792 research outputs found

    A Quantitative Comparison of Calibration Methods for RGB-D Sensors Using Different Technologies

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    RGB-D (Red Green Blue and Depth) sensors are devices that can provide color and depth information from a scene at the same time. Recently, they have been widely used in many solutions due to their commercial growth from the entertainment market to many diverse areas (e.g., robotics, CAD, etc.). In the research community, these devices have had good uptake due to their acceptable level of accuracy for many applications and their low cost, but in some cases, they work at the limit of their sensitivity, near to the minimum feature size that can be perceived. For this reason, calibration processes are critical in order to increase their accuracy and enable them to meet the requirements of such kinds of applications. To the best of our knowledge, there is not a comparative study of calibration algorithms evaluating its results in multiple RGB-D sensors. Specifically, in this paper, a comparison of the three most used calibration methods have been applied to three different RGB-D sensors based on structured light and time-of-flight. The comparison of methods has been carried out by a set of experiments to evaluate the accuracy of depth measurements. Additionally, an object reconstruction application has been used as example of an application for which the sensor works at the limit of its sensitivity. The obtained results of reconstruction have been evaluated through visual inspection and quantitative measurements

    Nonrigid reconstruction of 3D breast surfaces with a low-cost RGBD camera for surgical planning and aesthetic evaluation

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    Accounting for 26% of all new cancer cases worldwide, breast cancer remains the most common form of cancer in women. Although early breast cancer has a favourable long-term prognosis, roughly a third of patients suffer from a suboptimal aesthetic outcome despite breast conserving cancer treatment. Clinical-quality 3D modelling of the breast surface therefore assumes an increasingly important role in advancing treatment planning, prediction and evaluation of breast cosmesis. Yet, existing 3D torso scanners are expensive and either infrastructure-heavy or subject to motion artefacts. In this paper we employ a single consumer-grade RGBD camera with an ICP-based registration approach to jointly align all points from a sequence of depth images non-rigidly. Subtle body deformation due to postural sway and respiration is successfully mitigated leading to a higher geometric accuracy through regularised locally affine transformations. We present results from 6 clinical cases where our method compares well with the gold standard and outperforms a previous approach. We show that our method produces better reconstructions qualitatively by visual assessment and quantitatively by consistently obtaining lower landmark error scores and yielding more accurate breast volume estimates

    Appearance-Based Gaze Estimation in the Wild

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    Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks that significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithms on three current datasets, including our own. This evaluation provides clear insights and allows us to identify key research challenges of gaze estimation in the wild

    Assessing the suitability of the Microsoft Kinect for calculating person specific body segment parameters

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    Many biomechanical and medical analyses rely on the availability of reliable body segment parameter estimates. Current techniques typically take many manual measurements of the human body, in conjunction with geometric models or regression equations. However, such techniques are often criticised. 3D scanning offers many advantages, but current systems are prohibitively complex and costly. The recent interest in natural user interaction (NUI) has led to the development of low cost (-ÂŁ200) sensors capable of 3D body scanning, however, there has been little consideration of their validity. A scanning system comprising four Microsoft Kinect sensors (a typical NUI sensor) was used to scan twelve living male participants three times. Volume estimates from the system were compared to those from a geometric modelling technique. Results demonstrated high reliability (ICC >0.7, TEM <1%) and presence of a systematic measurement offset (0.001m3) suggesting the system would be well received by healthcare and sports communities

    Context-aware gestural interaction in the smart environments of the ubiquitous computing era

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    A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyTechnology is becoming pervasive and the current interfaces are not adequate for the interaction with the smart environments of the ubiquitous computing era. Recently, researchers have started to address this issue introducing the concept of natural user interface, which is mainly based on gestural interactions. Many issues are still open in this emerging domain and, in particular, there is a lack of common guidelines for coherent implementation of gestural interfaces. This research investigates gestural interactions between humans and smart environments. It proposes a novel framework for the high-level organization of the context information. The framework is conceived to provide the support for a novel approach using functional gestures to reduce the gesture ambiguity and the number of gestures in taxonomies and improve the usability. In order to validate this framework, a proof-of-concept has been developed. A prototype has been developed by implementing a novel method for the view-invariant recognition of deictic and dynamic gestures. Tests have been conducted to assess the gesture recognition accuracy and the usability of the interfaces developed following the proposed framework. The results show that the method provides optimal gesture recognition from very different view-points whilst the usability tests have yielded high scores. Further investigation on the context information has been performed tackling the problem of user status. It is intended as human activity and a technique based on an innovative application of electromyography is proposed. The tests show that the proposed technique has achieved good activity recognition accuracy. The context is treated also as system status. In ubiquitous computing, the system can adopt different paradigms: wearable, environmental and pervasive. A novel paradigm, called synergistic paradigm, is presented combining the advantages of the wearable and environmental paradigms. Moreover, it augments the interaction possibilities of the user and ensures better gesture recognition accuracy than with the other paradigms

