Machine Learning Approaches to Sentiment Analytics

One key aspect of sentiment analytics is emotion classification. This research studies the use of machine learning approaches to classify human emotion. Two different machine learning approaches were compared in an experimental study. In one approach, emotions from both genders were used to train the machine. In another approach, genders were separated and two separate machines were used to learn the emotions of the two genders. We also manipulated the training sample sizes and study the effect of training sample sizes on the two machine learning approaches. Our preliminary results show that the approach where the genders were separated produces a higher accuracy in classifying emotions. We also observe that training sample sizes have different impact on the two approaches

The 3-D world modeling with updating capability based on combinatorial geometry

A 3-D world modeling technique using range data is discribed. Range data quantify the distances from the sensor focal plane to the object surface, i.e., the 3-D coordinates of discrete points on the object surface are known. The approach proposed herein for 3-D world modeling is based on the Combinatorial Geometry (CG) method which is widely used in Monte Carlo particle transport calculations. First, each measured point on the object surface is surrounded by a small sphere with a radius determined by the range to that point. Then, the 3-D shapes of the visible surfaces are obtained by taking the (Boolean) union of all the spheres. The result is an unambiguous representation of the object's boundary surfaces. The pre-learned partial knowledge of the environment can be also represented using the CG Method with a relatively small amount of data. Using the CG type of representation, distances in desired directions to boundary surfaces of various objects are efficiently calculated. This feature is particularly useful for continuously verifying the world model against the data provided by a range finder, and for integrating range data from successive locations of the robot during motion. The efficiency of the proposed approach is illustrated by simulations of a spherical robot in a 3-D room in the presence of moving obstacles and inadequate prelearned partial knowledge of the environment

Structure from Motion (SFM) – Uma Breve Revisão Histórica, Aplicações nas Geociências e Perspectivas Futuras

O presente artigo de revisão narra a trajetória de um dos algoritmos mais utilizados na fotogrametria com Veículo Aéreo não Tripulado (VANT), que a partir da visão computacional, tem se destacado como solução de baixo custo para obtenção de informações da superfície terrestre. Apesar de sua concepção ter sido formulada em meados da década de 1950 e com propósitos distantes das geociências, foi a partir dos avanços da indústria da computação e robótica, no início da década de 1980, que o Structure from Motion (SfM) absorveu significativas melhorias para consagrá-lo como um importante recurso de modelagem tridimensional. No entanto, somente na última década (2010), observou-se um exponencial crescimento nas aplicações e análises do SfM nas geociências, principalmente a partir da popularização dos VANTs. Com isso, vieram à tona suas principais aplicações e limitações – neste estudo também serão abordadas suas características, principalmente as que diferem de técnicas já consagradas como o LiDAR, e perspectivas futuras dessa tecnologia

Computer Vision Based Object Tracking as a Teaching Aid for High School Physics Experiments

Experiments play a vital role in science education. In high school physics, especially in mechanics, many experiments are conducted where tracking a single or multiple objects are required. In most situations students visually observe the motion of objects and take the measurements. This manual method is time consuming, generates higher error and incapable of producing multiple readings rapidly. The research described in this work introduces a simple mechanism to integrate computer vision based tracking to enhance the quality of measurements and to new ways of looking at experiments. The case study consists of three standard experiments. In the first experiment a motion of the simple pendulum was tracked. Using computer vision students were able to obtain a correlation of 0.99 between the calculated period and the theoretical period. In addition, it was possible to calculate the position and the velocity of the bob more than 30 times during a single oscillation. Students were able to plot the extra data points for a better understanding of the simple harmonic motion, which was not possible in the manual method. Second experiment was focused on measuring the terminal velocity of a ball moving through a viscous medium. Final case study was on tracking multiple particles in a moving fluid. In all three experiments computer vision based system provided more accurate and higher number of data points than the manual method. This helps students to understanding the underline theory better. The tracking system was consisted of a digital camera, image preprocessing sub system, feature extraction subsystem, object identification subsystem and data export subsystem. The system was successfully tested on a normal PC which is cost effective to be used in high schools. Based on the case studies it was concluded that such systems can be used in high schools to improve the quality of experiments conducted.

Motion Safety with People: an Open Problem

International audienceIn this presentation, we explore and question the concept of motion safety, i.e. the ability to avoid collision, for robots sharing their workspace with people. We establish that absolute motion safety, in the sense that no collision between the robot and the people will ever take place, is impossible to guarantee (hence the open nature of the motion safety problem). We then discuss the choices that are available: mere risk minimization or what we call weaker motion safety, i.e. types of motion safety that are weaker than absolute motion safety but that can actually be guaranteed. In all cases, we argue that if robots are ever to be deployed among people, it is important to characterize the level of motion safety that can be achieved and to specify the conditions under which it can be guaranteed

Will the Driver Seat Ever Be Empty?

