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In this paper we present a novel technique for optical 3d surface reconstruction using line scan cameras in combination with structured light. The approach generating structured light is customized to the special demands of line-scan image capturing. Due to the distributed generation of light and the absence of shading elements we obtain a light intensity that is comparable with conventional line...
This paper explores a relatively new methodological approach for the field integrating learning and education, with other research areas, such as neurobiological, cognitive, and computational sciences. Specifically, presented work is an interdisciplinary piece of research aiming to simulate appropriately a challenging and critical issue concerned with academic performance in e-learning systems. Namely,...
The following paper introduces a slightly enhanced version of the Selective approach for tracking individuals in video sequences. In this version, tracking is achieved by evaluating candidate regions located in the eight adjacent neighbors in addition to the current position. Evaluation is handled using the Bhattacharyya distance. A process of motion estimation is incorporated to allow readjustment...
This paper provides interesting findings for modeling of a challenging and critical pedagogical issue namely online learning assessment of Multiple Choice Questions (MCQs) analysis and evaluation. More precisely, in fulfillment of that issue's objective, this work suggests using a realistic Artificial Neural Network (ANN) model. That, explicitly, characterized by two learning paradigms: supervised...
This work introduces analysis and evaluation of an interesting, challenging, and interdisciplinary, pedagogical issue. That's originated from categorization of the achievement diversity of students' (individual differences), equivalently students' Structure of the Observed Learning Outcome (SOLO). This students' academic diversity affected in classrooms by three interactive learning/teaching approaches...
In this work, we propose a real-time capable and robust method for human pose estimation based on geodesic distance features from depth images. Although a lot of work has been done in the field of the human pose estimation, it remains a challenging task - especially because of the high variability of human poses and self occlusions. The pose estimation focuses on the upper body, as it is the relevant...
A multi-agent robot system can speed up exploration or search and rescue operations in dangerous environments by working as a distributed sensor network. Each robot (e.g. Eddi Robot) equipped with a combined 2D/3D sensor (MS Kinect) and additional sensors needs to efficiently exchange its collected data with the other group members for task planning. For environment perception a 2D/3D panorama is...
Mobile robots can assist humans in disaster management or environmental perception by building a multi robot team, working as a distributed sensor actor system. In order to coordinate the operations of a multi robot team the human machine interface is required to decode orders which will potentially be performed by a different robot, e.g. pointing to an area to be scanned. The human operator sets...
Since its release in late 2010 the Microsoft Kinect depth sensor has boosted real time gesture recognition and new man-machine interaction endeavors in the computer vision community. Based on depth image data, in this paper we propose an accurate, fast and robust face pose estimation approach, which for example can be of interest for user behavior analysis, or be of use as a means of man machine interaction...
In this paper an approach for automatic user-specific 3D model generation and expression classification is proposed. User performance-driven avatar animation is recently in the focus of research due to the increasing amount of low-cost acquisition devices with integrated depth map computation. Thereby challenging is the user-specific emotion classification without a complex manual initialisation....
In this work, we propose a real-time capable and robust method for human pose estimation based on geodesic distance features from depth images. Although a lot of work has been done in the field of the human pose estimation, it remains a challenging task - especially because of the high variability of human poses and self occlusions. The pose estimation focuses on the upper body, as it is the relevant...
Since its release in late 2010 the Microsoft Kinect depth sensor has boosted real time gesture recognition and new man-machine interaction endeavors in the computer vision community. Based on depth image data, in this paper we propose an accurate, fast and robust face pose estimation approach, which for example can be of interest for user behavior analysis, or be of use as a means of man machine interaction...
In contrast to the mainstream HMM-based approaches dedicated for the recognition of offline handwritten Arabic, this paper proposes an HMM-based approach that built upon an explicit segmentation module. And shape representative based rather than sliding window based features, are extracted and used to build a reference as well as a confirmation model for each letter in each handwritten form. Additionally,...
In this paper we present a novel local group based method for extracting skewed and curved handwritten text lines in Arabic document images. We first detect all connected components and use a Support Vector Machine (SVM) to classify them either as Piece of Arabic Word (PAW) or diacritic. We then, use novel distance measures like sigmoid function based shapes to calculate the nearest neighbors for...
Comprehensive handwriting databases are crucial to train and test script recognition systems. However their generation is expensive in sense of manpower and time. As a result there is a lack of such databases which impedes research and development. This is especially true in case of holistic word recognition, since various samples must be available for each entry of the underlying vocabulary. To bypass...
Facial expressions play an important role in diverse disciplines ranging from entertainment (video games) to medical applications and affective computing. For tackling the problem of expression recognition, various approaches were proposed over the last two decades. These approaches are primarily divided into two types: geometry and appearance based. In this paper, we address the geometry based approaches...
The objective measurement of subjective, multi-dimensionally experienced pain is still a problem that has yet to be adequately solved. Though verbal methods (i.e., pain scales, questionnaires) and visual analogue scales are commonly used for measuring clinical pain, they tend to lack in reliability or validity when applied to mentally impaired individuals. Expression of pain and/or its biopotential...
Current speech-controlled human computer interaction is purely based on spoken information. For a successful interaction, additional information such as the individual skills, preferences and actual affective state of the user are often mandatory. The most challenging of these additional inputs is the affective state, since affective cues are in general expressed very sparsely. The problem can be...
This paper addresses an interdisciplinary approach integrating evaluation of an educational issue with Artificial Neural Network (ANN) modeling. Specifically, it is concerned with ANN modeling of two Computer Assisted Learning (CAL) packages/modules using various learning rate values. Both packages are considered for teaching a mathematical topic: “How to solve long division problem?”. They have been...
Within the EU-Project ARTRAC a radar sensor is developed that offers new capabilities for enhanced pedestrian protection. In this paper we describe a sensor model for high resolution measurements as a first step to help improving the object detection and tracking. For the tracking, with the commonly used Kalman filter and its derivations, one makes several assumptions which do not necessarily hold...
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