Real-Time Non-Rigid Surface Detection

On-line diagnostics of power system components is an important area since it allows the diagnostics to be performed at regular intervals during the normal operation of the components. This combined with reliability centered maintenance could lead to reduced customer outages. In this thesis the on-line diagnostic methods for medium voltage cross-linked polyethylene (XLPE) cables are investigated based on Time Domain Reflectometry (TDR). Degradation of XLPE insulated power cables by water-trees (WT) is a primary cause of failure of these cables. The detection of WT and information about the severity of the degradation can be obtained with off-line measurements using dielectric spectroscopy.  In many situations only a limited part of the cable may be degraded by the WT. In such a situation a method for localization of this WT section would be desirable. The developed high frequency measurements superimposed on HV system is presented. It was used to measure the propagation constant of the WT aged cables as a function of the applied HV. This was done in order to study the diagnostic criteria, which could be used for on-line TDR diagnostics of WT aged cables. A physically based dielectric model of WT was developed in order to explain qualitatively and quantitatively the permittivity and loss of WT at different frequencies and voltages. The sensors applicable for the on-line TDR were investigated in terms of sensitivity and bandwidth. High frequency models were built and the simulation results in frequency and time domains were verified by measurements. The developed on-line TDR systems are presented. Their applicability to detect water penetration under the cable sheath and localize the broken screen wires was investigated during the measurements in laboratory environment. The results of field measurements with on-line TDR are presented. Variations due to load cycling of the cable were observed, where an increase in the cable temperature cause an increase of the pulse propagation velocity in the cable. The temperature dependent wave propagation in the cable is investigated and explained by modeling.QC 20100709</p

Real-Time Non-Rigid Surface Detection

We present a real-time method for detecting deformable surfaces, with no need whatsoever for a priori pose knowledge. Our method starts from a set of wide baseline point matches between an undeformed image of the object and the image in which it is to be detected. The matches are used not only to detect but also to compute a precise mapping from one to the other. The algorithm is robust to large deformations, lighting changes, motion blur, and occlusions. It runs at 10 frames per second on a 2.8 GHz PC and we are not aware of any other published technique that produces similar results. Combining deformable meshes with a well designed robust estimator is key to dealing with the large number of parameters involved in modeling deformable surfaces and rejecting erroneous matches for error rates of up to 95%, which is considerably more than what is required in practice

Fast Non-Rigid Surface Detection, Registration and Realistic Augmentation

We present a real-time method for detecting deformable surfaces, with no need whatsoever for a priori pose knowledge. Our method starts from a set of wide baseline point matches between an undeformed image of the object and the image in which it is to be detected. The matches are used not only to detect but also to compute a precise mapping from one to the other. The algorithm is robust to large deformations, lighting changes, motion blur, and occlusions. It runs at 10 frames per second on a 2.8 GHz PC.We demonstrate its applicability by using it to realistically modify the texture of a deforming surface and to handle complex illumination effects. Combining deformable meshes with a well designed robust estimator is key to dealing with the large number of parameters involved in modeling deformable surfaces and rejecting erroneous matches for error rates of more than 90%, which is considerably more than what is required in practice

Face recognition with variation in pose angle using face graphs

Automatic recognition of human faces is an important and growing field. Several real-world applications have started to rely on the accuracy of computer-based face recognition systems for their own performance in terms of efficiency, safety and reliability. Many algorithms have already been established in terms of frontal face recognition, where the person to be recognized is looking directly at the camera. More recently, methods for non-frontal face recognition have been proposed. These include work related to 3D rigid face models, component-based 3D morphable models, eigenfaces and elastic bunched graph matching (EBGM). This thesis extends recognition algorithm based on EBGM to establish better face recognition across pose variation. Facial features are localized using active shape models and face recognition is based on elastic bunch graph matching. Recognition is performed by comparing feature descriptors based on Gabor wavelets for various orientations and scales, called jets. Two novel recognition schemes, feature weighting and jet-mapping, are proposed for improved performance of the base scheme, and a combination of the two schemes is considered as a further enhancement. The improvements in performance have been evaluated by studying recognition rates on an existing database and comparing the results with the base recognition scheme over which the schemes have been developed. Improvement of up to 20% has been observed for face pose variation as large as 45°

Longitudinal video Investigation of dyadic bodily dynamics around the time of word acquisition

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 105-110).Human movement encodes information about internal states and goals. When these goals involve dyadic interactions, such as in language acquisition, the nature of the movement and proximity become representative, allowing parts of our internal states to manifest. We propose an approach called Visually Grounded Virtual Accelerometers (VGVA), to aid with ecologically-valid video analysis investigations, involving humans during dyadic interactions. Utilizing the Human Speechome (HSP) [1] video corpus database, we examine a dyadic interaction paradigm taken from the caregiver-child ecology, during language acquisition. We proceed to characterize human interaction in a video cross-modally; by visually detecting and assessing the child's bodily dynamics in a video, grounded on the caregiver's bodily dynamics of the same video and the related HSP speech transcriptions [2]. Potential applications include analyzing a child's language acquisition, establishing longitudinal diagnostic means for child developmental disorders and generally establishing a metric of effective human communication on dyadic interactions under a video surveillance system. In this thesis, we examine word-learning transcribed video episodes before and after the age of the word's acquisition (AOA). As auditory stimulus is uttered from the caregiver, points along the VGVA tracked sequences corresponding to the onset and post-onset of the child-caregiver bodily responses, are used to longitudinally mark and characterize episodes of word learning. We report a systematic shift in terms of caregiver-child synchrony in motion and turning behavior, tied to exposures of the target word around the time the child begins to understand and thus respond to instances of the spoken word. The systematic shift, diminishes gradually after the age of word acquisition (AOA).by Kleovoulos (Leo) Tsourides.S.M

