Modeling Bottom-Up Visual Attention Using Dihedral Group D4

Published version. Source at http://dx.doi.org/10.3390/sym8080079 In this paper, first, we briefly describe the dihedral group D4 that serves as the basis for calculating saliency in our proposed model. Second, our saliency model makes two major changes in a latest state-of-the-art model known as group-based asymmetry. First, based on the properties of the dihedral group D4, we simplify the asymmetry calculations associated with the measurement of saliency. This results is an algorithm that reduces the number of calculations by at least half that makes it the fastest among the six best algorithms used in this research article. Second, in order to maximize the information across different chromatic and multi-resolution features, the color image space is de-correlated. We evaluate our algorithm against 10 state-of-the-art saliency models. Our results show that by using optimal parameters for a given dataset, our proposed model can outperform the best saliency algorithm in the literature. However, as the differences among the (few) best saliency models are small, we would like to suggest that our proposed model is among the best and the fastest among the best. Finally, as a part of future work, we suggest that our proposed approach on saliency can be extended to include three-dimensional image data

3D SEM Surface Reconstruction: An Optimized, Adaptive, and Intelligent Approach

Structural analysis of microscopic objects is a longstanding topic in several scientific disciplines, including biological, mechanical, and material sciences. The scanning electron microscope (SEM), as a promising imaging equipment has been around to determine the surface properties (e.g., compositions or geometries) of specimens by achieving increased magnification, contrast, and resolution greater than one nanometer. Whereas SEM micrographs still remain two-dimensional (2D), many research and educational questions truly require knowledge and information about their three-dimensional (3D) surface structures. Having 3D surfaces from SEM images would provide true anatomic shapes of micro samples which would allow for quantitative measurements and informative visualization of the systems being investigated. In this research project, we novel design and develop an optimized, adaptive, and intelligent multi-view approach named 3DSEM++ for 3D surface reconstruction of SEM images, making a 3D SEM dataset publicly and freely available to the research community. The work is expected to stimulate more interest and draw attention from the computer vision and multimedia communities to the fast-growing SEM application area

Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks

A learning control strategy is preferred for the control and guidance of a fixed-wing unmanned aerial vehicle to deal with lack of modeling and flight uncertainties. For learning the plant model as well as changing working conditions online, a fuzzy neural network (FNN) is used in parallel with a conventional P (proportional) controller. Among the learning algorithms in the literature, a derivative-free one, sliding mode control (SMC) theory-based learning algorithm, is preferred as it has been proved to be computationally efficient in real-time applications. Its proven robustness and finite time converging nature make the learning algorithm appropriate for controlling an unmanned aerial vehicle as the computational power is always limited in unmanned aerial vehicles (UAVs). The parameter update rules and stability conditions of the learning are derived, and the proof of the stability of the learning algorithm is shown by using a candidate Lyapunov function. Intensive simulations are performed to illustrate the applicability of the proposed controller which includes the tracking of a three-dimensional trajectory by the UAV subject to time-varying wind conditions. The simulation results show the efficiency of the proposed control algorithm, especially in real-time control systems because of its computational efficiency

Probabilistic correspondence analysis for neuroimaging problems

Establecer correspondencias de forma significativas entre los objetivos como en los problemas de neuroimagen es crucial para mejorar los procesos de correspondencia. Por ejemplo, el problema de correspondencia consiste en encontrar relaciones significativas entre cualquier par de estructuras cerebrales como en el problema de registro estático, o analizar cambios temporales de una enfermedad neurodegenerativa dada a través del tiempo para un análisis dinámico de la forma del cerebro..

Air Force Institute of Technology Research Report 2017

This Research Report presents the FY18 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs)

Design and Development of Climbing Robotic Systems for Automated Inspection of Steel Structures and Bridges

