A Measure of Similarity Between Trajectories of Vessels

The measurement of similarity between trajectories of vessels is one of the kernel problems that must be addressed to promote the development of maritime intelligent traffic system (ITS). In this study, a new model of trajectory similarity measurement was established to improve the data processing efficiency in dynamic application and to reflect actual sailing behaviors of vessels. In this model, a feature point detection algorithm was proposed to extract feature points, reduce data storage space and save computational resources. A new synthesized distance algorithm was also created to measure the similarity between trajectories by using the extracted feature points. An experiment was conducted to measure the similarity between the real trajectories of vessels. The growth of these trajectories required measurements to be conducted under different voyages. The results show that the similarity measurement between the vessel trajectories is efficient and correct. Comparison of the synthesized distance with the sailing behaviors of vessels proves that results are consistent with actual situations. The experiment results demonstrate the promising application of the proposed model in studying vessel traffic and in supplying reliable data for the development of maritime ITS

Implementação de um Modelo Bag of Features para Classificação de Frutas

This work explores a classic technique in computer vision, the Bag of Features (BoF) model, in a fruit and vegetable classification problem. There’s an increasing trend in the use of Neural Networks and Deep Learning techniques applied to the automation of processes and systems. This work goes against this trend, examining how a simpler Machine Learning (ML) model would perform. For this, we defined two scenarios, one in a more controlled environment with differences only in light and objects positions, and another with more background clutter. We show that, although the trend is to use bigger and more complex ML models, simpler techniques continue to be relevant in certain scenarios.Este trabalho explora uma técnica clássica de visão computacional, o modelo de Bag of Features, em um problema de classificação de frutas e vegetais. Há uma tendência crescente no uso de técnicas de Redes Neurais e Deep Learning aplicadas na automatização de processos e sistemas. Esse trabalho vai contra essa tendência, examinando o desempenho de um modelo de Machine Learning (ML) mais simples. Para tal, definimos dois cenários, um em que o ambiente é controlado com apenas diferenças de iluminação e posições dos objetos e, outro, com imagens mais desafiadoras, com maior presença de ruído no background. Mostramos que apesar de existir uma clara tendência ao uso de modelos complexos de ML, técnicas mais simples continuam relevantes em certos cenários

Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for eighteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated

An Approach to Automatic Selection of the Optimal Local Feature Detector

Feature matching techniques have significantly contributed in making vision applications more reliable by solving the image correspondence problem. The feature matching process requires an effective feature detection stage capable of providing high quality interest points. The effort of the research community in this field has produced a wide number of different approaches to the problem of feature detection. However, imaging conditions influence the performance of a feature detector, making it suitable only for a limited range of applications. This thesis aims to improve the reliability and effectiveness of feature detection by proposing an approach for the automatic selection of the optimal feature detector in relation to the input image characteristics. Having knowledge of how the imaging conditions will influence a feature detector's performance is fundamental to this research. Thus, the behaviour of feature detectors under varying image changes and in relation to the scene content is investigated. The results obtained through analysis allowed to make the first but important step towards a fully adaptive selection method of the optimal feature detector for any given operating condition

Kodizajn arhitekture i algoritama za lokalizacijumobilnih robota i detekciju prepreka baziranih namodelu

This thesis proposes SoPC (System on a Programmable Chip) architectures for efficient embedding of vison-based localization and obstacle detection tasks in a navigational pipeline on autonomous mobile robots. The obtained results are equivalent or better in comparison to state-ofthe- art. For localization, an efficient hardware architecture that supports EKF-SLAM's local map management with seven-dimensional landmarks in real time is developed. For obstacle detection a novel method of object recognition is proposed - detection by identification framework based on single detection window scale. This framework allows adequate algorithmic precision and execution speeds on embedded hardware platforms.Ova teza bavi se dizajnom SoPC (engl. System on a Programmable Chip) arhitektura i algoritama za efikasnu implementaciju zadataka lokalizacije i detekcije prepreka baziranih na viziji u kontekstu autonomne robotske navigacije. Za lokalizaciju, razvijena je efikasna računarska arhitektura za EKF-SLAM algoritam, koja podržava skladištenje i obradu sedmodimenzionalnih orijentira lokalne mape u realnom vremenu. Za detekciju prepreka je predložena nova metoda prepoznavanja objekata u slici putem prozora detekcije fiksne dimenzije, koja omogućava veću brzinu izvršavanja algoritma detekcije na namenskim računarskim platformama

