Human-Centric Chronographics:Making Historical Time Memorable

A series of experiments is described, evaluating user recall of visualisations of historical chronology. Such visualisations are widely created but have not hitherto been evaluated. Users were tested on their ability to learn a sequence of historical events presented in a virtual environment (VE) fly-through visualisation, compared with the learning of equivalent material in other formats that are sequential but lack the 3D spatial aspect. Memorability is a particularly important function of visualisation in education. The measures used during evaluation are enumerated and discussed. The majority of the experiments reported compared three conditions, one using a virtual environment visualisation with a significant spatial element, one using a serial on-screen presentation in PowerPoint, and one using serial presentation on paper. Some aspects were trialled with groups having contrasting prior experience of computers, in the UK and Ukraine. Evidence suggests that a more complex environment including animations and sounds or music, intended to engage users and reinforce memorability, were in fact distracting. Findings are reported in relation to the age of the participants, suggesting that children at 11–14 years benefit less from, or are even disadvantaged by, VE visualisations when compared with 7–9 year olds or undergraduates. Finally, results suggest that VE visualisations offering a ‘landscape’ of information are more memorable than those based on a linear model. Keywords: timeline, chronographics

Painterly rendering using human vision

Painterly rendering has been linked to computer vision, but we propose to link it to human vision because perception and painting are two processes that are interwoven. Recent progress in developing computational models allows to establish this link. We show that completely automatic rendering can be obtained by applying four image representations in the visual system: (1) colour constancy can be used to correct colours, (2) coarse background brightness in combination with colour coding in cytochrome-oxidase blobs can be used to create a background with a big brush, (3) the multi-scale line and edge representation provides a very natural way to render fi ner brush strokes, and (4) the multi-scale keypoint representation serves to create saliency maps for Focus-of-Attention, and FoA can be used to render important structures. Basic processes are described, renderings are shown, and important ideas for future research are discussed.

Looking through the eyes of the painter: from visual perception to non-photorealistic rendering

In this paper we present a brief overview of the processing in the primary visual cortex, the multi-scale line/edge and keypoint representations, and a model of brightness perception. This model, which is being extended from 1D to 2D, is based on a symbolic line and edge interpretation: lines are represented by scaled Gaussians and edges by scaled, Gaussian-windowed error functions. We show that this model, in combination with standard techniques from graphics, provides a very fertile basis for non-photorealistic image rendering.

Artistic rendering of the visual cortex

In this paper we explain the processing in the first layers of the visual cortex by simple, complex and endstopped cells, plus grouping cells for line, edge, keypoint and saliency detection. Three visualisations are presented: (a) an integrated scheme that shows activities of simple, complex and end-stopped cells, (b) artistic combinations of selected activity maps that give an impression of global image structure and/or local detail, and (c) NPR on the basis of a 2D brightness model. The cortical image representations offer many possibilities for non-photorealistic rendering.

Integrated multi-scale architecture of the cortex with application to computer vision

Tese de dout., Engenharia Electrónica e de Computadores, Faculdade de Ciência e Tecnologia, Universidade do Algarve, 2007

Correlates of facial expressions in the primary visual cortex

Face detection and recognition should be complemented by recognition of facial expression, for example for social robots which must react to human emotions. Our framework is based on two multi-scale representations in cortical area V1: keypoints at eyes, nose and mouth are grouped for face detection [1]; lines and edges provide information for face recognition [2].

Enhancing extractive summarization with automatic post-processing

Tese de doutoramento, Informática (Ciência da Computação), Universidade de Lisboa, Faculdade de Ciências, 2015