Deformable model-based shape and motion analysis from images using motion residual error

We present a novel method for the shape and motion estimation of a deformable model using error residuals from model-based motion analysis. The motion of the model is first estimated using a model-based least squares method. Using the residuals from the least squares solution, the non-rigid structure of the model can be better estimated by computing how changes in the shape of the model affect its motion parameterization. This method is implemented as a component in a deformable model-based framework that uses optical flow information and edges. This general model-based framework is applied to human face shape and motion estimation. We present experiments that demonstrate that this framework is a considerable improvement over a framework that uses only optical flow information and edges.

Quantitative Estimation of Movement Progress during Rehabilitation after Knee/Hip Replacement Surgery

Mobility improvement for patients is one of the primary concerns of physiotherapy rehabilitation. In a typical physiotherapy session, the patient is instructed to perform multiple exercises, based on a specific regimen recommended by the physiotherapist for each patient. The physiotherapist then evaluates the patient's progress based on his or her performance during the exercises. Providing the physiotherapist and the patient with a quantified and objective measure of progress, based on both individual exercises and the exercise set, can be beneficial for monitoring the patient's performance. The quantified measure can also be beneficial when the physiotherapist is not available, e.g., crowded gym or rehabilitation at home. In this thesis, two approaches are introduced for quantifying patient performance. One approach describes the movement timeseries by statistical measures and the other by a stochastic model. Both approaches formulate a distance between patient data and the healthy population as the measure of performance. Distance measures are defined to capture the performance of one repetition of an exercise or multiple repetitions of the same exercise. To capture patient progress across multiple exercises, a quality measure and overall score are formulated based on the distance measures and are used to quantify the overall performance for each session. The proposed approaches are compared to several existing approaches, including sample distribution approaches (two sample kernel), classifier-based approaches (Naive Bayes, Support Vector Machines, and Kullback-Leibler Divergence), and dynamical movement primitives. In their original formulation, existing approaches are not capable of estimating measures of performance for multiple exercises. Therefore, the measures of performance for multiple repetitions of the same exercise are estimated using the existing approaches, while the formulation proposed in this thesis is used to estimate the overall performance for multiple exercises in one session. The effects of different variabilities in human motion on the performance of the proposed approaches and the comparison approaches are investigated with both synthetic and patient data. The patient data consists of rehabilitation data recorded from patients recovering from knee or hip replacement surgery, the associated exercise regimen and physiotherapist evaluations of progress. The methods are evaluated quantitatively based on correlation between methods, correlation with exercise regimen difficulty, and qualitatively based on the patients' medical charts. The proposed approaches are capable of capturing the trend of progress for the synthetic dataset and are superior to the existing approaches in presence of multiple sources of variability. For patient data, the proposed approaches correlate moderately with the score obtained from the exercise regimen, and qualitatively correspond with the patients' medical charts. The results indicate that the quantified measures of progress obtained from the proposed approaches are promising tools for supporting physiotherapy practice through monitoring patient progress.1 yea

Augmented reality for non-rigid surfaces

Augmented Reality (AR) is the process of integrating virtual elements in reality, often by mixing computer graphics into a live video stream of a real scene. It requires registration of the target object with respect to the cameras. To this end, some approaches rely on dedicated hardware, such as magnetic trackers or infra-red cameras, but they are too expensive and cumbersome to reach a large public. Others are based on specifically designed markers which usually look like bar-codes. However, they alter the look of objects to be augmented, thereby hindering their use in application for which visual design matters. Recent advances in Computer Vision have made it possible to track and detect objects by relying on natural features. However, no such method is commonly used in the AR community, because the maturity of available packages is not sufficient yet. As far as deformable surfaces are concerned, the choice is even more limited, mainly because initialization is so difficult. Our main contribution is therefore a new AR framework that can properly augment deforming surfaces in real-time. Its target platform is a standard PC and a single webcam. It does not require any complex calibration procedure, making it perfectly suitable for novice end-users. To satisfy to the most demanding application designers, our framework does not require any scene engineering, renders virtual objects illuminated by real light, and let real elements occlude virtual ones. To meet this challenge, we developed several innovative techniques. Our approach to real-time registration of a deforming surface is based on wide-baseline feature matching. However, traditional outlier elimination techniques such as RANSAC are unable to handle the non-rigid surface's large number of degrees of freedom. We therefore proposed a new robust estimation scheme that allows both 2–D and 3–D non-rigid surface registration. Another issue of critical importance in AR to achieve realism is illumination handling, for which existing techniques often require setup procedures or devices such as reflective spheres. By contrast, our framework includes methods to estimate illumination for rendering purposes without sacrificing ease of use. Finally, several existing approaches to handling occlusions in AR rely on multiple cameras or can only deal with occluding objects modeled beforehand. Our requires only one camera and models occluding objects at runtime. We incorporated these components in a consistent and flexible framework. We used it to augment many different objects such as a deforming T-shirt or a sheet of paper, under challenging conditions, in real-time, and with correct handling of illumination and occlusions. We also used our non-rigid surface registration technique to measure the shape of deformed sails. We validated the ease of deployment of our framework by distributing a software package and letting an artist use it to create two AR applications