Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Visualization and Set-theoretic Difference Operations for Accurate Geometric Modeling in Real-time Simulations

: We present a novel approach, which supports efﬁcient visualization and set-theoretic difference operations for accurate geometric modelling in real-time simulations. The geometric operands can be in any representation as long as they are watertight and are convertible to an oriented point cloud. A novel region growing based ﬁtting routine converts the oriented point clouds into a watertight piecewise quadratic implicit representation. During set-theoretic modeling these implicit representations are mapped to the ﬁxed depth hierarchical grid of the resulting geometric model. Thereby a surface elimination algorithm removes parts not contributing to the ﬁnal surface. This guarantees that the number of already performed modeling operations have only a minor performance impact on the algorithms processing the model. For visualization a novel ray casting based approach was developed, enabling interactive frame rates at Full-HD screen resolutions. The evaluation of the developed method proves its modeling performance and high geometric accuracy by means of the simulation of subtractive manufacturing examples.
An Overlook On Diffusion Mr Of Stroke Patients

Cerebrovascular event is defined as neurological deficit because of ischemic or haemorrhagic neurological situation. Treatment methods differ depending on the causes. Approximately 87% of CVE (cerebrovascular event) are ischemic (1). A study based on the data of social security in Turkey between 2008 and 2013 showed that 400.000 people died and 1.5 million people experienced the reults of SVE. Prevalance is 2.2% (2).
Metrics Performance Analysis of Optical Flow

Significant amount of research has been conducted on optical flow estimation in previous decades. However, only limited number of research has been conducted on performance analysis of optical flow. These evaluations have shortcomings and a theoretical justification of using one approach and why is needed. In practice, design choices are often made based on qualitative unmotivated criteria or by trial and error. In this paper, novel optical flow performance metrics are proposed and evaluated alongside with current metrics. Our empirical findings suggest using two new optical flow performance metrics namely: Normalized Euclidean Error (NEE) and Enhanced Normalized Euclidean Error version one (ENEE1) for optical flow performance evaluation with ground truth.
Curtain Graphs: Using a Floating Baseline for Comparison in a Two-dimensional Graphical Space

: We present a novel visualization tool designed to provide support for the analysis of data sets focused around deviation from a baseline and including data from multiple series. The incorporation of a ﬂoating baseline makes the curtain graph distinct from waterfall plots and bar charts. Each data series therefore has a visual anchor that assists in interpretability, gives focus, and provides a means for easily broadening analysis across all presented series. The use of this tool in real-world examples based on relative and absolute comparisons is discussed.
Human Climbing and Bouldering Motion Analysis: A Survey on Sensors, Motion Capture, Analysis Algorithms, Recent Advances and Applications

Bouldering and climbing motion analysis are increasingly attracting interest in scientific research. Although there is a number of studies dealing with climbing motion analysis, there is no comprehensive survey that exhaustively contemplates sensor technologies, approaches for motion capture and algorithms for the analysis of climbing motions. To promote further advances in this field of research, there is an urgent need to unite available information from different perspectives, such as from a sensory, analytical and application-specific point of view. Therefore, this survey conveys a general understanding of available technologies, algorithms and open questions in the field of climbing motion analysis. The survey is not only aimed at researchers with technical background, but also addresses sports scientists and emphasises the use and advantages of visionbased approaches for climbing motion analysis.
Towards Visual Loop Detection in Underwater Robotics using a Deep Neural Network

This paper constitutes a first step towards the use of Deep Neural Networks to fast and robustly detect underwater visual loops. The proposed architecture is based on an autoencoder, replacing the decoder part by a set of fully connected layers. Thanks to that it is possible to guide the training process by means of a global image descriptor built upon clusters of local SIFT features. After training, the NN builds two different descriptors of the input image. Both descriptors can be compared among different images to decide if they are likely to close a loop. The experiments, performed in coastal areas of Mallorca (Spain), evaluate both descriptors, show the ability of the presented approach to detect loop candidates and favourably compare our proposal to a previously existing method.
Real-time Surveillance based Crime Detection for Edge Devices