A case study in identifying acceptable bitrates for human face recognition tasks

Face recognition from images or video footage requires a certain level of recorded image quality. This paper derives acceptable bitrates (relating to levels of compression and consequently quality) of footage with human faces, using an industry implementation of the standard H.264/MPEG-4 AVC and the Closed-Circuit Television (CCTV) recording systems on London buses. The London buses application is utilized as a case study for setting up a methodology and implementing suitable data analysis for face recognition from recorded footage, which has been degraded by compression. The majority of CCTV recorders on buses use a proprietary format based on the H.264/MPEG-4 AVC video coding standard, exploiting both spatial and temporal redundancy. Low bitrates are favored in the CCTV industry for saving storage and transmission bandwidth, but they compromise the image usefulness of the recorded imagery. In this context, usefulness is determined by the presence of enough facial information remaining in the compressed image to allow a specialist to recognize a person. The investigation includes four steps: (1) Development of a video dataset representative of typical CCTV bus scenarios. (2) Selection and grouping of video scenes based on local (facial) and global (entire scene) content properties. (3) Psychophysical investigations to identify the key scenes, which are most affected by compression, using an industry implementation of H.264/MPEG-4 AVC. (4) Testing of CCTV recording systems on buses with the key scenes and further psychophysical investigations. The results showed a dependency upon scene content properties. Very dark scenes and scenes with high levels of spatial–temporal busyness were the most challenging to compress, requiring higher bitrates to maintain useful information.

Are Deep Learning Approaches Suitable for Natural Language Processing?

In recent years, Deep Learning (DL) techniques have gained much at-tention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human design or engineering interventions. In addition, DL approaches have achieved some remarkable results. In this paper, we have surveyed major recent contributions that use DL techniques for NLP tasks. All these reviewed topics have been limited to show contributions to text understand-ing, such as sentence modelling, sentiment classification, semantic role labelling, question answering, etc. We provide an overview of deep learning architectures based on Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recursive Neural Networks (RNNs).

Wavelet analysis of human DNA

This paper studies the human DNA in the perspective of signal processing. Six wavelets are tested for analyzing the information content of the human DNA. By adopting real Shannon wavelet several fundamental properties of the code are revealed. A quantitative comparison of the chromosomes and visualization through multidimensional and dendograms is developed.

A case study on the improvement of eco-efficiency ratios: application to a composite processing industry

The World Business Council for Sustainable Development (WBCSD) defines Eco-Efficiency as follows: ‘Eco- Efficiency is achieved by the delivery of competitively priced-goods and services that satisfy human needs and bring quality of life, while progressively reducing ecological impacts and resource intensity throughout the life-cycle to a level at least in line with the earth’s estimated carrying capacity’. Eco-Efficiency is under this point of view a key concept for sustainable development, bringing together economic and ecological progress. Measuring the Eco-Efficiency of a company, factory or business, is a complex process that involves the measurement and control of several and relevant parameters or indicators, globally applied to all companies in general, or specific according to the nature and specificities of the business itself. In this study, an attempt was made in order to measure and evaluate the eco-efficiency of a pultruded composite processing company. For this purpose the recommendations of WBCSD [1] and the directives of ISO 14301 standard [2] were followed and applied. The analysis was restricted to the main business branch of the company: the production and sale of standard GFRP pultrusion profiles. The main general indicators of eco-efficiency, as well as the specific indicators, were defined and determined according to ISO 14031 recommendations. With basis on indicators’ figures, the value profile, the environmental profile, and the pertinent eco-efficiency’s ratios were established and analyzed. In order to evaluate potential improvements on company eco-performance, new indicators values and ecoefficiency ratios were estimated taking into account the implementation of new proceedings and procedures, both in upstream and downstream of the production process, namely: a) Adoption of new heating system for pultrusion die in the manufacturing process, more effective and with minor heat losses; b) Implementation of new software for stock management (raw materials and final products) that minimize production failures and delivery delays to final consumer; c) Recycling approach, with partial waste reuse of scrap material derived from manufacturing, cutting and assembly processes of GFRP profiles. In particular, the last approach seems to significantly improve the eco-efficient performance of the company. Currently, by-products and wastes generated in the manufacturing process of GFRP profiles are landfilled, with supplementary added costs to this company traduced by transport of scrap, landfill taxes and required test analysis to waste materials. However, mechanical recycling of GFRP waste materials, with reduction to powdered and fibrous particulates, constitutes a recycling process that can be easily attained on heavy-duty cutting mills. The posterior reuse of obtained recyclates, either into a close-looping process, as filler replacement of resin matrix of GFRP profiles, or as reinforcement of other composite materials produced by the company, will drive to both costs reduction in raw materials and landfill process, and minimization of waste landfill. These features lead to significant improvements on the sequent assessed eco-efficiency ratios of the present case study, yielding to a more sustainable product and manufacturing process of pultruded GFRP profiles.