Steel structures are indispensable parts of modern civilization, with typical civil infrastructures including bridges, wind turbines, electric towers, oil rigs, ships, and submarines, all made of steel. These structures require frequent maintenance to ensure safety and longevity. Steel bridges are the most challenging architectures due totheir complexity and height. Most inspections are conducted manually by professional human inspectors with special devices to inspect visible damages and defects on or inside these structures. However, this procedure is usually highly time-consuming, costly, and risky. Automated solutions are desired to address this problem. However, arduous engineering is delaying progress. A complete system needs to deal with three main problems: (1) locomotive performance for the high complexity of steel bridges, including differential curvatures, transitions between beams, and obstacles; (2) data collection capability, inclusive of visible and invisible damages, in-depth information such as vibration, coat, and material thickness, etc.; and (3) working conditions made up of gust winds. To achieve such a complete system, this dissertation presents novel developments of inspection-climbing robots. Five different robot versions are designed to find the simplest and most effective configuration as well as control manner. Our approach started with (1) a transformable tank-like robot integrated with a haptic device and ii two natural-inspired locomotion, (2) a roller chain-like robot, (3) a hybrid worming mobile robot, (4) a multi-directional bicycle robot, and (5) an omni-directional climbing Robot, identified as the most potential solution for automated steel bridge inspection. For each robotic development, detailed mechanical analysis frameworks are presented. Both lab tests and field deployments of these robotic systems have been conducted to validate the proposed designs

A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving

Radar sensors were among the first perceptual sensors used for automated driving. Although several other technologies such as lidar, camera, and ultrasonic sensors are available, radar sensors have maintained and will continue to maintain their importance due to their reliability in adverse weather conditions. Virtual methods are being developed for verification and validation of automated driving functions to reduce the time and cost of testing. Due to the complexity of modelling high-frequency wave propagation and signal processing and perception algorithms, sensor models that seek a high degree of accuracy are challenging to simulate. Therefore, a variety of different modelling approaches have been presented in the last two decades. This paper comprehensively summarises the heterogeneous state of the art in radar sensor modelling. Instead of a technology-oriented classification as introduced in previous review articles, we present a classification of how these models can be used in vehicle development by using the V-model originating from software development. Sensor models are divided into operational, functional, technical, and individual models. The application and usability of these models along the development process are summarised in a comprehensive tabular overview, which is intended to support future research and development at the vehicle level and will be continuously updated

Three dimensional visualisation of Port Dickson Polytechnic campus in cityengine web viewer

Development of modern technology and its ability to store, process and supply of digital data has led to a demand for three dimensional (3D) modeling of virtual campus has increased dramatically. Of late, many educational institutes have developed their own campus geodatabase in 3D environment. In this research, the emphasis is given on the development of the 3D building model campus using close range photogrammetry approach due to high cost of data acquisition techniques using airbone laser scanning, terrestrial laser scanning techniques and availability of data. Thus, the close range photogrammetry technique has been selected due to low cost method for data acquisition through capturing the selected building photographs using digital camera. In order to develop the 3D building models, six buildings with different architectural designs and geometries in Port Dickson Polytechnic campus have been chosen as prototype which are modelled using close range photogrammtry approach. The photographs of buildings are then processed using PhotoModeler software to produce the 3D building models in the level of detail (LoD) 2. The building models are textured with the real photographs taken from the field while the roof of the buildings are edited using the SketchUp software. The building models are also georeferenced to the real world coordinate system based on the geocentric Rectified Skew Orthomorphic (RSO) coordinate system. Due to the lack of information access on the web in 3D, CityEngine Web Viewer is used for 3D visualisation of the building models and supported features are also be added to create a realistic model of 3D virtual campus. Through the viewer, the users are able to navigate the 3D building models, zooming and performing the spatial query to extract the information of the buildings. The accuracy of 3D buildings models are evaluated and determined based on the visual analyses and quantitative analyses. At the end of the research, the 3D buildings models can be visualised in the LoD 2

Visualization for Recommendation Explainability: A Survey and New Perspectives

Providing system-generated explanations for recommendations represents an important step towards transparent and trustworthy recommender systems. Explainable recommender systems provide a human-understandable rationale for their outputs. Over the last two decades, explainable recommendation has attracted much attention in the recommender systems research community. This paper aims to provide a comprehensive review of research efforts on visual explanation in recommender systems. More concretely, we systematically review the literature on explanations in recommender systems based on four dimensions, namely explanation goal, explanation scope, explanation style, and explanation format. Recognizing the importance of visualization, we approach the recommender system literature from the angle of explanatory visualizations, that is using visualizations as a display style of explanation. As a result, we derive a set of guidelines that might be constructive for designing explanatory visualizations in recommender systems and identify perspectives for future work in this field. The aim of this review is to help recommendation researchers and practitioners better understand the potential of visually explainable recommendation research and to support them in the systematic design of visual explanations in current and future recommender systems.Comment: Updated version Nov. 2023, 36 page