Matching Items (382)
Description
Motion capture using cost-effective sensing technology is challenging and the huge success of Microsoft Kinect has been attracting researchers to uncover the potential of using this technology into computer vision applications. In this thesis, an upper-body motion analysis in a home-based system for stroke rehabilitation using novel RGB-D camera - Kinect is presented. We address this problem by first conducting a systematic analysis of the usability of Kinect for motion analysis in stroke rehabilitation. Then a hybrid upper body tracking approach is proposed which combines off-the-shelf skeleton tracking with a novel depth-fused mean shift tracking method. We proposed several kinematic features reliably extracted from the proposed inexpensive and portable motion capture system and classifiers that correlate torso movement to clinical measures of unimpaired and impaired. Experiment results show that the proposed sensing and analysis works reliably on measuring torso movement quality and is promising for end-point tracking. The system is currently being deployed for large-scale evaluations.
ContributorsDu, Tingfang (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Rikakis, Thanassis (Committee member) / Arizona State University (Publisher)
Created2012
Description
Our research focuses on finding answers through decentralized search, for complex, imprecise queries (such as "Which is the best hair salon nearby?") in situations where there is a spatiotemporal constraint (say answer needs to be found within 15 minutes) associated with the query. In general, human networks are good in answering imprecise queries. We try to use the social network of a person to answer his query. Our research aims at designing a framework that exploits the user's social network in order to maximize the answers for a given query. Exploiting an user's social network has several challenges. The major challenge is that the user's immediate social circle may not possess the answer for the given query, and hence the framework designed needs to carry out the query diffusion process across the network. The next challenge involves in finding the right set of seeds to pass the query to in the user's social circle. One other challenge is to incentivize people in the social network to respond to the query and thereby maximize the quality and quantity of replies. Our proposed framework is a mobile application where an individual can either respond to the query or forward it to his friends. We simulated the query diffusion process in three types of graphs: Small World, Random and Preferential Attachment. Given a type of network and a particular query, we carried out the query diffusion by selecting seeds based on attributes of the seed. The main attributes are Topic relevance, Replying or Forwarding probability and Time to Respond. We found that there is a considerable increase in the number of replies attained, even without saturating the user's network, if we adopt an optimal seed selection process. We found the output of the optimal algorithm to be satisfactory as the number of replies received at the interrogator's end was close to three times the number of neighbors an interrogator has. We addressed the challenge of incentivizing people to respond by associating a particular amount of points for each query asked, and awarding the same to people involved in answering the query. Thus, we aim to design a mobile application based on our proposed framework so that it helps in maximizing the replies for the interrogator's query by diffusing the query across his/her social network.
ContributorsSwaminathan, Neelakantan (Author) / Sundaram, Hari (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Video denoising has been an important task in many multimedia and computer vision applications. Recent developments in the matrix completion theory and emergence of new numerical methods which can efficiently solve the matrix completion problem have paved the way for exploration of new techniques for some classical image processing tasks. Recent literature shows that many computer vision and image processing problems can be solved by using the matrix completion theory. This thesis explores the application of matrix completion in video denoising. A state-of-the-art video denoising algorithm in which the denoising task is modeled as a matrix completion problem is chosen for detailed study. The contribution of this thesis lies in both providing extensive analysis to bridge the gap in existing literature on matrix completion frame work for video denoising and also in proposing some novel techniques to improve the performance of the chosen denoising algorithm. The chosen algorithm is implemented for thorough analysis. Experiments and discussions are presented to enable better understanding of the problem. Instability shown by the algorithm at some parameter values in a particular case of low levels of pure Gaussian noise is identified. Artifacts introduced in such cases are analyzed. A novel way of grouping structurally-relevant patches is proposed to improve the algorithm. Experiments show that this technique is useful, especially in videos containing high amounts of motion. Based on the observation that matrix completion is not suitable for denoising patches containing relatively low amount of image details, a framework is designed to separate patches corresponding to low structured regions from a noisy image. Experiments are conducted by not subjecting such patches to matrix completion, instead denoising such patches in a different way. The resulting improvement in performance suggests that denoising low structured patches does not require a complex method like matrix completion and in fact it is counter-productive to subject such patches to matrix completion. These results also indicate the inherent limitation of matrix completion to deal with cases in which noise dominates the structural properties of an image. A novel method for introducing priorities to the ranked patches in matrix completion is also presented. Results showed that this method yields improved performance in general. It is observed that the artifacts in presence of low levels of pure Gaussian noise appear differently after introducing priorities to the patches and the artifacts occur at a wider range of parameter values. Results and discussion suggesting future ways to explore this problem are also presented.