Autonomous Surface Vehicle Docking Manoeuvre with Visual Information

This work presents a hybrid coordinated manoeuvre for docking an autonomous surface vehicle with an autonomous underwater vehicle. The control manoeuvre uses visual information to estimate the AUV relative position and attitude in relation to the ASV and steers the ASV in order to dock with the AUV. The AUV is assumed to be at surface with only a small fraction of its volume visible. The system implemented in the autonomous surface vehicle ROAZ, developed by LSA-ISEP to perform missions in river environment, test autonomous AUV docking capabilities and multiple AUV/ASV coordinated missions is presented. Information from a low cost embedded robotics vision system (LSAVision), along with inertial navigation sensors is fused in an extended Kalman filter and used to determine AUV relative position and orientation to the surface vehicle The real time vision processing system is described and results are presented in operational scenario.

Vision-Based Assisted Teleoperation for Inspection Tasks with a Small ROV

It is well-known that ROVs require human intervention to guarantee the success of their assignment, as well as the equipment safety. However, as its teleoperation is quite complex to perform, there is a need for assisted teleoperation. This study aims to take on this challenge by developing vision-based assisted teleoperation maneuvers, since a standard camera is present in any ROV. The proposed approach is a visual servoing solution, that allows the user to select between several standard image processing methods and is applied to a 3-DOF ROV. The most interesting characteristic of the presented system is the exclusive use of the camera data to improve the teleoperation of an underactuated ROV. It is demonstrated through the comparison and evaluation of standard implementations of different vision methods and the execution of simple maneuvers to acquire experimental results, that the teleoperation of a small ROV can be drastically improved without the need to install additional sensors.

Application of Visual-Inertial SLAM for 3D Mapping of Underground Environments

The underground scenarios are one of the most challenging environments for accurate and precise 3d mapping where hostile conditions like absence of Global Positioning Systems, extreme lighting variations and geometrically smooth surfaces may be expected. So far, the state-of-the-art methods in underground modelling remain restricted to environments in which pronounced geometric features are abundant. This limitation is a consequence of the scan matching algorithms used to solve the localization and registration problems. This paper contributes to the expansion of the modelling capabilities to structures characterized by uniform geometry and smooth surfaces, as is the case of road and train tunnels. To achieve that, we combine some state of the art techniques from mobile robotics, and propose a method for 6DOF platform positioning in such scenarios, that is latter used for the environment modelling. A visual monocular Simultaneous Localization and Mapping (MonoSLAM) approach based on the Extended Kalman Filter (EKF), complemented by the introduction of inertial measurements in the prediction step, allows our system to localize himself over long distances, using exclusively sensors carried on board a mobile platform. By feeding the Extended Kalman Filter with inertial data we were able to overcome the major problem related with MonoSLAM implementations, known as scale factor ambiguity. Despite extreme lighting variations, reliable visual features were extracted through the SIFT algorithm, and inserted directly in the EKF mechanism according to the Inverse Depth Parametrization. Through the 1-Point RANSAC (Random Sample Consensus) wrong frame-to-frame feature matches were rejected. The developed method was tested based on a dataset acquired inside a road tunnel and the navigation results compared with a ground truth obtained by post-processing a high grade Inertial Navigation System and L1/L2 RTK-GPS measurements acquired outside the tunnel. Results from the localization strategy are presented and analyzed.

Calibration Method for Underwater Visual Ground-Truth System

This work presents an automatic calibration method for a vision based external underwater ground-truth positioning system. These systems are a relevant tool in benchmarking and assessing the quality of research in underwater robotics applications. A stereo vision system can in suitable environments such as test tanks or in clear water conditions provide accurate position with low cost and flexible operation. In this work we present a two step extrinsic camera parameter calibration procedure in order to reduce the setup time and provide accurate results. The proposed method uses a planar homography decomposition in order to determine the relative camera poses and the determination of vanishing points of detected lines in the image to obtain the global pose of the stereo rig in the reference frame. This method was applied to our external vision based ground-truth at the INESC TEC/Robotics test tank. Results are presented in comparison with an precise calibration performed using points obtained from an accurate 3D LIDAR modelling of the environment.