveness of the method through its application to content-based retrieval from image database.

Hybrid 3D Rendering of Large Map Data for Crisis Management

In this paper we investigate the use of games technologies for the research and the development of 3D representations of real environments captured from GIS information and open source map data. Challenges involved in this area concern the large data-sets to be dealt with. Some existing map data include errors and are not complete, which makes the generation of realistic and accurate 3D environments problematic. The domain of application of our work is crisis management which requires very accurate GIS or map information. We believe the use of creating a 3D virtual environment using real map data whilst correcting and completing the missing data, improves the quality and performance of crisis management decision support system to provide a more natural and intuitive interface for crisis managers. Consequently, we present a case study into issues related to combining multiple large datasets to create an accurate representation of a novel, multi-layered, hybrid real-world maps. The hybrid map generation combines LiDAR, Ordnance Survey, and OpenStreetMap data to generate 3D cities spanning 1 km2. Evaluation of initial visualised scenes is presented. Initial tests consist of a 1 km2 landscape map containing up to 16 million vertices’ and run at an optimal 51.66 frames per-second.

Color image coding, indexing and retrieval using binary space partitioning tree

This paper presents a unified approach to colour image coding, content-based indexing, and retrieval for database applications. The binary space partitioning (BSP) tree, traditionally used in gray scale image coding (Wu 1992, Radha et al. 1996) is extended to represent colour images. The BSP tree, hence the structure, of the image is explicitly coded. A method is developed to compute the similarities of images based on their BSP tree representations. In image database applications, the images in the database are coded by BSP tree to achieve a good balance between storage efficiency and easy manipulation of image data. Content-based image querying is performed in the compressed bit streams by comparing the BSP tree of the query image with those of the images in the database.

A Framework on a Computer Assisted and Systematic Methodology for Detection of Chronic Lower Back Pain Using Artificial Intelligence and Computer Graphics Technologies

Back pain is one of the major musculoskeletal pain problems that can affect many people and is considered as one of the main causes of disability all over the world. Lower back pain, which is the most common type of back pain, is estimated to affect at least 60 % to 80 % of the adult population in the United Kingdom at some time in their lives. Some of those patients develop a more serious condition namely Chronic Lower Back Pain in which physicians must carry out a more involved diagnostic procedure to determine its cause. In most cases, this procedure involves a long and laborious task by the physicians to visually identify abnormalities from the patient’s Magnetic Resonance Images. Limited technological advances have been made in the past decades to support this process. This paper presents a comprehensive literature review on these technological advances and presents a framework of a methodology for diagnosing and predicting Chronic Lower Back Pain. This framework will combine current state-of-the-art computing technologies including those in the area of artificial intelligence, physics modelling, and computer graphics, and is argued to be able to improve the diagnosis process.

Region adaptive digital image watermarking system using DWT-SVD algorithm

Improving the robustness of watermark in withstanding attacks has been one of the main research objectives in digital image watermarking. In this paper we propose a novel region-adaptive watermarking technique that can provide improvements in both robustness and visual quality of the watermarks when compared to the original, non-region-adaptive, embedding technique. The proposed technique, which is derived from our previously published research finding, shows that the relative difference in spectral distributions between the watermark data and the host image plays an important role in improving the watermark robustness and transparency.

Region-Adaptive Watermarking System and Its Application

Digital image watermarking is one of the most widely used techniques for protection of ownership rights of digital images. The main objective of the majority of research in this area remains to be the improvement in imperceptibility and robustness to attack. In this paper, we present a novel watermarking algorithm using a region-adaptive approach to further improve upon these criteria. The watermark data is embedded on different regions of the host image using a combination of discrete wavelet transform and singular value decomposition technique. In addition, there is a novel use the region-adaptive watermarking technique as a means to detect if certain types of attack have occurred. This is a unique feature of our watermarking algorithm which separates it from other state-of-the-art watermarking techniques. The watermark detection process uses coefficients derived from the region-adaptive watermarking algorithm in a linear classifier. The experiment conducted to validate this feature shows that all watermark attacks can be correctly detected and identified.