Self-driving technologies have matured and improved to the point that, in the past few years, self-driving cars have been able to safely drive an impressive number of kilometers. It should be noted though that, in all cases, the driver seat was never empty: a human driver was behind the wheel, ready to take over whenever the situation dictated it. This is an interesting paradox since the point of a self-driving car is to remove the most unreliable part of the car, namely the human driver. So, the question naturally arises: will the driver seat ever be empty? Besides legal liability issues, the answer to that question may lie in our ability to improve the self-driving technologies to the point that the human driver can safely be removed from the driving loop altogether. However, things are not that simple. Motion safety, i.e. the ability to avoid collisions, is the critical aspect concerning self-driving cars and autonomous vehicles in general. Before letting self-driving cars transport people around (and move among them) in a truly autonomous way, it is crucial to assess their ability to avoid collision, and to seek to characterize the levels of motion safety that can be achieved and the conditions under which they can be guaranteed. All these issues are explored in this article.Les technologies de conduite automatique ont mûries et se sont améliorées au point que, au cours des dernières années, les voitures automatiques ont été en mesure de conduire en toute sécurité un nombre impressionnant de kilomètres. Il convient cependant de noter que, dans tous les cas, le siège du conducteur n'était jamais vide : un conducteur humain était au volant, prêt à prendre le relais dès que la situation dictée. C'est un paradoxe intéressant car le point d'une voiture automatique est d'enlever la partie la plus sensible de la voiture, à savoir le conducteur humain. Ainsi, la question se pose naturellement: le siège du conducteur sera t'il vide un jour? Outre les questions de responsabilité juridique, la réponse à cette question réside peut-être dans notre capacité à améliorer les technologies de la conduite automatique, au point que le pilote humain peut en toute sécurité être retiré de la boucle de conduite. Toutefois, les choses ne sont pas aussi simple que cela. La sécurité de mouvement, i.e. la capacité à éviter les collisions, est l'aspect critique à l'égard de voitures automatiques et les véhicules autonomes en général. Avant de laisser les voitures automatiques transporter des personnes (et se déplacer parmi eux) d'une manière réellement autonome, il est crucial d'évaluer leur capacité à éviter la collision, et de chercher à caractériser les niveaux de sécurité de mouvement qui peuvent être atteints et les conditions dans lesquelles elles peuvent être garanties. Toutes ces questions sont examinées dans cet article

Regional Image Perturbation Reduces LpL_p Norms of Adversarial Examples While Maintaining Model-to-model Transferability

Regional adversarial attacks often rely on complicated methods for generating adversarial perturbations, making it hard to compare their efficacy against well-known attacks. In this study, we show that effective regional perturbations can be generated without resorting to complex methods. We develop a very simple regional adversarial perturbation attack method using cross-entropy sign, one of the most commonly used losses in adversarial machine learning. Our experiments on ImageNet with multiple models reveal that, on average, $76\%$ of the generated adversarial examples maintain model-to-model transferability when the perturbation is applied to local image regions. Depending on the selected region, these localized adversarial examples require significantly less $L_p$ norm distortion (for $p \in \{0, 2, \infty\}$) compared to their non-local counterparts. These localized attacks therefore have the potential to undermine defenses that claim robustness under the aforementioned norms.Comment: Accepted for the ICML 2020, Workshop on Uncertainty and Robustness in Deep Learning (UDL

FEATURE MATCHING ENHANCEMENT OF UAV IMAGES USING GEOMETRIC CONSTRAINTS

Preliminary matching of image features is based on the distance between their descriptors. Matches are further filtered using RANSAC, or a similar method that fits the matches to a model; usually the fundamental matrix and rejects matches not belonging to that model. There are a few issues with this scheme. First, mismatches are no longer considered after RANSAC rejection. Second, RANSAC might fail to detect an accurate model if the number of outliers is significant. Third, a fundamental matrix model could be degenerate even if the matches are all inliers. To address these issues, a new method is proposed that relies on the prior knowledge of the images’ geometry, which can be obtained from the orientation sensors or a set of initial matches. Using a set of initial matches, a fundamental matrix and a global homography can be estimated. These two entities are then used with a detect-and-match strategy to gain more accurate matches. Features are detected in one image, then the locations of their correspondences in the other image are predicted using the epipolar constraints and the global homography. The feature correspondences are then corrected with template matching. Since global homography is only valid with a plane-to-plane mapping, discrepancy vectors are introduced to represent an alternative to local homographies. The method was tested on Unmanned Aerial Vehicle (UAV) images, where the images are usually taken successively, and differences in scale and orientation are not an issue. The method promises to find a well-distributed set of matches over the scene structure, especially with scenes of multiple depths. Furthermore; the number of outliers is reduced, encouraging to use a least square adjustment instead of RANSAC, to fit a non-degenerate model