ContributorsMaguluri, Hima Bindu (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Claveau, Claude (Committee member) / Arizona State University (Publisher)
Created2013
Description
Current information on successful leadership and management practices is contradictory and inconsistent, which makes difficult to understand what successful business practices are and what are not. The purpose of this study is to identify a simple process that quickly and logically identifies consistent and inconsistent leadership and management criteria. The hypothesis proposed is that Information Measurement Theory (IMT) along with the Kashiwagi Solution Model (KSM) is a methodology than can differentiate between accurate and inaccurate principles the initial part of the study about authors in these areas show how information is conflictive, and also served to establish an initial baseline of recommended practices aligned with IMT. The one author that excels in comparison to the rest suits the "Initial Baseline Matrix from Deming" which composes the first model. The second model is denominated the "Full Extended KSM-Matrix" composed of all the LS characteristics found among all authors and IMT. Both models were tested-out for accuracy. The second part of the study was directed to evaluate the perception of individuals on these principles. Two different groups were evaluated, one group of people that had prior training and knowledge of IMT; another group of people without any knowledge of IMT. The results of the survey showed more confusion in the group of people without knowledge to IMT and improved consistency and less variation in the group of people with knowledge in IMT. The third part of the study, the analysis of case studies of success and failure, identified principles as contributors, and categorized them into LS/type "A" characteristics and RS/type "C" characteristics, by applying the KSM. The results validated the initial proposal and led to the conclusion that practices that fall into the LS side of the KSM will lead to success, while practices that fall into the RS of the KSM will lead to failure. The comparison and testing of both models indicated a dominant support of the IMT concepts as contributors to success; while the KSM model has a higher accuracy of prediction.
ContributorsReynolds, Harry (Author) / Kashiwagi, Dean (Thesis advisor) / Sullivan, Kenneth (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2011
Description
Photovoltaics (PV) is an important and rapidly growing area of research. With the advent of power system monitoring and communication technology collectively known as the "smart grid," an opportunity exists to apply signal processing techniques to monitoring and control of PV arrays. In this paper a monitoring system which provides real-time measurements of each PV module's voltage and current is considered. A fault detection algorithm formulated as a clustering problem and addressed using the robust minimum covariance determinant (MCD) estimator is described; its performance on simulated instances of arc and ground faults is evaluated. The algorithm is found to perform well on many types of faults commonly occurring in PV arrays. Among several types of detection algorithms considered, only the MCD shows high performance on both types of faults.
ContributorsBraun, Henry (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2012
Description
In this thesis, we consider the problem of fast and efficient indexing techniques for time sequences which evolve on manifold-valued spaces. Using manifolds is a convenient way to work with complex features that often do not live in Euclidean spaces. However, computing standard notions of geodesic distance, mean etc. can get very involved due to the underlying non-linearity associated with the space. As a result a complex task such as manifold sequence matching would require very large number of computations making it hard to use in practice. We believe that one can device smart approximation algorithms for several classes of such problems which take into account the geometry of the manifold and maintain the favorable properties of the exact approach. This problem has several applications in areas of human activity discovery and recognition, where several features and representations are naturally studied in a non-Euclidean setting. We propose a novel solution to the problem of indexing manifold-valued sequences by proposing an intrinsic approach to map sequences to a symbolic representation. This is shown to enable the deployment of fast and accurate algorithms for activity recognition, motif discovery, and anomaly detection. Toward this end, we present generalizations of key concepts of piece-wise aggregation and symbolic approximation for the case of non-Euclidean manifolds. Experiments show that one can replace expensive geodesic computations with much faster symbolic computations with little loss of accuracy in activity recognition and discovery applications. The proposed methods are ideally suited for real-time systems and resource constrained scenarios.
