Redia Mohd Redzuwan

Bio: Redia Mohd Redzuwan is an academic researcher from Universiti Tenaga Nasional. The author has contributed to research in topics: RANSAC & Feature detection (computer vision). The author has an hindex of 2, co-authored 3 publications receiving 13 citations.

Affine versus projective transformation for SIFT and RANSAC image matching methods

Abstract: Image registration is a process of determining the geometrical transformation that aligns two or more images taken from different viewpoints and sensors at different times. Scale Invariant Feature Transform (SIFT) method has gained more popularity since it extracts the highest number of features and matching points compared to Speeded-Up Robust Feature (SURF) and Harris Corner Detector at little computational cost. In this paper, a combination of SIFT and Random Sample Consensus (RANSAC) is used to produce panoramic image. In order to reject outliers and estimate the transformation model, affine and projective transformations are used to study the best geometrical transformations methods to be used. The results shows that the projective transformation has a better performance in terms of accuracy.

Wireless communication technology's selection for landslide monitoring in rural area

Abstract: For the past 5 decades, a major number of Tenaga Nasional Berhad (TNB) transmission towers that traversed throughout Peninsular Malaysia have already existed. Some of the tower located at a mountainous terrain and suspected to be insecure and risky. One method is monitoring the landslide in real-time, by deploying various sensor at the high risk area. This paper presents the applicability of multicriteria decision making (MCDM) method, specifically Analytical Hierarchy Process (AHP) to select the wireless communication technology for transmitting the sensor data to the control centre. Criteria that influence the selection are technical, economical, infrastructure and functionality. On the other hand, four alternative options are fiber optical network, cellular, wireless LAN and zigBee.

Transmission Tower Environment Monitoring Using UAV

Abstract: Power utility engineers used to conduct ground survey to collect topographic data. Therefore, they can get detailed and accurate information, but these techniques take a lot of labors and expenses, and spending times for the surveying. An attractive solution to the ground survey is using images taken using Unmanned Aerial Vehicle (UAV). Images captured from UAV can be collected quickly and efficiently over the same area covered in the land survey, in a fraction of the time. The purpose of this research is to mosaic the large numbers of spectral images together into a region wide panoramic image which allows experts to analyze the data for transmission tower monitoring purposes.

Multi-Camera Imaging System for UAV Photogrammetry.

Abstract: In the last few years, it has been possible to observe a considerable increase in the use of unmanned aerial vehicles (UAV) equipped with compact digital cameras for environment mapping. The next stage in the development of photogrammetry from low altitudes was the development of the imagery data from UAV oblique images. Imagery data was obtained from side-facing directions. As in professional photogrammetric systems, it is possible to record footprints of tree crowns and other forms of the natural environment. The use of a multi-camera system will significantly reduce one of the main UAV photogrammetry limitations (especially in the case of multirotor UAV) which is a reduction of the ground coverage area, while increasing the number of images, increasing the number of flight lines, and reducing the surface imaged during one flight. The approach proposed in this paper is based on using several head cameras to enhance the imaging geometry during one flight of UAV for mapping. As part of the research work, a multi-camera system consisting of several cameras was designed to increase the total Field of View (FOV). Thanks to this, it will be possible to increase the ground coverage area and to acquire image data effectively. The acquired images will be mosaicked in order to limit the total number of images for the mapped area. As part of the research, a set of cameras was calibrated to determine the interior orientation parameters (IOPs). Next, the method of image alignment using the feature image matching algorithms was presented. In the proposed approach, the images are combined in such a way that the final image has a joint centre of projections of component images. The experimental results showed that the proposed solution was reliable and accurate for the mapping purpose. The paper also presents the effectiveness of existing transformation models for images with a large coverage subjected to initial geometric correction due to the influence of distortion.

Deep Learning for Critical Infrastructure Resilience

Abstract: Ensuring the resiliency of critical infrastructures is essential in modern society, but much of the deployed infrastructure has yet to fully leverage modern technical developments. This pap...

Panoramic image mosaic technology based on SIFT algorithm in power monitoring

Abstract: Panoramic image plays a very important role in power monitoring, which is conducive to a comprehensive understanding of the surrounding environment for transmission line operators and maintenance personnel and provides an important guarantee for line safety and information collection. In this paper, based on SIFT (Scale-invariant feature transform) algorithm, feature matching of video surveillance images is carried out, and then multi-image fusion and natural mosaic are realized. The software and experiment results show that the algorithm can realize the full mosaic of the power environment panorama, and can be applied to the power environment monitoring system. It effectively provides the omnidirectional information of the surrounding environment of the transmission line and has been effectively applied.

Keypoint-based mapping analysis on transformed Side Scan Sonar images

Abstract: In recent years, detecting wrecks and submerged sites is a challenging task and time-consuming. A fine technique is required to pick out features from the sea-floor. Side Scan Sonar (SSS) has been used for underwater imaging to detect submerged objects on the sea-floor. Due to the rotational and translational instabilities of SSS tow-fish, the acquired SSS images may be distorted. Hence, this paper concentrates on the geometric transformation analysis for different shadows of SSS images. Based on the intensity-based threshold method, the SSS images are classified as high-shadows, low-shadows, no-shadows, and semi-shadows. Different keypoint detector and mapping algorithms are considered for the extraction of keypoints (features) and matching of those detected points. The point mapping performance was evaluated using Repeatability rate and Mapping rate. It is observed that the Mapping rate of 98.7% is obtained using Scale Invariant Feature Transform (SIFT) with Fast Library for Approximate Nearest Neighbor Search (FLANN) and RANdom Sample Consensus (RANSAC). It is also inferred that the Repeatability rate is 0.8 using Oriented FAST & Rotated BRIEF (ORB) compared with other algorithms considered.