Sökning: "Riemannian manifold"

Visar resultat 1 - 5 av 15 avhandlingar innehållade orden Riemannian manifold.

1. 1. Riemannian Manifold-Based Modeling and Classification Methods for Video Activities with Applications to Assisted Living and Smart Home

   Författare :Yixiao Yun; Chalmers tekniska högskola; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; assisted living; Riemannian manifold; fall detection; smart homes; activities of daily living ADL ; video tracking; activity classification;

   Sammanfattning : This thesis mainly focuses on visual-information based daily activity classification, anomaly detection, and video tracking through using visual sensors. The main reasons for adopting visual-information based methods are due to: (i) vision plays a major role in recognition/classification of activities which is a fundamental issue in a human-centric system; (ii) visual sensor-based analysis may possibly offer high performance with minimum disturbance to individuals' daily lives. LÄS MER

3. 2. Stochastic Modeling for Video Object Tracking and Online Learning: manifolds and particle filters

   Författare :Zulfiqar Hasan Khan; Chalmers tekniska högskola; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; anisotropic mean shift; online learning of reference object; covariance tracking; consensus point feature correspondences; Bayesian tracking; Gabor features; Visual object tracking; Grassmann manifold.; Riemannian manifold; particle filters;

   Sammanfattning : Classical visual object tracking techniques provide effective methods when parameters of the underlying process lie in a vector space. However, various parameter spaces commonly occurring in visual tracking violate this assumption. LÄS MER

4. 3. Manifold learning and representations for image analysis and visualization

   Författare :Anders Brun; Hans Knutsson; Magnus Herberthson; Carl-Fredrik Westin; Reiner Lenz; Linköpings universitet; []
   Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; manifold learning; image analysis; signal processing; diffusion tensor mri; Medical informatics; Medicinsk informatik;

   Sammanfattning : We present a novel method for manifold learning, i.e. identification of the low-dimensional manifold-like structure present in a set of data points in a possibly high-dimensional space. The main idea is derived from the concept of Riemannian normal coordinates. LÄS MER

5. 4. Partial Balayage and Related Concepts in Potential Theory

   Författare :Joakim Roos; Henrik Shahgholian; Björn Gustafsson; Charles Smart; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Partial balayage; equilibrium measure; obstacle problem; divisible sandpile; Riemannian manifold; quadrature domain; Laplacian growth; mother body; Mathematics; Matematik;

   Sammanfattning : This thesis consists of three papers, all treating various aspects of the operation partial balayage from potential theory.The first paper concerns the equilibrium measure in the setting of two dimensional weighted potential theory, an important measure arising in various mathematical areas, e.g. LÄS MER

7. 5. Visual Object Tracking and Classification Using Multiple Sensor Measurements

   Författare :Yixiao Yun; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; sensor fusion; Riemannian manifold; visual pattern classification; multiple view geometry; Visual object tracking; boosting; multiple sensor measurement;

   Sammanfattning : Multiple sensor measurement has gained in popularity for computer vision tasks such as visual object tracking and visual pattern classification. The main idea is that multiple sensors may provide rich and redundant information, due to wide spatial or frequency coverage of the scene, which is advantageous over single sensor measurement in learning object model/feature and inferring target state/attribute in complex scenarios. LÄS MER