Amr Hussein Yousef

Bio: Amr Hussein Yousef is an academic researcher from Old Dominion University. The author has contributed to research in topics: Wiener filter & Lidar. The author has an hindex of 6, co-authored 22 publications receiving 110 citations. Previous affiliations of Amr Hussein Yousef include College of Business Administration & Alexandria University.

High-Speed Image Registration Algorithm with Subpixel Accuracy

Abstract: A new, fast and computationally efficient lateral subpixel shift registration algorithm is presented. It is limited to register images that differ by small subpixel shifts otherwise its performance degrades. This algorithm significantly improves the performance of the single-step discrete Fourier transform approach proposed by Guizar-Sicairos and can be applied efficiently on large dimension images. It reduces the dimension of Fourier transform of the cross correlation matrix and reduces the discrete Fourier transform (DFT) matrix multiplications to speed up the registration process. Simulations show that our algorithm reduces computation time and memory requirements without sacricing the accuracy associated with the usual FFT approach accuracy.

Movie success prediction using data mining

Abstract: In this project, we developed a mathematical model to predict the success and failure of the upcoming movies based on several attributes. Some of the criteria in calculating movie success included budget, actors, director, producer, set locations, story writer, movie release day, competing movie releases at the same time, music, release location and target audience. The success prediction of a movie plays a vital role in movie industry because it involves huge investments. However, success cannot be predicted based on a particular attribute. So, we have built a model based on interesting relation between attributes. The movie industry can use this model to modify the movie criteria for obtaining likelihood of blockbusters. Also, this model can be used by movie watchers in determining a blockbuster before purchasing a ticket. Each of the criteria involved was given a weight and then the prediction was made based on these. For example, if a movies budget was below 5 million, the budget was given a lower weight. Depending on the number of actors, directors and producers past successful movies, each of these categories was given a weight. If the movie was to be released on a weekend, it was given higher weight because the chances of success were greater. If with the release of a movie, there was another high success movie released, a lower weight was given to the release time indicating that the chances of movie success were low due to the competition. The criteria were not limited just to the ones mentioned. There were be additional factors discussed in this work. We have conducted our work with simulation data.

Shoreline extraction from the fusion of LiDAR DEM data and aerial images using mutual information and genetic algrithms

Abstract: As sea level rises and coastal populations continue to grow, there is an increased demand for understanding the accurate position of the shorelines. The automatic extraction of shorelines utilizing the digital elevation models (DEMs) obtained from light detection and ranging (LiDAR), aerial images and multi-spectral images has become very promising. In this paper, we propose a new algorithm that can effectively extract shorelines from fused LiDAR DEMs with aerial images depending on the availability of training data. The LiDAR data and the aerial image are fused together by maximizing the mutual information using the genetic algorithm. The extraction of shoreline is obtained by segmenting the fused data into water and land by means of the support vector machines classifier. Compared with other relevant techniques in literature, the proposed method offers better accuracy in shoreline extraction.

Shoreline extraction from light detection and ranging digital elevation model data and aerial images

Abstract: There is an increased demand for understanding the accurate position of the shorelines. The automatic extraction of shorelines utilizing the digital elevation models (DEMs) obtained from light detection and ranging (LiDAR), aerial images, and multispectral images has become very promising. In this article, we develop two innovative algorithms that can effectively extract shorelines depending on the available data sources. The first is a multistep morphological technique that works on LiDAR DEM with respect to a tidal datum, whereas the second depends on the availability of training data to extract shorelines from LiDAR DEM fused with aerial images. Unlike similar techniques, the morphological approach detects and eliminates the outliers that result from waves, etc., by means of an anomaly test with neighborhood constraints. Additionally, it eliminates docks, bridges, and fishing piers along the extracted shorelines by means of Hough transform. The second approach extracts the shoreline by means of color space conversion of the aerial images and the support vector machines classifier to segment the fused data into water and land. We perform Monte-Carlo simulations to estimate the confidence interval for the error in shoreline position. Compared with other relevant techniques in literature, the proposed methods offer better accuracy in shoreline extraction.

On the restoration of the microscanned images captured from unmanned airborne vehicles

Abstract: Unmanned Airborne Vehicles (UAVs) during flight capture a set of images that have slightly different looks of the scene. These images often contain a sufficient overlapped area between them and subpixel shifts of random fractions that allows for constructing a high resolution image within the overlapped area. The high resolution image may have a poor visual quality due to the degradations during acquisition and display processes such as blurring caused by the system optics or aliasing due to sampling. A technique referred to as the microscanning is an effective method for reducing aliasing and increasing spatial resolution. By moving the field of view (FOV) on the detector array with predetermined sub-pixel shifts, both aliasing reduction and resolution improvement are realized with increasing effective spatial sampling periods. In this paper we introduce the idea of the microscanning in UAV captured images. Based on the continuous-discrete-continuous (CDC) model, a Wiener restoration filter is used to restore the visually poor quality image to a super resolution (SR) image.