ContributorsAnirudh, Rushil (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Diabetic retinopathy (DR) is a common cause of blindness occurring due to prolonged presence of diabetes. The risk of developing DR or having the disease progress is increasing over time. Despite advances in diabetes care over the years, DR remains a vision-threatening complication and one of the leading causes of blindness among American adults. Recent studies have shown that diagnosis based on digital retinal imaging has potential benefits over traditional face-to-face evaluation. Yet there is a dearth of computer-based systems that can match the level of performance achieved by ophthalmologists. This thesis takes a fresh perspective in developing a computer-based system aimed at improving diagnosis of DR images. These images are categorized into three classes according to their severity level. The proposed approach explores effective methods to classify new images and retrieve clinically-relevant images from a database with prior diagnosis information associated with them. Retrieval provides a novel way to utilize the vast knowledge in the archives of previously-diagnosed DR images and thereby improve a clinician's performance while classification can safely reduce the burden on DR screening programs and possibly achieve higher detection accuracy than human experts. To solve the three-class retrieval and classification problem, the approach uses a multi-class multiple-instance medical image retrieval framework that makes use of spectrally tuned color correlogram and steerable Gaussian filter response features. The results show better retrieval and classification performances than prior-art methods and are also observed to be of clinical and visual relevance.
ContributorsChandakkar, Parag Shridhar (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2012
Description
Recent advances in camera architectures and associated mathematical representations now enable compressive acquisition of images and videos at low data-rates. While most computer vision applications of today are composed of conventional cameras, which collect a large amount redundant data and power hungry embedded systems, which compress the collected data for further processing, compressive cameras offer the advantage of direct acquisition of data in compressed domain and hence readily promise to find applicability in computer vision, particularly in environments hampered by limited communication bandwidths. However, despite the significant progress in theory and methods of compressive sensing, little headway has been made in developing systems for such applications by exploiting the merits of compressive sensing. In such a setting, we consider the problem of activity recognition, which is an important inference problem in many security and surveillance applications. Since all successful activity recognition systems involve detection of human, followed by recognition, a potential fully functioning system motivated by compressive camera would involve the tracking of human, which requires the reconstruction of atleast the initial few frames to detect the human. Once the human is tracked, the recognition part of the system requires only the features to be extracted from the tracked sequences, which can be the reconstructed images or the compressed measurements of such sequences. However, it is desirable in resource constrained environments that these features be extracted from the compressive measurements without reconstruction. Motivated by this, in this thesis, we propose a framework for understanding activities as a non-linear dynamical system, and propose a robust, generalizable feature that can be extracted directly from the compressed measurements without reconstructing the original video frames. The proposed feature is termed recurrence texture and is motivated from recurrence analysis of non-linear dynamical systems. We show that it is possible to obtain discriminative features directly from the compressed stream and show its utility in recognition of activities at very low data rates.
ContributorsKulkarni, Kuldeep Sharad (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2012
Description
The Art of Extraction: ABSTRACT
Anthropocentric society faces a multiplicity of environmental challenges, catalyzed and perpetuated by urban-industrial culture. Many of today’s perspectives and sustainable strategies cannot accommodate the challenges’ inherent complexity. Because urban-industrial society is only projected to grow, both in enormity and influence, the only viable option is to elucidate the complexity and employ it.
A potential setting in which to frame this exploration is the intersection of urbanism, landscape, and ecology –an overlap first introduced by the theories of Landscape Urbanism and Ecological Urbanism. Here, urbanization is not just discussed as an isolated phenomenon but one that is embedded within and responding to a variety of systems and scales. The methodologies of Landscape Urbanism and Ecological Urbanism also acknowledge artists and the visual arts as invaluable tools for realizing, communicating, and inspiring the new perspectives and modes of intervention needed to address the aforementioned urban complexity. Such artists who operate within this realm include Sissel Tolaas, Maya Lin, Katrin Sigurdardottir, David Maisel, Olafur Eliason, Mierle Ukeles, Suzanne Lacy, Steve Rowell, Mel Chin, and the Center for Land Use Interpretation. Case study analyses reveal many of these artists begin their investigations with provocative, searching questions situated within the realms of urbanism, landscape, and ecology. This is proceeded by relative scientific research and/or community involvement or outreach. Furthermore, the artists work within and extrapolate from a variety of other disciplines —increasing the scope and applicability of their work. The information they collect via this multidisciplinary approach is then metaphorically translated to the visual arts, where the public can not only physically or sensorially experience it, but understand and deduce its meaning and significance: public awareness being one of the more essential aspects of a sustainable society and at the root of our current struggle.