There is a growing use of surveillance cameras to maintain a log of events that would help in the identification of criminal activities. However, it is necessary to continuously monitor the acquired footage which contributes to increased labor costs but more importantly, violation of privacy. Therefore, we need decentralized surveillance systems that function autonomously in real-time to reduce crime rates even further. In our work, we discuss an efficient method of crime detection using Deep Learning, that can be used for ondevice crime monitoring. By making the inferences on-device, we can reduce the latency, the cost overhead for the collection of data into a centralized unit and counteract the lack of privacy. Using the concept of EarlyStopping–Multiple Instance Learning to provide low inference time, we build specialized models for crime detection using two real-world datasets in the domain. We implement the concept of Sub-Nyquist sampling on a video and introduce a metric ηcomp for evaluating the reduction of computation due to undersampling. On average, our models tested on Raspberry Pi 3 Model B provide a 30% increase of accuracy over benchmarks, computational savings as 80.23% and around 13 times lesser inference times. This allows for the development of efficient and accurate real-time implementation on edge devices for on-device crime detection.
RustBelt: securing the foundations of the rust programming language

Rust is a new systems programming language that promises to overcome the seemingly fundamental tradeoff between high-level safety guarantees and low-level control over resource management. Unfortunately, none of Rust's safety claims have been formally proven, and there is good reason to question whether they actually hold. Specifically, Rust employs a strong, ownership-based type system, but then extends the expressive power of this core type system through libraries that internally use unsafe features. In this paper, we give the first formal (and machine-checked) safety proof for a language representing a realistic subset of Rust. Our proof is extensible in the sense that, for each new Rust library that uses unsafe features, we can say what verification condition it must satisfy in order for it to be deemed a safe extension to the language. We have carried out this verification for some of the most important libraries that are used throughout the Rust ecosystem.
Manifold Learning-based Clustering Approach Applied to Anomaly Detection in Surveillance Videos

: The huge increase in the amount of multimedia data available and the pressing need for organizing them in different categories, especially in scenarios where there are no labels available, makes data clustering an essential task in different scenarios. In this work, we present a novel clustering method based on an unsupervised manifold learning algorithm, in which a more effective similarity measure is computed by the manifold learning and used for clustering purposes. The proposed approach is applied to anomaly detection in videos and used in combination with different background segmentation methods to improve their effectiveness. An experimental evaluation is conducted on three different image datasets and one video dataset. The obtained results indicate superior accuracy in most clustering tasks when compared to the baselines. Results also demonstrate that the clustering step can improve the results of background subtraction approaches in the majority of cases.
Raindrop Removal in a Vehicle Camera Video Considering the Temporal Consistency for Driving Support

: This paper proposes a recursive framework for raindrop removal in a vehicle camera video considering the temporal consistency. Raindrops attached to a vehicle camera lens may prevent a driver or a camera-based system from recognizing the trafﬁc environment. This research aims to develop a framework for raindrop detection and removal in order to deal with such a situation. The proposed method sequentially and recursively restores a video containing no raindrops from original one that may contain raindrops. The proposed method uses an output (restored) image as one of the input frames for the next image restoration process in order to improve the restoration quality, which is the key concept of the proposed framework. In each restoration process, the proposed method ﬁrst detects raindrops in each input video frame, and then restores the raindrop regions based on the optical ﬂow. The optical ﬂow can be calculated in the outer part of the raindrop region more accurately than the inner part due to the difﬁculty of ﬁnding a corresponding pixel, which is the assumption for designing the proposed method. We conﬁrmed that the proposed framework has the potential for improving the restoration accuracy through several preliminary experiments and evaluation experiments.
Comparative Study of a Commercial Tracking Camera and ORB-SLAM2 for Person Localization

: Aiming at localizing persons in industrial sites is a major concern towards the development of the factory of the future. During the last years, developments have been made in several active research domains targeting the localization problem, among which the vision-based Simultaneous Localization and Mapping paradigm. This has led to the development of multiple algorithms in this ﬁeld such as ORB-SLAM2 known to be the most complete method as it incorporates the majority of the state-of-the-art techniques. Recently, new commercial and low-cost systems have also emerged in the market that can estimate the 6-DOF motion. In particular, we refer here to the Intel Realsense T265, a standalone 6-DOF tracking sensor that runs a visual-inertial SLAM algorithm and that accurately estimates the 6-DOF motion as claimed by the Intel company. In this paper, we present an evaluation of the Intel T265 tracking camera by comparing its localization performances to the ORB-SLAM2 algorithm. This benchmarking ﬁts within a speciﬁc use-case: the person localization in an industrial site. The experiments have been conducted in a platform equipped with a VICON motion capture system, which physical structure is similar to a one that we could ﬁnd in an industrial site. The Vicon system is made of ﬁfteen high-speedtracking cameras (100 Hz) which provides highly accurate poses that were used as ground truth reference. The sequences have been recorded using both an Intel RealSense D435 camera to use its stereo images with ORB-SLAM2 and the Intel RealSense T265. The two sets of timestamped poses (VICON and the ones provided by the cameras) were aligned then calibrated using the point set registration method. The Absolute Trajectory Error, the Relative Trajectory Error and the Euclidian Distance Error metrics were employed to benchmark the localization accuracy from ORB-SLAM2 and T265. The results show a competitive accuracy of both systems for a handheld camera in an indoor industrial environment with a better reliability with the T265 Tracking system.
Plant Species Identification using Discriminant Bag of Words (DBoW)