Image Registration With Fourier-Based Image Correlation: A Comprehensive Review of Developments and Applications

Abstract: Fourier-based image correlation is a powerful area-based image registration technique, which involves aligning images based on a translation model or similarity model by means of the image information and operation in the frequency domain. In recent years, Fourier-based image correlation has made significant progress and attracted extensive research interest in a variety of applications, especially in the field of photogrammetry and remote sensing, leading to the development of a number of subpixel methods that have improved the accuracy and robustness. However, to date, a detailed review of the literature related to Fourier-based image correlation is still lacking. In this review, we aim at providing a comprehensive overview of the fundamentals, developments, and applications of image registration with Fourier-based image correlation methods. Specifically, this review introduces the principal laws underlying these methods, presents a survey of the existing subpixel methods calculated both in the spatial domain and in the frequency domain, summarizes the major applications from three aspects, and discusses the challenges and possible directions of future research. This review is expected to be beneficial for researchers working in the relevant fields to obtain an insight into the current state of the art, to develop new variants, to explore potential applications, and to suggest promising future trends of image registration with Fourier-based image correlation.

Automatic land-water classification using multispectral airborne LiDAR data for near-shore and river environments

Abstract: Rapid mapping of near-shore and coastal regions has become an indispensable task for the local authority to serve the purpose of coastal management and post-disaster monitoring. Aerial photogrammetry and satellite remote sensing have been utilized to fulfill such a task in the last few decades. Airborne LiDAR can further compensate the drawbacks of these image capturing approaches as a result of the direct geo-referenced 3D point cloud. The recent introduction of multispectral airborne LiDAR, such as the Teledyne Optech Titan, can potentially enhance the capability of water mapping, minimize the involvement of manual intervention and reduce the use of supplementary information or ancillary data. This study demonstrates the use of multispectral airborne LiDAR data for automatic land-water classification under different coastal and inland river environments. Two automatic training data selection methods are proposed. The first method utilizes Gaussian mixture model (GMM) to split preliminarily the land and water region based on the elevation/intensity histogram, and the second method is developed based on the use of scan line intensity-elevation ratio (SLIER). Subsequently, various LiDAR-derived feature sets, particularly based on the multispectral LiDAR intensity, are constructed in order to serve as an input for the log-likelihood classification model. Two optional post-classification enhancements can be implemented to further adjust the misclassified data points. The proposed workflow was evaluated with four Optech Titan datasets collected for different near-shore and river environments that are located nearby Lake Ontario, Ontario, Canada. Our experimental work demonstrated that the multispectral LiDAR intensity data was capable of enhancing the classification capability, where an overall accuracy better than 96% was achieved in most of the cases.

Automatic Coastline Extraction Using Edge Detection and Optimization Procedures

Abstract: Coastal areas are quite fragile landscapes as they are among the most vulnerable to climate change and natural hazards. Coastline mapping and change detection are essential for safe navigation, resource management, environmental protection, and sustainable coastal development and planning. In this paper, we proposed a new methodology for the automatic extraction of coastline, using aerial images. This method is based on edge detection and active contours (snake method). Initially the noise of the image is reduced which is followed by an image segmentation. The output images are further processed to remove all small spatial objects and to concentrate on the spatial objects of interests. Then, the morphological operators are applied. We used aerial images taken from an aircraft and high-resolution satellite images from a coastal area in Crete, Greece, and we compared the results with geodetic measurements, to validate the methodology.

Alignment methods for nanotomography with deep subpixel accuracy.

Abstract: As the resolution of X-ray tomography improves, the limited long-term stability and accuracy of nanoimaging tools does not allow computing artifact-free three-dimensional (3D) reconstructions without an additional step of numerical alignment of the measured projections. However, the common iterative alignment methods are significantly more computationally demanding than a simple tomographic reconstruction of the acquired volume. Here, we address this issue and present an alignment toolkit, which exploits methods with deep-subpixel accuracy combined with a multi-resolution scheme. This leads to robust and accurate alignment with significantly reduced computational and memory requirements. The performance of the presented methods is demonstrated on simulated and measured datasets for tomography and also laminography acquisition geometries. A GPU accelerated implementation of our alignment framework is publicly available.