As a designer and architect, I will engage the artist’s mindset to explore the current and complex issue of resource extraction within Superior, Arizona: a topic at the core of urbanism, landscape, and ecology. While the town is not considered "urban" by standard definition, it and its surrounding landscapes are indirectly sculpted by the needs of urban society —rendering it the setting for this application. Within a group, we will begin with a searching question. We will conduct relative scientific research, engage the community of Superior, and call upon a variety of other disciplines to aid and inform our work. Through metaphor, the research and resulting discoveries will be artistically represented and composed within a designed exhibition of hopeful “things” (See Bruno Latour, “From Realpolitik to Dingpolitik”). This exhibition will theoretically take place on Superior’s currently dilapidated Main Street, amid a more accessible sphere. The eventual goal of the project is to illuminate and understand the complexities of resource extraction, specifically within Superior, while also enabling public awareness and empowerment through lucidity and comprehension.
Anthropocentric society faces a multiplicity of environmental challenges, catalyzed and perpetuated by urban-industrial culture. Many of today’s perspectives and sustainable strategies cannot accommodate the challenges’ inherent complexity. Because urban-industrial society is only projected to grow, both in enormity and influence, the only viable option is to elucidate the complexity and employ it.
A potential setting in which to frame this exploration is the intersection of urbanism, landscape, and ecology –an overlap first introduced by the theories of Landscape Urbanism and Ecological Urbanism. Here, urbanization is not just discussed as an isolated phenomenon but one that is embedded within and responding to a variety of systems and scales. The methodologies of Landscape Urbanism and Ecological Urbanism also acknowledge artists and the visual arts as invaluable tools for realizing, communicating, and inspiring the new perspectives and modes of intervention needed to address the aforementioned urban complexity. Such artists who operate within this realm include Sissel Tolaas, Maya Lin, Katrin Sigurdardottir, David Maisel, Olafur Eliason, Mierle Ukeles, Suzanne Lacy, Steve Rowell, Mel Chin, and the Center for Land Use Interpretation. Case study analyses reveal many of these artists begin their investigations with provocative, searching questions situated within the realms of urbanism, landscape, and ecology. This is proceeded by relative scientific research and/or community involvement or outreach. Furthermore, the artists work within and extrapolate from a variety of other disciplines —increasing the scope and applicability of their work. The information they collect via this multidisciplinary approach is then metaphorically translated to the visual arts, where the public can not only physically or sensorially experience it, but understand and deduce its meaning and significance: public awareness being one of the more essential aspects of a sustainable society and at the root of our current struggle.
As a designer and architect, I will engage the artist’s mindset to explore the current and complex issue of resource extraction within Superior, Arizona: a topic at the core of urbanism, landscape, and ecology. While the town is not considered "urban" by standard definition, it and its surrounding landscapes are indirectly sculpted by the needs of urban society —rendering it the setting for this application. Within a group, we will begin with a searching question. We will conduct relative scientific research, engage the community of Superior, and call upon a variety of other disciplines to aid and inform our work. Through metaphor, the research and resulting discoveries will be artistically represented and composed within a designed exhibition of hopeful “things” (See Bruno Latour, “From Realpolitik to Dingpolitik”). This exhibition will theoretically take place on Superior’s currently dilapidated Main Street, amid a more accessible sphere. The eventual goal of the project is to illuminate and understand the complexities of resource extraction, specifically within Superior, while also enabling public awareness and empowerment through lucidity and comprehension.
ContributorsTwilling, Emilie Marie (Author) / Stayner, Christian (Thesis director) / Harris, Catherine (Committee member) / Barrett, The Honors College (Contributor) / School of Sustainability (Contributor) / The Design School (Contributor)
Created2015-05
Description
Censorship is used as a structure to limit the ability of minority social groups to share opinions and ideologies. This in turn constricts our view of reality. My creative project educates the public about the different forms censorship takes. It also provides a space for people to speak uncensored in an effort to protect their right to be heard.
ContributorsEngler, Jessie Christine (Author) / Fehler, Michelle (Thesis director) / Heywood, William (Committee member) / Barrett, The Honors College (Contributor) / The Design School (Contributor)
Created2015-05