Plant species identification is necessary for protecting biodiversity which is declining rapidly throughout the world. This research work focuses on plant species identification in simple and complex background using Computer Vision techniques. Intra-class variability and inter-class similarity are the key challenges in a large plant species dataset. In this paper, multiple organs of plants such as leaf, flower, stem, fruit, etc. are classified using hand-crafted features for identification of plant species. We propose a novel encoding scheme named as Discriminant Bag of Words (DBoW) to identify multiple organs of plants. The proposed DBoW extracts the class specific codewords, and assigns the weights to codewords in order to signify discriminant power of the codewords. We evaluated our proposed method on two publicly available datasets: Flavia and ImageClef. The experimental results achieved classification accuracy rates of 98% and 94% on FLAVIA and ImageClef datasets, respectively.
Corner Detection in Manifold-valued Images and in Vector Fields

: This paper is devoted to the problem of corner detection in manifold-valued images and in vector ﬁelds on manifolds. Our solution is a generalization of the Harris corner detector (C. Harris, 1988). As in the grayscale case, our algorithm is based on an estimation of a self-similarity of a point neighborhood. We deﬁne the self-similarity for the general cases and obtain approximations of it by an action of a bilinear form. This form can be viewed as a generalization of the structure tensor (M. Kass, 1987). The generalized structure tensor is then used as usual in the corner detection procedure. Finally, we describe future experiments: the algorithm will be tested on a task of chemical compounds classiﬁcation.
Deep Body-pose Estimation via Synthetic Depth Data: A Case Study

: Computer Vision research is nowadays largely data-driven due to the prevalence of deep learning. This is one reason why depth data have become less popular, as no datasets exist that are comparable to common color datasets in terms of size and quality. However, depth data have advantages in practical applications that involve people, in which case utilizing cameras raises privacy concerns. We consider one such application, namely 3D human pose estimation for a health care application, to study whether the lack of large depth datasets that represent this problem can be overcome via synthetic data, which aspects must be considered to ensure generalization, and how this compares to alternative approaches for obtaining training data. Furthermore, we compare the pose estimation performance of our method on depth data to that of state-of-the-art methods for color images and show that depth data is a suitable alternative to color images in this regard.
Combining Image and Caption Analysis for Classifying Charts in Biodiversity Texts

: Chart type classiﬁcation through caption analysis is a new area of study. Distinct keywords in the captions that relate to the visualization vocabulary (e.g., for scatterplot: dot, y-axis, x-axis, bubble) and keywords from the speciﬁc domain (e.g., species richness, species abundance, phylogenetic associations in the case of biodiversity research), serve as parameters to train a text classiﬁer. For better chart comprehensibility, along with the visual characteristics of the chart, a classiﬁer should also understand these parameters well. Such conceptual/semantic chart classiﬁers then will not only be useful for chart classiﬁcation purposes but also for other visualization studies. One of the applications of such a classiﬁer is in the creation of the domain knowledge-assisted visualization recommendation system, where these text classiﬁers can provide the recommendation of visualization types based on the classiﬁcation of the text provided along with the dataset. Motivated by this use case, in this paper, we have explored our idea of semantic chart classiﬁers. We have taken the assistance of state-of-the-art natural language processing (NLP) and computer vision algorithms to create a biodiversity domain-based visualization classiﬁer. With an average test accuracy (F1-score) of 92.2% over all 15 classes, we can prove that our classiﬁers can differentiate between different chart types conceptually and visually.
Intention Indication for Human Aware Robot Navigation