    Comparative analysis of Kinect-based and Oculus-based gaze region estimation methods in a driving simulator

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    Producción CientíficaDriver’s gaze information can be crucial in driving research because of its relation to driver attention. Particularly, the inclusion of gaze data in driving simulators broadens the scope of research studies as they can relate drivers’ gaze patterns to their features and performance. In this paper, we present two gaze region estimation modules integrated in a driving simulator. One uses the 3D Kinect device and another uses the virtual reality Oculus Rift device. The modules are able to detect the region, out of seven in which the driving scene was divided, where a driver is gazing at in every route processed frame. Four methods were implemented and compared for gaze estimation, which learn the relation between gaze displacement and head movement. Two are simpler and based on points that try to capture this relation and two are based on classifiers such as MLP and SVM. Experiments were carried out with 12 users that drove on the same scenario twice, each one with a different visualization display, first with a big screen and later with Oculus Rift. On the whole, Oculus Rift outperformed Kinect as the best hardware for gaze estimation. The Oculus-based gaze region estimation method with the highest performance achieved an accuracy of 97.94%. The information provided by the Oculus Rift module enriches the driving simulator data and makes it possible a multimodal driving performance analysis apart from the immersion and realism obtained with the virtual reality experience provided by Oculus.Dirección General de Tráfico y Ministerio del Interior - (Proyecto SPIP2015-01801

    Human activity recognition from object interaction in domestic scenarios

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    This work describes the recognition of human activity based on the interaction between people and objects in domestic settings, specifically in a kitchen. In order to achieve the aim of recognizing activity it is necessary to establish a procedure and essential equipment. Regarding the procedure, in a simplified manner, it is based on capturing local images where the activity takes place using a colour camera (RGB), and processing the above mentioned images to recognize the present objects and its location. The interaction with the objects is classified as five types of possible actions (unchanged, add, remove, move and Indeterminate), which are used to analyze the probability of the human activity that is being performed at the moment. As for the technological tools employed, the system works with Ubuntu as general Operating System, ROS (Robot Operating System) as framework, OpenCV (Open Source Computer Vision) for the vision algorithms used, and Python programming language. The development starts with the segmentation using the "difference image" method that obtains the area that the objects take up in the image the recognition of objects is carried out by distinguishing them according to its colour histogram. the positioning is obtained through its centroid, applying the corresponding homography to go from the coordinate system of the image to the coordinates of the real world using comparisons of the historical and the new information of the objects we determine the actions that have been fulfilled as final stage, we filter the relevant objects on the basis of the actions carried out and compare with the objects defined for the accomplishment of every activity the result is the probability of executing each activity

    Anatomical Mirroring: Real-time User-specific Anatomy in Motion Using a Commodity Depth Camera

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    International audienceThis paper presents a mirror-like augmented reality (AR) system to display the internal anatomy of a user. Using a single Microsoft V2.0 Kinect, we animate in real-time a user-specific internal anatomy according to the user’s motion and we superimpose it onto the user’s color map. The user can visualize his anatomy moving as if he was able to look inside his own body in real-time. A new calibration procedure to set up and attach a user-specific anatomy to the Kinect body tracking skeleton is introduced. At calibration time, the bone lengths are estimated using a set of poses. By using Kinect data as input, the practical limitation of skin correspondance in prior work is overcome. The generic 3D anatomical model is attached to the internal anatomy registration skeleton, and warped on the depth image using a novel elastic deformer, subject to a closest-point registration force and anatomical constraints. The noise in Kinect outputs precludes any realistic human display. Therefore, a novel filter to reconstruct plausible motions based onfixed length bones as well as realistic angular degrees of freedom (DOFs) and limits is introduced to enforce anatomical plausibility. Anatomical constraints applied to the Kinect body tracking skeleton joints are used to maximize the physical plausibility of the anatomy motion, while minimizing the distance to the raw data. At run-time,a simulation loop is used to attract the bones towards the raw data, and skinning shaders efficiently drag the resulting anatomy to the user’s tracked motion.Our user-specific internal anatomy model is validated by comparing the skeleton with segmented MRI images. A user study is established to evaluate the believability of the animated anatomy
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