Object tracking in augmented reality remote access laboratories without fiducial markers

Remote Access Laboratories provide students with access to learning resources without the need to be in-situ (with the assets). The technology endows users with access to physical experiments anywhere and anytime, while also minimising or distributing the cost of operation for expensive laboratory equipment. Augmented Reality is a technology which provides interactive sensory feedback to users. The user experiences reality through a computer-based user interface with additional computer-generated information in the form applicable to the targeted senses. Recent advances in high definition video capture devices, video screens and mobile computers have driven resurgence in mainstream Augmented Reality technologies. Lower cost and greater processing power of microprocessors and memory place the resources in the hands of developers and users alike, allowing education institutes to invest in technologies that enhance the delivery of course content. This increase in pedagogical resources has already allowed the phenomenon of education at a distance to reach students from a wide range of demographics, improving access and outcomes in multiple disciplines. Incorporating Augmented Reality into Remote Access Laboratories resources has the benefit of improving overall user immersion into the remote experiment, thus improving student engagement and understanding of the delivered material. Visual implementations of Augmented Reality rely on providing the user with seamless integration of the current environment (through mobile device, desktop PC, or heads up display) with computer generated artificial visual artefacts. Virtual objects must appear in context to the current environment, and respond in a realistic period, or else the user suffers from a disjointed and confusing blend of real and virtual information. Understanding and interacting with the visual scene is controlled through Computer Vision algorithms, and are crucial in ensuring that the AR systems co-operate with the data discovered through the systems. While Augmented Reality has begun to expand in the educational environment, currently, there is still very little overlap of Augmented Reality technologies with Remote Access Laboratories. This research has investigated Computer Vision models that support Augmented Reality technologies such that live video streams from Remote Laboratories are enhanced by synthetic overlays pertinent to the experiments. Orientation of synthetic visual overlays requires knowledge of key reference points, often performed by fiducial markers. Removing the equipment’s need for fiducial markers and a priori knowledge simplifies and accelerates the uptake and expansion of the technology. These works uncover hybrid Computer Vision models which require no prior knowledge of the laboratory environment, including no fiducial markers or tags to track important objects and references. Developed models derive all relevant data from the live video stream and require no previous knowledge regarding the configuration of the physical scene. The new image analysis paradigms, (Two-Dimensional Colour Histograms and Neighbourhood Gradient Signature) improve the current state of markerless tracking through the unique attributes discovered within the sequential video frames. Novel methods are also established, with which to assess and measure the performance of Computer Vision models. Objective ground truth images minimise the level of subjective interference in measuring the efficacy of CV edge and corner detectors. Additionally, locating an effective method to contrast detected attributes associated with an image or object, has provided a means to measure the likelihood of an image match between video frames. In combination with existing material and new contributions, this research demonstrates effective object detection and tracking for Augmented Reality systems within a Remote Access Laboratory environment, with no requirement for fiducial markers, or prior knowledge of the environment. The models that have been proposed in the work can be generalised to be used in any cyber-physical environment that facilitates peripherals such as cameras and other sensors

Performance comparisons of contour-based corner detectors

Abstract— Corner detectors have many applications in computer vision and image identification and retrieval. Contour-based corner detectors directly or indirectly estimate a significance measure (e.g., curvature) on the points of a planar curve, and select the curvature extrema points as corners. While an extensive number of contour-based corner detectors have been proposed over the last four decades, there is no comparative study of recently proposed detectors. This paper is an attempt to fill this gap. The general framework of contour-based corner detection is presented, and two major issues – curve smoothing and curvature estimation, which have major impacts on the corner detection performance, are discussed. A number of promising detectors are compared using both automatic and manual evaluation systems on two large datasets. It is observed that while the detectors using indirect curvature estimation techniques are more robust, the detectors using direct curvature estimation techniques are faster