: Robots are gradually making their ways from factory ﬂoors to our everyday living environments. Mobile robots are becoming more ubiquitous in many domains: logistics, entertainment, security, healthcare, etc. For robots to enter the everyday human environment they need to understand us and make themselves understood. In other words, they need to make their intentions clear to people. This is especially important regarding intentions of movement: when robots are starting, stopping, turning left, right, etc. In this study we explore three different ways for a wheeled mobile robot to communicate its intentions on which way it will go at a hallway intersection: one analogous to automotive signaling, another based on movement gesture and as a third option a novel light signal. We recorded videos of the robot approaching an intersection with the given methods and asked subjects via a survey to predict the robot’s actions. The car analogy and turn gesture performed adequately, while the novel light signal less so. In the following we describe the setup and outcomes of this study, as well as give suggestions on how mobile robots should signal in indoor spaces based on our ﬁndings.
Defining Controlled Experiments Inside the Access Control Environment

: In ICT systems and modern applications access control systems are important mechanisms for managing resources and data access. Their criticality requires high security levels and consequently, the application of effective and efﬁcient testing approaches. In this paper we propose standardized guidelines for correctly and systematically performing the testing process in order to avoid errors and improve the effectiveness of the validation. We focus in particular on Controlled Experiments, and we provide here a characterization of the ﬁrst three steps of the experiment process (i.e., Scoping, Planning and Operation) by the adoption of the Goal-Question-Metric template. The specialization of the three phases is provided through a concrete example.
Vague Visualizations to Reduce Quantification Bias in Shared Medical Decision Making

: This paper aims to contribute to the research focusing on how to render properly uncertainty in decision making, especially in regard to classiﬁcation (like in medical diagnosis) or risk prediction (like in medical prognosis). Information visualizations leverage perception to convey information on data in ways that make their interpretation easier. Unfortunately, many visualizations omit uncertainty or communicate it less than effectively. We devised a novel way, which we call vague visualization , to render uncertainty without convert-ing it in any numerical or symbolic form, and tested the usability and task ﬁtness of these alternative solutions in a user study that involved a panel of lay people (as proxies of potential patients). In so doing, we aimed to understand whether our solutions facilitate (or at least do not hinder) communication and understanding of probabilistic estimates in a medical context, and if one solution is more effective than the others. We observed that three different vague visualizations convey the right sense of risk with respect to chance (50%) of percentage shown, and inspire an interpretation of the magnitude of the percentages that replicates the typi-cal response of decision making under uncertainty condition. We then claim that these methods are effective because they allow for data interpretations that are uncertain (vague), and yet correct and compatible with appropriate decisions and actions.
Eye Gaze Tracking for Detecting Non-verbal Communication in Meeting Environments

: Non-verbal communication in a team meeting is important to understand the essence of the conversation. Among other gestures, eye gaze shows the focus of interest on a common workspace and can also be used for an interpersonal synchronisation. If this non-verbal information is missing and or cannot be perceived by blind and visually impaired people (BVIP), they would lack important information to get fully immersed in the meeting and may feel alienated in the course of the discussion. Thus, this paper proposes an automatic system to track where a sighted person is gazing at. We use the open source software ’OpenFace’ and develop it as an eye tracker by using a support vector regressor to make it work similarly to commercially available expensive eye trackers. We calibrate OpenFace using a desktop screen with a 2 × 3 box matrix and conduct a user study with 28 users on a big screen (161.7 cm x 99.8 cm x 11.5 cm) with a 1 × 5 box matrix. In this user study, we compare the results of our developed algorithm for OpenFace to an SMI RED 250 eye tracker. The results showed that our work achieved an overall relative accuracy of 58.54%.
A Comparison of Visual Navigation Approaches based on Localization and Reinforcement Learning in Virtual and Real Environments

: Visual navigation algorithms allow a mobile agent to sense the environment and autonomously ﬁnd its way to reach a target (e.g. an object in the environment). While many recent approaches tackled this task using reinforcement learning, which neglects any prior knowledge about the environments, more classic approaches strongly rely on self-localization and path planning. In this study, we compare the performance of single-target and multi-target visual navigation approaches based on the reinforcement learning paradigm, and simple baselines which rely on image-based localization. Experiments performed on discrete-state environments of different sizes, comprised of both real and virtual images, show that the two paradigms tend to achieve complementary results, hence suggesting that a combination of the two approaches to visual navigation may be beneﬁcial.