Novel robust computer vision algorithms for micro autonomous systems

People detection and tracking are an essential component of many autonomous platforms, interactive systems and intelligent vehicles used in various search and rescues operations and similar humanitarian applications. Currently, researchers are focusing on the use of vision sensors such as cameras due to their advantages over other sensor types. Cameras are information rich, relatively inexpensive and easily available. Additionally, 3D information is obtained from stereo vision, or by triangulating over several frames in monocular configurations. Another method to obtain 3D data is by using RGB-D sensors (e.g. Kinect) that provide both image and depth data. This method is becoming more attractive over the past few years due to its affordable price and availability for researchers. The aim of this research was to find robust multi-target detection and tracking algorithms for Micro Autonomous Systems (MAS) that incorporate the use of the RGB-D sensor. Contributions include the discovery of novel robust computer vision algorithms. It proposed a new framework for human body detection, from video file, to detect a single person adapted from Viola and Jones framework. The 2D Multi Targets Detection and Tracking (MTDT) algorithm applied the Gaussian Mixture Model (GMM) to reduce noise in the pre-processing stage. Blob analysis was used to detect targets, and Kalman filter was used to track targets. The 3D MTDT extends beyond 2D with the use of depth data from the RGB-D sensor in the pre-processing stage. Bayesian model was employed to provide multiple cues. It includes detection of the upper body, face, skin colour, motion and shape. Kalman filter proved for speed and robustness of the track management. Simultaneous Localisation and Mapping (SLAM) fusing with 3D information was investigated. The new framework introduced front end and back end processing. The front end consists of localisation steps, post refinement and loop closing system. The back-end focus on the post-graph optimisation to eliminate errors.The proposed computer vision algorithms proved for better speed and robustness. The frameworks produced impressive results. New algorithms can be used to improve performances in real time applications including surveillance, vision navigation, environmental perception and vision-based control system on MAS

The 1st International Conference on Computational Engineering and Intelligent Systems

Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system

Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture

This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy

Emerging Technologies, Environment and Research for Sustainable Aquaculture

Traditional aquaculture and fishery systems have caused a series of ecological and environmental problems. For the purpose of sustainable development, new technologies and policies are highly needed in the field of aquaculture and fisheries. This book mainly focuses on two topics, technologies and environment, and sustainable aquaculture. It is expected that this book can help researchers and technicians in the aquaculture industry to get more new ideas and techniques

Aeronautical engineering: A continuing bibliography with indexes (supplement 259)

This bibliography lists 774 reports, articles, and other documents introduced into the NASA scientific and technical information system in November, 1990. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed